===============================================================================

About this build: this rebuild has been done as part of reproduce.debian.net
where we aim to reproduce Debian binary packages distributed via ftp.debian.org,
by rebuilding using the exact same packages as the original build on the buildds,
as described in the relevant .buildinfo file from buildinfos.debian.net.

For more information please go to https://reproduce.debian.net or join
#debian-reproducible on irc.debian.org

===============================================================================

Preparing download of sources for /srv/rebuilderd/tmp/rebuilderdQSPWid/inputs/octave-statistics_1.8.1-3_riscv64.buildinfo
Source: octave-statistics
Version: 1.8.1-3
rebuilderd-worker node: riscv64-33

+------------------------------------------------------------------------------+
| Downloading sources                          Thu, 26 Feb 2026 23:11:49 +0000 |
+------------------------------------------------------------------------------+

Get:1 https://deb.debian.org/debian trixie InRelease [140 kB]
Get:2 https://deb.debian.org/debian-security trixie-security InRelease [43.4 kB]
Get:3 https://deb.debian.org/debian trixie-updates InRelease [47.3 kB]
Get:4 https://deb.debian.org/debian trixie-proposed-updates InRelease [57.6 kB]
Get:5 https://deb.debian.org/debian trixie-backports InRelease [54.0 kB]
Get:6 https://deb.debian.org/debian forky InRelease [137 kB]
Get:7 https://deb.debian.org/debian sid InRelease [187 kB]
Get:8 https://deb.debian.org/debian experimental InRelease [91.7 kB]
Get:9 https://deb.debian.org/debian trixie/non-free-firmware Sources [6,552 B]
Get:10 https://deb.debian.org/debian trixie/main Sources [10.5 MB]
Get:11 https://deb.debian.org/debian-security trixie-security/main Sources [136 kB]
Get:12 https://deb.debian.org/debian-security trixie-security/non-free-firmware Sources [696 B]
Get:13 https://deb.debian.org/debian trixie-updates/main Sources [2,788 B]
Get:14 https://deb.debian.org/debian trixie-proposed-updates/main Sources [70.5 kB]
Get:15 https://deb.debian.org/debian trixie-backports/non-free-firmware Sources [2,620 B]
Get:16 https://deb.debian.org/debian trixie-backports/main Sources [148 kB]
Get:17 https://deb.debian.org/debian forky/non-free-firmware Sources [8,304 B]
Get:18 https://deb.debian.org/debian forky/main Sources [10.6 MB]
Get:19 https://deb.debian.org/debian sid/main Sources [11.3 MB]
Get:20 https://deb.debian.org/debian sid/non-free-firmware Sources [10.3 kB]
Get:21 https://deb.debian.org/debian experimental/non-free-firmware Sources [2,572 B]
Get:22 https://deb.debian.org/debian experimental/main Sources [426 kB]
Fetched 34.1 MB in 37s (917 kB/s)
Reading package lists...
'https://deb.debian.org/debian/pool/main/o/octave-statistics/octave-statistics_1.8.1-3.dsc' octave-statistics_1.8.1-3.dsc 2466 SHA256:e3a391972f4fc284071991f4d01d1210ee3a4506a21dc44e01ef2cb175b05bb9
'https://deb.debian.org/debian/pool/main/o/octave-statistics/octave-statistics_1.8.1.orig.tar.gz' octave-statistics_1.8.1.orig.tar.gz 1465996 SHA256:5174f80ddf182674ca06d871d9fbed439f3eaed3e3970892a0716ed480f5545d
'https://deb.debian.org/debian/pool/main/o/octave-statistics/octave-statistics_1.8.1-3.debian.tar.xz' octave-statistics_1.8.1-3.debian.tar.xz 10752 SHA256:ebde791e6ebde725ec4e565ebf18b2c20878a521e7c084a553d7b8c9d2350ae5
5174f80ddf182674ca06d871d9fbed439f3eaed3e3970892a0716ed480f5545d  octave-statistics_1.8.1.orig.tar.gz
ebde791e6ebde725ec4e565ebf18b2c20878a521e7c084a553d7b8c9d2350ae5  octave-statistics_1.8.1-3.debian.tar.xz
e3a391972f4fc284071991f4d01d1210ee3a4506a21dc44e01ef2cb175b05bb9  octave-statistics_1.8.1-3.dsc

+------------------------------------------------------------------------------+
| Calling debrebuild                           Thu, 26 Feb 2026 23:12:28 +0000 |
+------------------------------------------------------------------------------+

Rebuilding octave-statistics=1.8.1-3 in /srv/rebuilderd/tmp/rebuilderdQSPWid/inputs now.
+ nice /usr/bin/debrebuild --buildresult=/srv/rebuilderd/tmp/rebuilderdQSPWid/out --builder=sbuild+unshare --cache=/srv/rebuilderd/cache -- /srv/rebuilderd/tmp/rebuilderdQSPWid/inputs/octave-statistics_1.8.1-3_riscv64.buildinfo
/srv/rebuilderd/tmp/rebuilderdQSPWid/inputs/octave-statistics_1.8.1-3_riscv64.buildinfo contains a GPG signature which has NOT been validated
Using defined Build-Path: /build/reproducible-path/octave-statistics-1.8.1
I: verifying dsc... successful!
Get:1 http://deb.debian.org/debian unstable InRelease [187 kB]
Get:2 http://snapshot.debian.org/archive/debian/20260223T142455Z sid InRelease [187 kB]
Get:3 http://snapshot.debian.org/archive/debian/20260225T215433Z sid InRelease [187 kB]
Get:4 http://deb.debian.org/debian unstable/main riscv64 Packages [9876 kB]
Get:5 http://snapshot.debian.org/archive/debian/20260223T142455Z sid/main riscv64 Packages [9824 kB]
Get:6 http://snapshot.debian.org/archive/debian/20260225T215433Z sid/main riscv64 Packages [9829 kB]
Fetched 30.1 MB in 8s (3565 kB/s)
Reading package lists...
W: http://snapshot.debian.org/archive/debian/20260223T142455Z/dists/sid/InRelease: Loading /etc/apt/trusted.gpg from deprecated option Dir::Etc::Trusted
W: http://snapshot.debian.org/archive/debian/20260225T215433Z/dists/sid/InRelease: Loading /etc/apt/trusted.gpg from deprecated option Dir::Etc::Trusted
Get:1 http://deb.debian.org/debian unstable/main riscv64 libregexp-common-perl all 2024080801-1 [167 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 gcc-riscv64-linux-gnu riscv64 4:15.2.0-5 [1432 B]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libpackage-stash-perl all 0.40-1 [22.0 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libxml-sax-base-perl all 1.09-3 [20.6 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpwuc3gbw6/libio-string-perl_1.08-4_all.deb'
Get:1 http://snapshot.debian.org/archive/debian/20260223T142455Z sid/main riscv64 autoconf all 2.72-3.1 [494 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libgdbm6t64 riscv64 1.26-1+b1 [79.2 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libtime-duration-perl all 1.21-2 [13.1 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp3rvisdyd/libtime-duration-perl_1.21-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libc6-dev riscv64 2.42-13 [3438 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libnet-ssleay-perl riscv64 1.94-3+b1 [339 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp2e3dexqy/libnet-ssleay-perl_1.94-3+b1_riscv64.deb'
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libsensors5 riscv64 1:3.6.2-2+b1 [37.5 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 bzip2 riscv64 1.0.8-6+b1 [40.8 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libtext-charwidth-perl riscv64 0.04-11+b5 [9416 B]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp1tnn7ms4/libtext-charwidth-perl_0.04-11+b5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcb-glx0 riscv64 1.17.0-2+b2 [123 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libbsd0 riscv64 0.12.2-2+b1 [132 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpkep7_7w8/libbsd0_0.12.2-2+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfile-find-rule-perl all 0.35-1 [25.9 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libgudev-1.0-0 riscv64 238-7+b1 [14.7 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libtime-moment-perl riscv64 0.46-1 [80.0 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp5zlwm2pt/libtime-moment-perl_0.46-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libwww-mechanize-perl all 2.20-1 [117 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libpixman-1-0 riscv64 0.46.4-1+b1 [179 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libfftw3-double3 riscv64 3.3.10-2+b2 [376 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp07nkks1g/libfftw3-double3_3.3.10-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxpm4 riscv64 1:3.5.17-1+b4 [55.9 kB]
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dpkg-name: info: moved 'libxpm4_1%3a3.5.17-1+b4_riscv64.deb' to '/srv/rebuilderd/tmp/tmpib7lu8fx/libxpm4_3.5.17-1+b4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpkgconf7 riscv64 2.5.1-4 [47.1 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpfkfeogjs/libpkgconf7_2.5.1-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libbrotli-dev riscv64 1.2.0-3 [949 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp2nkp4usp/libbrotli-dev_1.2.0-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 gnuplot-data all 6.0.3+dfsg1-1 [73.0 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp4z49r1w3/gnuplot-data_6.0.3+dfsg1-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 x11-common all 1:7.7+26 [217 kB]
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dpkg-name: info: moved 'x11-common_1%3a7.7+26_all.deb' to '/srv/rebuilderd/tmp/tmpgipqhbj5/x11-common_7.7+26_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libjpeg62-turbo riscv64 1:2.1.5-4 [155 kB]
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dpkg-name: info: moved 'libjpeg62-turbo_1%3a2.1.5-4_riscv64.deb' to '/srv/rebuilderd/tmp/tmpec49_2mr/libjpeg62-turbo_2.1.5-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsvtav1enc2 riscv64 2.3.0+dfsg-1 [1119 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpjjat5sxn/libsvtav1enc2_2.3.0+dfsg-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liblist-moreutils-xs-perl riscv64 0.430-4+b2 [42.8 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp8zv9mdm0/liblist-moreutils-xs-perl_0.430-4+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 t1utils riscv64 1.41-4+b1 [60.0 kB]
Fetched 60.0 kB in 0s (1119 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpmye2pgc0/t1utils_1.41-4+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxrender1 riscv64 1:0.9.12-1+b1 [28.3 kB]
Fetched 28.3 kB in 0s (1408 kB/s)
dpkg-name: info: moved 'libxrender1_1%3a0.9.12-1+b1_riscv64.deb' to '/srv/rebuilderd/tmp/tmprvfnrnhs/libxrender1_0.9.12-1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 xorg-sgml-doctools all 1:1.11-1.1 [22.1 kB]
Fetched 22.1 kB in 0s (1094 kB/s)
dpkg-name: info: moved 'xorg-sgml-doctools_1%3a1.11-1.1_all.deb' to '/srv/rebuilderd/tmp/tmptb28kmqa/xorg-sgml-doctools_1.11-1.1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libnetaddr-ip-perl riscv64 4.079+dfsg-2+b4 [98.3 kB]
Fetched 98.3 kB in 0s (1803 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp1h0_5e9g/libnetaddr-ip-perl_4.079+dfsg-2+b4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 fonts-freefont-otf all 20211204+svn4273-4 [4322 kB]
Fetched 4322 kB in 0s (28.6 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0uy5mriy/fonts-freefont-otf_20211204+svn4273-4_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libapt-pkg7.0 riscv64 3.1.16 [1173 kB]
Fetched 1173 kB in 0s (13.9 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp_0durtvo/libapt-pkg7.0_3.1.16_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libcc1-0 riscv64 15.2.0-13 [40.3 kB]
Fetched 40.3 kB in 0s (1958 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpv4bv8j7b/libcc1-0_15.2.0-13_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfontconfig1 riscv64 2.17.1-5 [137 kB]
Fetched 137 kB in 0s (6037 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpmp2b4gsf/libfontconfig1_2.17.1-5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgl1-mesa-dri riscv64 26.0.0-1 [47.0 kB]
Fetched 47.0 kB in 0s (2264 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpjmhlnmzb/libgl1-mesa-dri_26.0.0-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libjpeg-dev riscv64 1:2.1.5-4 [72.2 kB]
Fetched 72.2 kB in 0s (1330 kB/s)
dpkg-name: info: moved 'libjpeg-dev_1%3a2.1.5-4_riscv64.deb' to '/srv/rebuilderd/tmp/tmp8h06vhft/libjpeg-dev_2.1.5-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libaudit1 riscv64 1:4.1.2-1+b1 [58.7 kB]
Fetched 58.7 kB in 0s (2787 kB/s)
dpkg-name: info: moved 'libaudit1_1%3a4.1.2-1+b1_riscv64.deb' to '/srv/rebuilderd/tmp/tmphqi0fjqe/libaudit1_4.1.2-1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libqt6core5compat6 riscv64 6.9.2-3 [142 kB]
Fetched 142 kB in 0s (2557 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpws0jurt2/libqt6core5compat6_6.9.2-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libdb5.3t64 riscv64 5.3.28+dfsg2-11 [719 kB]
Fetched 719 kB in 0s (20.4 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpsu3gnle2/libdb5.3t64_5.3.28+dfsg2-11_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 pkgconf riscv64 2.5.1-4 [33.6 kB]
Fetched 33.6 kB in 0s (1612 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpcq6s2pe7/pkgconf_2.5.1-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtext-wrapi18n-perl all 0.06-10 [8808 B]
Fetched 8808 B in 0s (168 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpvxc6ujfa/libtext-wrapi18n-perl_0.06-10_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgssrpc4t64 riscv64 1.22.1-2 [60.8 kB]
Fetched 60.8 kB in 0s (1118 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpz8eozhnl/libgssrpc4t64_1.22.1-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmd0 riscv64 1.1.0-2+b2 [36.7 kB]
Fetched 36.7 kB in 0s (1784 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpc_16wekm/libmd0_1.1.0-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libwebpmux3 riscv64 1.5.0-0.1+b1 [126 kB]
Fetched 126 kB in 0s (2289 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpjzjhdav8/libwebpmux3_1.5.0-0.1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 dh-octave-autopkgtest all 1.14.1 [11.8 kB]
Fetched 11.8 kB in 0s (596 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpoup7lz5i/dh-octave-autopkgtest_1.14.1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 dpkg riscv64 1.23.5 [1528 kB]
Fetched 1528 kB in 0s (17.3 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpgeh5gtms/dpkg_1.23.5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libx11-dev riscv64 2:1.8.13-1 [1507 kB]
Fetched 1507 kB in 0s (17.1 MB/s)
dpkg-name: info: moved 'libx11-dev_2%3a1.8.13-1_riscv64.deb' to '/srv/rebuilderd/tmp/tmpr88r0itf/libx11-dev_1.8.13-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsort-versions-perl all 1.62-3 [8928 B]
Fetched 8928 B in 0s (167 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpljll1g72/libsort-versions-perl_1.62-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 dash riscv64 0.5.12-12 [101 kB]
Fetched 101 kB in 0s (4640 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp2yuolkay/dash_0.5.12-12_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmpg123-0t64 riscv64 1.33.3-2 [156 kB]
Fetched 156 kB in 0s (5162 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0lw03v7n/libmpg123-0t64_1.33.3-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libngtcp2-crypto-ossl-dev riscv64 1.16.0-1 [51.8 kB]
Fetched 51.8 kB in 0s (2519 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp86yc66ii/libngtcp2-crypto-ossl-dev_1.16.0-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libharfbuzz0b riscv64 12.3.2-2 [566 kB]
Fetched 566 kB in 0s (17.8 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpg37ji0wh/libharfbuzz0b_12.3.2-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcb-shape0 riscv64 1.17.0-2+b2 [105 kB]
Fetched 105 kB in 0s (4806 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpy0rsig06/libxcb-shape0_1.17.0-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libio-interactive-perl all 1.027-1 [11.8 kB]
Fetched 11.8 kB in 0s (133 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp59gfjbzz/libio-interactive-perl_1.027-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libjson-perl all 4.10000-1 [87.5 kB]
Fetched 87.5 kB in 0s (1613 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpwquayxd9/libjson-perl_4.10000-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmoox-aliases-perl all 0.001006-3 [6996 B]
Fetched 6996 B in 0s (134 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpzrnovbqj/libmoox-aliases-perl_0.001006-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libuchardet0 riscv64 0.0.8-2+b1 [68.9 kB]
Fetched 68.9 kB in 0s (3275 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmplskrne_z/libuchardet0_0.0.8-2+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 debianutils riscv64 5.23.2 [91.7 kB]
Fetched 91.7 kB in 0s (4251 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp6asryrcd/debianutils_5.23.2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libcups2t64 riscv64 2.4.16-1 [252 kB]
Fetched 252 kB in 0s (10.1 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpiaia0apg/libcups2t64_2.4.16-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libnpth0t64 riscv64 1.8-3+b1 [22.9 kB]
Fetched 22.9 kB in 0s (1132 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpi0p64lwy/libnpth0t64_1.8-3+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libalgorithm-c3-perl all 0.11-2 [10.8 kB]
Fetched 10.8 kB in 0s (208 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpodpx8zqd/libalgorithm-c3-perl_0.11-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfyaml0 riscv64 0.9.4-1 [287 kB]
Fetched 287 kB in 0s (4899 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp87d0f758/libfyaml0_0.9.4-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libiterator-perl all 0.03+ds1-2 [18.8 kB]
Fetched 18.8 kB in 0s (357 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0975ogtl/libiterator-perl_0.03+ds1-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libnettle8t64 riscv64 3.10.2-1 [332 kB]
Fetched 332 kB in 0s (12.3 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpt8_hdua6/libnettle8t64_3.10.2-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfltk-gl1.3t64 riscv64 1.3.11-3 [62.5 kB]
Fetched 62.5 kB in 0s (1162 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpd73wigw0/libfltk-gl1.3t64_1.3.11-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcb-dri3-0 riscv64 1.17.0-2+b2 [107 kB]
Fetched 107 kB in 0s (1933 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpnzzbw8cu/libxcb-dri3-0_1.17.0-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpath-tiny-perl all 0.148-1 [56.7 kB]
Fetched 56.7 kB in 0s (1062 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpiyj1til4/libpath-tiny-perl_0.148-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 licensecheck all 3.3.9-1 [50.1 kB]
Fetched 50.1 kB in 0s (938 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp42wcp2k7/licensecheck_3.3.9-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libdpkg-perl all 1.23.5 [668 kB]
Fetched 668 kB in 0s (19.7 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpz_it8y2f/libdpkg-perl_1.23.5_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libdeflate0 riscv64 1.23-2+b1 [38.4 kB]
Fetched 38.4 kB in 0s (723 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmphbgurkiy/libdeflate0_1.23-2+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 texinfo all 7.2-5 [1973 kB]
Fetched 1973 kB in 0s (19.1 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpcfl_5sl6/texinfo_7.2-5_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcb-cursor0 riscv64 0.1.6-1 [18.0 kB]
Fetched 18.0 kB in 0s (339 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp2sf0pyh7/libxcb-cursor0_0.1.6-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libhdf5-fortran-310 riscv64 1.14.6+repack-2 [110 kB]
Fetched 110 kB in 0s (4982 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpktj1eu2r/libhdf5-fortran-310_1.14.6+repack-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 lzop riscv64 1.04-2+b1 [83.9 kB]
Fetched 83.9 kB in 0s (1541 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp__m2s958/lzop_1.04-2+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsamplerate0 riscv64 0.2.2-4+b3 [948 kB]
Fetched 948 kB in 0s (23.5 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp8h8ehyet/libsamplerate0_0.2.2-4+b3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libzstd1 riscv64 1.5.7+dfsg-3+b1 [372 kB]
Fetched 372 kB in 0s (13.3 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp3xkzw8n2/libzstd1_1.5.7+dfsg-3+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 groff-base riscv64 1.23.0-10 [1163 kB]
Fetched 1163 kB in 0s (26.2 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpnc9u4f5e/groff-base_1.23.0-10_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libasound2t64 riscv64 1.2.15.3-1 [390 kB]
Fetched 390 kB in 0s (13.8 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpdp2a_fda/libasound2t64_1.2.15.3-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libdata-dpath-perl all 0.60-1 [41.8 kB]
Fetched 41.8 kB in 0s (787 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpd72ut1mb/libdata-dpath-perl_0.60-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtext-reform-perl all 1.20-5 [36.0 kB]
Fetched 36.0 kB in 0s (678 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp5l8b5rcc/libtext-reform-perl_1.20-5_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libconfig-inifiles-perl all 3.000003-4 [44.9 kB]
Fetched 44.9 kB in 0s (844 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpckuba41w/libconfig-inifiles-perl_3.000003-4_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libx11-xcb1 riscv64 2:1.8.13-1 [250 kB]
Fetched 250 kB in 0s (9981 kB/s)
dpkg-name: info: moved 'libx11-xcb1_2%3a1.8.13-1_riscv64.deb' to '/srv/rebuilderd/tmp/tmpdvxn8qkc/libx11-xcb1_1.8.13-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtext-levenshteinxs-perl riscv64 0.03-5+b3 [8528 B]
Fetched 8528 B in 0s (163 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpx4rpcq4q/libtext-levenshteinxs-perl_0.03-5+b3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liblist-compare-perl all 0.55-2 [65.7 kB]
Fetched 65.7 kB in 0s (1204 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpeqjx1l5c/liblist-compare-perl_0.55-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libqt6core6t64 riscv64 6.9.2+dfsg-4 [1902 kB]
Fetched 1902 kB in 0s (19.6 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp4q2zio07/libqt6core6t64_6.9.2+dfsg-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtiff6 riscv64 4.7.1-1 [354 kB]
Fetched 354 kB in 0s (5904 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0drmyz7t/libtiff6_4.7.1-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libwacom-common all 2.18.0-1 [117 kB]
Fetched 117 kB in 0s (5232 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp_gsrs0zf/libwacom-common_2.18.0-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libhtml-form-perl all 6.13-1 [32.6 kB]
Fetched 32.6 kB in 0s (613 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp2rlzec6j/libhtml-form-perl_6.13-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libhttp-message-perl all 7.01-1 [80.0 kB]
Fetched 80.0 kB in 0s (1479 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpb4bn8iu_/libhttp-message-perl_7.01-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmagic-mgc riscv64 1:5.46-5+b1 [338 kB]
Fetched 338 kB in 0s (12.5 MB/s)
dpkg-name: info: moved 'libmagic-mgc_1%3a5.46-5+b1_riscv64.deb' to '/srv/rebuilderd/tmp/tmpdd8368_v/libmagic-mgc_5.46-5+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 gcc riscv64 4:15.2.0-5 [5156 B]
Fetched 5156 B in 0s (265 kB/s)
dpkg-name: info: moved 'gcc_4%3a15.2.0-5_riscv64.deb' to '/srv/rebuilderd/tmp/tmp6entqcjc/gcc_15.2.0-5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 zlib1g-dev riscv64 1:1.3.dfsg+really1.3.1-3 [996 kB]
Fetched 996 kB in 0s (24.3 MB/s)
dpkg-name: info: moved 'zlib1g-dev_1%3a1.3.dfsg+really1.3.1-3_riscv64.deb' to '/srv/rebuilderd/tmp/tmpeavxfawh/zlib1g-dev_1.3.dfsg+really1.3.1-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libthai0 riscv64 0.1.30-1 [51.7 kB]
Fetched 51.7 kB in 0s (967 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp2c5dt6v6/libthai0_0.1.30-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 plzip riscv64 1.13~rc1-3 [63.9 kB]
Fetched 63.9 kB in 0s (1188 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp2sx254ns/plzip_1.13~rc1-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libcgi-pm-perl all 4.71-1 [217 kB]
Fetched 217 kB in 0s (3800 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpt8l4b9yd/libcgi-pm-perl_4.71-1_all.deb'
Downloading dependency 1 of 663: libregexp-common-perl:riscv64=2024080801-1
Downloading dependency 2 of 663: libxfixes3:riscv64=1:6.0.0-2+b5
Downloading dependency 3 of 663: gcc-riscv64-linux-gnu:riscv64=4:15.2.0-5
Downloading dependency 4 of 663: libpackage-stash-perl:riscv64=0.40-1
Downloading dependency 5 of 663: libselinux1:riscv64=3.9-4+b1
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Downloading dependency 122 of 663: libxmlb2:riscv64=0.3.24-2Get:1 http://deb.debian.org/debian unstable/main riscv64 libxmlb2 riscv64 0.3.24-2 [65.4 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libset-intspan-perl all 1.19-3 [25.3 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libqt6help6 riscv64 6.9.2-5 [191 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp6drxc0ol/libparse-recdescent-perl_1.967015+dfsg-4_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libatomic1 riscv64 15.2.0-13 [8580 B]
Fetched 8580 B in 0s (438 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmppw54551g/libatomic1_15.2.0-13_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libjson-maybexs-perl all 1.004008-1 [12.9 kB]
Fetched 12.9 kB in 0s (239 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpvmoa98w1/libjson-maybexs-perl_1.004008-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libattr1 riscv64 1:2.5.2-4 [23.0 kB]
Fetched 23.0 kB in 0s (1138 kB/s)
dpkg-name: info: moved 'libattr1_1%3a2.5.2-4_riscv64.deb' to '/srv/rebuilderd/tmp/tmp3xqkmg_u/libattr1_2.5.2-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libwebp7 riscv64 1.5.0-0.1+b1 [284 kB]
Fetched 284 kB in 0s (4828 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpwdgm38oh/libwebp7_1.5.0-0.1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libclass-load-perl all 0.25-2 [15.3 kB]
Fetched 15.3 kB in 0s (294 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0ie6fi3x/libclass-load-perl_0.25-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpod-parser-perl all 1.67-1 [94.1 kB]
Fetched 94.1 kB in 0s (1741 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0ss0cgxv/libpod-parser-perl_1.67-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libasound2-data all 1.2.15.3-1 [21.2 kB]
Fetched 21.2 kB in 0s (1070 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpen3gfvyq/libasound2-data_1.2.15.3-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmarkdown2 riscv64 2.2.7-2.1+b1 [35.9 kB]
Fetched 35.9 kB in 0s (669 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpzjfxkw0a/libmarkdown2_2.2.7-2.1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxxf86vm1 riscv64 1:1.1.4-2 [20.3 kB]
Fetched 20.3 kB in 0s (1019 kB/s)
dpkg-name: info: moved 'libxxf86vm1_1%3a1.1.4-2_riscv64.deb' to '/srv/rebuilderd/tmp/tmpa65rtgeq/libxxf86vm1_1.1.4-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libassuan9 riscv64 3.0.2-2+b1 [61.4 kB]
Fetched 61.4 kB in 0s (1128 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpqwe5fwzo/libassuan9_3.0.2-2+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liblsan0 riscv64 15.2.0-13 [1326 kB]
Fetched 1326 kB in 0s (27.8 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpvlzlmgzn/liblsan0_15.2.0-13_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtext-autoformat-perl all 1.750000-2 [35.2 kB]
Fetched 35.2 kB in 0s (661 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpj8csk9wc/libtext-autoformat-perl_1.750000-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libconfig-model-perl all 2.155-1 [398 kB]
Fetched 398 kB in 0s (14.1 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpobjoxggl/libconfig-model-perl_2.155-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libavahi-common-data riscv64 0.8-18 [113 kB]
Fetched 113 kB in 0s (2039 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpk_lb70gm/libavahi-common-data_0.8-18_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmodule-implementation-perl all 0.09-2 [12.6 kB]
Fetched 12.6 kB in 0s (240 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp325e1a3x/libmodule-implementation-perl_0.09-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libarchive-zip-perl all 1.68-1 [104 kB]
Fetched 104 kB in 0s (4760 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp60klehcp/libarchive-zip-perl_1.68-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libboolean-perl all 0.46-3 [9924 B]
Fetched 9924 B in 0s (190 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmphfglebkt/libboolean-perl_0.46-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libapt-pkg-perl riscv64 0.1.43 [66.8 kB]
Fetched 66.8 kB in 0s (1243 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpqowh2q6y/libapt-pkg-perl_0.1.43_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfftw3-bin riscv64 3.3.10-2+b2 [44.7 kB]
Fetched 44.7 kB in 0s (843 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp04nhq6c9/libfftw3-bin_3.3.10-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgmp10 riscv64 2:6.3.0+dfsg-5+b1 [563 kB]
Fetched 563 kB in 0s (8667 kB/s)
dpkg-name: info: moved 'libgmp10_2%3a6.3.0+dfsg-5+b1_riscv64.deb' to '/srv/rebuilderd/tmp/tmp9iiu6sis/libgmp10_6.3.0+dfsg-5+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libheif-plugin-dav1d riscv64 1.21.2-3 [19.6 kB]
Fetched 19.6 kB in 0s (373 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpcu5pewy4/libheif-plugin-dav1d_1.21.2-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmouse-perl riscv64 2.6.1-1 [143 kB]
Fetched 143 kB in 0s (2582 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0x5giab2/libmouse-perl_2.6.1-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcb-icccm4 riscv64 0.4.2-1+b1 [27.2 kB]
Fetched 27.2 kB in 0s (1355 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpvu9pfdbn/libxcb-icccm4_0.4.2-1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpsl-dev riscv64 0.21.2-1.1+b2 [91.7 kB]
Fetched 91.7 kB in 0s (4302 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpoo66ht28/libpsl-dev_0.21.2-1.1+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libyaml-0-2 riscv64 0.2.5-2+b1 [56.2 kB]
Fetched 56.2 kB in 0s (2674 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpbzr_rhtp/libyaml-0-2_0.2.5-2+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 man-db riscv64 2.13.1-1 [1458 kB]
Fetched 1458 kB in 0s (28.7 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp83s1ooeg/man-db_2.13.1-1_riscv64.deb'
Get:1 http://snapshot.debian.org/archive/debian/20260223T142455Z sid/main riscv64 ca-certificates all 20250419 [162 kB]
Fetched 162 kB in 0s (2888 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp35hjft3j/ca-certificates_20250419_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libstdc++-15-dev riscv64 15.2.0-13 [6162 kB]
Fetched 6162 kB in 0s (41.6 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpge4pvtr5/libstdc++-15-dev_15.2.0-13_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfile-listing-perl all 6.16-1 [12.4 kB]
Fetched 12.4 kB in 0s (237 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpgloqpnhg/libfile-listing-perl_6.16-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmailtools-perl all 2.22-1 [88.8 kB]
Fetched 88.8 kB in 0s (1636 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp5cvvgzp7/libmailtools-perl_2.22-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libperl-critic-perl all 1.156-1 [685 kB]
Fetched 685 kB in 0s (10.2 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpx5meh436/libperl-critic-perl_1.156-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 xz-utils riscv64 5.8.2-2 [705 kB]
Fetched 705 kB in 0s (20.2 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmptwmft0n5/xz-utils_5.8.2-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libglu1-mesa riscv64 9.0.2-1.1+b4 [181 kB]
Fetched 181 kB in 0s (3222 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpbvtyy_9s/libglu1-mesa_9.0.2-1.1+b4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libglpk40 riscv64 5.0-2+b1 [376 kB]
Fetched 376 kB in 0s (6217 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp5kr_46ly/libglpk40_5.0-2+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 comerr-dev riscv64 2.1-1.47.2-3+b8 [60.9 kB]
Fetched 60.9 kB in 0s (2900 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmps9gevoy7/comerr-dev_2.1-1.47.2-3+b8_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libnet-http-perl all 6.24-1 [23.2 kB]
Fetched 23.2 kB in 0s (443 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpi0cdw518/libnet-http-perl_6.24-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpango-1.0-0 riscv64 1.57.0-1 [227 kB]
Fetched 227 kB in 0s (3943 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpjohkrbq4/libpango-1.0-0_1.57.0-1_riscv64.deb'
Get:1 http://snapshot.debian.org/archive/debian/20260223T142455Z sid/main riscv64 liblz4-1 riscv64 1.10.0-6 [86.6 kB]
Fetched 86.6 kB in 0s (2278 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpwzmo32t3/liblz4-1_1.10.0-6_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmodule-pluggable-perl all 6.3-1 [24.1 kB]
Fetched 24.1 kB in 0s (460 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp1yr36gx0/libmodule-pluggable-perl_6.3-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtext-glob-perl all 0.11-3 [7676 B]
Fetched 7676 B in 0s (147 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpjpqdx9wp/libtext-glob-perl_0.11-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libconvert-binhex-perl all 1.125-3 [27.4 kB]
Fetched 27.4 kB in 0s (518 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpg29ca4mx/libconvert-binhex-perl_1.125-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libdbus-1-3 riscv64 1.16.2-4 [180 kB]
Fetched 180 kB in 0s (7751 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpi7ncf9hh/libdbus-1-3_1.16.2-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liblist-moreutils-perl all 0.430-2 [46.9 kB]
Fetched 46.9 kB in 0s (882 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmphg20kip0/liblist-moreutils-perl_0.430-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libwayland-client0 riscv64 1.24.0-2+b2 [28.4 kB]
Fetched 28.4 kB in 0s (529 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpnnjwo8z1/libwayland-client0_1.24.0-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 hostname riscv64 3.25 [10.7 kB]
Fetched 10.7 kB in 0s (546 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpw94jzzos/hostname_3.25_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libcrypt1 riscv64 1:4.5.1-1 [114 kB]
Fetched 114 kB in 0s (5086 kB/s)
dpkg-name: info: moved 'libcrypt1_1%3a4.5.1-1_riscv64.deb' to '/srv/rebuilderd/tmp/tmpgtub61aq/libcrypt1_4.5.1-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsqlite3-0 riscv64 3.46.1-9 [910 kB]
Fetched 910 kB in 0s (23.2 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpxmzd6p7_/libsqlite3-0_3.46.1-9_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libhash-merge-perl all 0.302-1 [14.7 kB]
Fetched 14.7 kB in 0s (281 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmptbdfpjol/libhash-merge-perl_0.302-1_all.deb'
Get:1 http://snapshot.debian.org/archive/debian/20260223T142455Z sid/main riscv64 libnghttp2-dev riscv64 1.68.0-1 [237 kB]
Fetched 237 kB in 0s (4121 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp8lt8byk9/libnghttp2-dev_1.68.0-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 perl-openssl-defaults riscv64 7+b2 [6724 B]
Fetched 6724 B in 0s (128 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp41maz5ky/perl-openssl-defaults_7+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liburi-perl all 5.34-2 [111 kB]
Fetched 111 kB in 0s (2021 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpli4ak37s/liburi-perl_5.34-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libparse-debcontrol-perl all 2.005-6 [21.6 kB]
Fetched 21.6 kB in 0s (409 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpfe2sk78g/libparse-debcontrol-perl_2.005-6_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtry-tiny-perl all 0.32-1 [22.9 kB]
Fetched 22.9 kB in 0s (437 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmppztoupta/libtry-tiny-perl_0.32-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxxhash0 riscv64 0.8.3-2+b1 [40.6 kB]
Fetched 40.6 kB in 0s (1996 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp61pohrsp/libxxhash0_0.8.3-2+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 unzip riscv64 6.0-29 [173 kB]
Fetched 173 kB in 0s (3070 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpdpxs6qe8/unzip_6.0-29_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcb-xinput0 riscv64 1.17.0-2+b2 [130 kB]
Fetched 130 kB in 0s (2066 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp9wzq5d8n/libxcb-xinput0_1.17.0-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libhdf5-310 riscv64 1.14.6+repack-2 [1388 kB]
Fetched 1388 kB in 0s (28.1 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpmndzqj_9/libhdf5-310_1.14.6+repack-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liblist-someutils-perl all 0.59-1 [37.1 kB]
Fetched 37.1 kB in 0s (693 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpdmcm5ibh/liblist-someutils-perl_0.59-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtool all 2.5.4-9 [540 kB]
Fetched 540 kB in 0s (17.4 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpn733j7tz/libtool_2.5.4-9_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libapp-cmd-perl all 0.339-1 [64.6 kB]
Fetched 64.6 kB in 0s (1208 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp90m42oqg/libapp-cmd-perl_0.339-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtasn1-6 riscv64 4.21.0-2 [50.7 kB]
Fetched 50.7 kB in 0s (2485 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpm6eiym1n/libtasn1-6_4.21.0-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 gcc-15-riscv64-linux-gnu riscv64 15.2.0-13 [28.6 MB]
Fetched 28.6 MB in 1s (40.2 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0hz59ckj/gcc-15-riscv64-linux-gnu_15.2.0-13_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libencode-locale-perl all 1.05-3 [12.9 kB]
Fetched 12.9 kB in 0s (247 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpmbr9jm99/libencode-locale-perl_1.05-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libwww-robotrules-perl all 6.02-1 [12.9 kB]
Fetched 12.9 kB in 0s (245 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp9auq3plx/libwww-robotrules-perl_6.02-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libqt6xml6 riscv64 6.9.2+dfsg-4 [88.1 kB]
Fetched 88.1 kB in 0s (4143 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpxjf7wnbq/libqt6xml6_6.9.2+dfsg-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgnutls30t64 riscv64 3.8.12-3 [1491 kB]
Fetched 1491 kB in 0s (29.0 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpcojz0qsl/libgnutls30t64_3.8.12-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libappstream5 riscv64 1.1.2-1 [220 kB]
Fetched 220 kB in 0s (3789 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpwalqovm4/libappstream5_1.1.2-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liblog-any-perl all 1.718-1 [75.0 kB]
Fetched 75.0 kB in 0s (1392 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp_98jah72/liblog-any-perl_1.718-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libqrupdate1 riscv64 1.1.5-3 [33.3 kB]
Fetched 33.3 kB in 0s (631 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp39h_up1q/libqrupdate1_1.1.5-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgcrypt20 riscv64 1.11.2-3+b1 [867 kB]
Fetched 867 kB in 0s (22.7 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpblueqilc/libgcrypt20_1.11.2-3+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsyntax-keyword-try-perl riscv64 0.31-1 [27.3 kB]
Fetched 27.3 kB in 0s (518 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmph0s8r2_d/libsyntax-keyword-try-perl_0.31-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 patchutils riscv64 0.4.3-1 [77.0 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp9qx9nhd_/patchutils_0.4.3-1_riscv64.deb'
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Downloading dependency 123 of 663: libwmflite-0.2-7:riscv64=0.2.13-2
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Downloading dependency 243 of 663: gnuplot-nox:riscv64=6.0.3+dfsg1-1Get:1 http://deb.debian.org/debian unstable/main riscv64 gnuplot-nox riscv64 6.0.3+dfsg1-1 [923 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libspqr4 riscv64 1:7.12.2+dfsg-1 [139 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 liblz1 riscv64 1.16~rc1-3 [42.5 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libexception-class-perl all 1.45-1 [34.6 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libhdf5-hl-cpp-310 riscv64 1.14.6+repack-2 [19.7 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libtimedate-perl all 2.3300-2 [39.3 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libppix-quotelike-perl all 0.023-1 [74.6 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libqt6openglwidgets6 riscv64 6.9.2+dfsg-4 [50.6 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libpam-modules-bin riscv64 1.7.0-5+b1 [49.6 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libsafe-isa-perl all 1.000010-1 [8288 B]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libgcc-s1 riscv64 15.2.0-13 [61.5 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libtsan2 riscv64 15.2.0-13 [2654 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libproc-processtable-perl riscv64 0.637-1+b1 [41.6 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libdata-messagepack-perl riscv64 1.02-3 [33.3 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libegl-mesa0 riscv64 26.0.0-1 [128 kB]
Fetched 128 kB in 0s (2297 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpsddp3knt/libegl-mesa0_26.0.0-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libkrb5-dev riscv64 1.22.1-2 [16.2 kB]
Fetched 16.2 kB in 0s (813 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpx8g7c63v/libkrb5-dev_1.22.1-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgfortran5 riscv64 15.2.0-13 [420 kB]
Fetched 420 kB in 0s (14.7 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp_dulrvkq/libgfortran5_15.2.0-13_riscv64.deb'
Get:1 http://snapshot.debian.org/archive/debian/20260225T215433Z sid/main riscv64 libunicode-utf8-perl riscv64 0.64-1 [18.8 kB]
Fetched 18.8 kB in 0s (357 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpia0eiqkq/libunicode-utf8-perl_0.64-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 iso-codes all 4.20.1-1 [3319 kB]
Fetched 3319 kB in 0s (26.8 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpxkyklgi3/iso-codes_4.20.1-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmp3lame0 riscv64 3.101~svn6525+dfsg-2 [285 kB]
Fetched 285 kB in 0s (11.0 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpghsqrhe5/libmp3lame0_3.101~svn6525+dfsg-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfile-libmagic-perl riscv64 1.23-2+b2 [30.9 kB]
Fetched 30.9 kB in 0s (332 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpsqi5cm_c/libfile-libmagic-perl_1.23-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libbinutils riscv64 2.46-2 [509 kB]
Fetched 509 kB in 0s (16.7 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpl4dh2fzj/libbinutils_2.46-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libyaml-pp-perl all 0.39.0-1 [111 kB]
Fetched 111 kB in 0s (2032 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp2nv48h7h/libyaml-pp-perl_0.39.0-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libproxy1v5 riscv64 0.5.12-1 [26.0 kB]
Fetched 26.0 kB in 0s (1299 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0h2u3q33/libproxy1v5_0.5.12-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 gettext riscv64 0.23.2-1 [1684 kB]
Fetched 1684 kB in 0s (18.3 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmplk4b6eac/gettext_0.23.2-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libregexp-pattern-license-perl all 3.11.2-1 [94.6 kB]
Fetched 94.6 kB in 0s (1743 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmplwrhz03w/libregexp-pattern-license-perl_3.11.2-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libngtcp2-crypto-ossl0 riscv64 1.16.0-1 [27.7 kB]
Fetched 27.7 kB in 0s (441 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpkzhnjc0r/libngtcp2-crypto-ossl0_1.16.0-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtasn1-6-dev riscv64 4.21.0-2 [161 kB]
Fetched 161 kB in 0s (6772 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmph7fn17p5/libtasn1-6-dev_4.21.0-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcb-util1 riscv64 0.4.1-1+b1 [23.4 kB]
Fetched 23.4 kB in 0s (1179 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpzikpw8_s/libxcb-util1_0.4.1-1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libncurses6 riscv64 6.6+20251231-1 [105 kB]
Fetched 105 kB in 0s (4681 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpfaiz52f9/libncurses6_6.6+20251231-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsm6 riscv64 2:1.2.6-1+b1 [38.1 kB]
Fetched 38.1 kB in 0s (1867 kB/s)
dpkg-name: info: moved 'libsm6_2%3a1.2.6-1+b1_riscv64.deb' to '/srv/rebuilderd/tmp/tmp14j5_3qw/libsm6_1.2.6-1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpam0g riscv64 1.7.0-5+b1 [70.7 kB]
Fetched 70.7 kB in 0s (3370 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpnf0z6bfr/libpam0g_1.7.0-5+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcb-image0 riscv64 0.4.0-2+b3 [22.1 kB]
Fetched 22.1 kB in 0s (1110 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpfmdwzued/libxcb-image0_0.4.0-2+b3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 dpkg-dev all 1.23.5 [1318 kB]
Fetched 1318 kB in 0s (27.6 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpi9y_smn8/dpkg-dev_1.23.5_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfeature-compat-class-perl all 0.08-1 [12.4 kB]
Fetched 12.4 kB in 0s (238 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp3i2wxbw9/libfeature-compat-class-perl_0.08-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libflac14 riscv64 1.5.0+ds-5 [162 kB]
Fetched 162 kB in 0s (6948 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpckhk524u/libflac14_1.5.0+ds-5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libunistring5 riscv64 1.3-2+b1 [471 kB]
Fetched 471 kB in 0s (15.9 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpowqsiij4/libunistring5_1.3-2+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libdatrie1 riscv64 0.2.14-1 [39.7 kB]
Fetched 39.7 kB in 0s (749 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp2_h5yyt1/libdatrie1_0.2.14-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 zlib1g riscv64 1:1.3.dfsg+really1.3.1-3 [85.7 kB]
Fetched 85.7 kB in 0s (1562 kB/s)
dpkg-name: info: moved 'zlib1g_1%3a1.3.dfsg+really1.3.1-3_riscv64.deb' to '/srv/rebuilderd/tmp/tmpe5hlra6v/zlib1g_1.3.dfsg+really1.3.1-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libqt6printsupport6 riscv64 6.9.2+dfsg-4 [221 kB]
Fetched 221 kB in 0s (9062 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp1f2i3qd0/libqt6printsupport6_6.9.2+dfsg-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libkadm5clnt-mit12 riscv64 1.22.1-2 [42.2 kB]
Fetched 42.2 kB in 0s (2093 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmphw__uic2/libkadm5clnt-mit12_1.22.1-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsoftware-license-perl all 0.104007-1 [121 kB]
Fetched 121 kB in 0s (2195 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpb54jggfx/libsoftware-license-perl_0.104007-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 build-essential riscv64 12.12 [4628 B]
Fetched 4628 B in 0s (235 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpm6vryd1_/build-essential_12.12_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 base-files riscv64 14 [72.9 kB]
Fetched 72.9 kB in 0s (3461 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpgrwu_ty4/base-files_14_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 cpp riscv64 4:15.2.0-5 [1572 B]
Fetched 1572 B in 0s (79.9 kB/s)
dpkg-name: info: moved 'cpp_4%3a15.2.0-5_riscv64.deb' to '/srv/rebuilderd/tmp/tmp915pbfqe/cpp_15.2.0-5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libinput-bin riscv64 1.31.0-1 [28.7 kB]
Fetched 28.7 kB in 0s (1413 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp9_ojzx4l/libinput-bin_1.31.0-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfftw3-long3 riscv64 3.3.10-2+b2 [701 kB]
Fetched 701 kB in 0s (10.4 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp8fdxv2mv/libfftw3-long3_3.3.10-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libicu76 riscv64 76.1-4+b1 [9709 kB]
Fetched 9709 kB in 0s (44.2 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpla_q9r5o/libicu76_76.1-4+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libhdf5-hl-310 riscv64 1.14.6+repack-2 [67.1 kB]
Fetched 67.1 kB in 0s (3179 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp91lsmyvr/libhdf5-hl-310_1.14.6+repack-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libclass-data-inheritable-perl all 0.10-1 [8632 B]
Fetched 8632 B in 0s (165 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp4yj08pye/libclass-data-inheritable-perl_0.10-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libheif1 riscv64 1.21.2-3 [616 kB]
Fetched 616 kB in 0s (9354 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmprrojesez/libheif1_1.21.2-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liblist-utilsby-perl all 0.12-2 [15.5 kB]
Fetched 15.5 kB in 0s (297 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpmsno9d0y/liblist-utilsby-perl_0.12-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 shared-mime-info riscv64 2.4-5+b3 [756 kB]
Fetched 756 kB in 0s (21.1 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpzo8hrcv9/shared-mime-info_2.4-5+b3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libkdb5-10t64 riscv64 1.22.1-2 [43.9 kB]
Fetched 43.9 kB in 0s (2151 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpa_daylj2/libkdb5-10t64_1.22.1-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmime-tools-perl all 5.517-1 [204 kB]
Fetched 204 kB in 0s (3594 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpz7115b06/libmime-tools-perl_5.517-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 patch riscv64 2.8-2 [134 kB]
Fetched 134 kB in 0s (5978 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmphmyw898o/patch_2.8-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libparams-util-perl riscv64 1.102-3+b1 [23.9 kB]
Fetched 23.9 kB in 0s (543 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp_spqecc_/libparams-util-perl_1.102-3+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpipeline1 riscv64 1.5.8-2 [40.8 kB]
Fetched 40.8 kB in 0s (1928 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpgx_2e165/libpipeline1_1.5.8-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtext-template-perl all 1.61-1 [54.4 kB]
Fetched 54.4 kB in 0s (999 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmppeya23hn/libtext-template-perl_1.61-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libisl23 riscv64 0.27-1+b1 [664 kB]
Fetched 664 kB in 0s (19.6 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpe_qxzx0c/libisl23_0.27-1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 g++-15-riscv64-linux-gnu riscv64 15.2.0-13 [15.8 MB]
Fetched 15.8 MB in 0s (40.6 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp94e4hitu/g++-15-riscv64-linux-gnu_15.2.0-13_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsuitesparseconfig7 riscv64 1:7.12.2+dfsg-1 [33.4 kB]
Fetched 33.4 kB in 0s (619 kB/s)
dpkg-name: info: moved 'libsuitesparseconfig7_1%3a7.12.2+dfsg-1_riscv64.deb' to '/srv/rebuilderd/tmp/tmpv2xws279/libsuitesparseconfig7_7.12.2+dfsg-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 dh-octave all 1.14.1 [25.8 kB]
Fetched 25.8 kB in 0s (1074 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpd4bmal5f/dh-octave_1.14.1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmoo-perl all 2.005005-1 [58.0 kB]
Fetched 58.0 kB in 0s (1057 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpb_3qewa3/libmoo-perl_2.005005-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libedit2 riscv64 3.1-20251016-1 [92.5 kB]
Fetched 92.5 kB in 0s (4153 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpinj8dz_e/libedit2_3.1-20251016-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libobject-pad-perl riscv64 0.823-2 [142 kB]
Fetched 142 kB in 0s (5771 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp_00m9wvi/libobject-pad-perl_0.823-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 librtmp-dev riscv64 2.4+20151223.gitfa8646d.1-3+b1 [133 kB]
Fetched 133 kB in 0s (5629 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpe0qnmj2p/librtmp-dev_2.4+20151223.gitfa8646d.1-3+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgd3 riscv64 2.3.3-13+b1 [127 kB]
Fetched 127 kB in 0s (2304 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpf2p7q2ju/libgd3_2.3.3-13+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libclass-tiny-perl all 1.008-2 [18.6 kB]
Fetched 18.6 kB in 0s (353 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpcrl30qan/libclass-tiny-perl_1.008-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsoftware-copyright-perl all 0.015-1 [15.5 kB]
Fetched 15.5 kB in 0s (296 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpx7yjfoui/libsoftware-copyright-perl_0.015-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 gcc-15-base riscv64 15.2.0-13 [54.8 kB]
Fetched 54.8 kB in 0s (2643 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp69_jem_m/gcc-15-base_15.2.0-13_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libctf0 riscv64 2.46-2 [96.1 kB]
Fetched 96.1 kB in 0s (4475 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp2ztbgcks/libctf0_2.46-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcb-present0 riscv64 1.17.0-2+b2 [105 kB]
Fetched 105 kB in 0s (4832 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp92z0ewgy/libxcb-present0_1.17.0-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libngtcp2-16 riscv64 1.16.0-1 [141 kB]
Fetched 141 kB in 0s (5408 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpq_p92fpd/libngtcp2-16_1.16.0-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libconfig-tiny-perl all 2.30-1 [18.9 kB]
Fetched 18.9 kB in 0s (361 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpcy4vr8n0/libconfig-tiny-perl_2.30-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgraphicsmagick-q16-3t64 riscv64 1.4+really1.3.46-2 [1202 kB]
Fetched 1202 kB in 0s (15.5 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmppc29roa9/libgraphicsmagick-q16-3t64_1.4+really1.3.46-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgssapi-krb5-2 riscv64 1.22.1-2 [141 kB]
Fetched 141 kB in 0s (6206 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpgum6xf_f/libgssapi-krb5-2_1.22.1-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liblcms2-2 riscv64 2.17-1 [170 kB]
Fetched 170 kB in 0s (3022 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp67iws72o/liblcms2-2_2.17-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 texinfo-lib riscv64 7.2-5 [808 kB]
Fetched 808 kB in 0s (11.7 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpwj86e7_0/texinfo-lib_7.2-5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 findutils riscv64 4.10.0-3 [706 kB]
Fetched 706 kB in 0s (20.2 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp7cw3ezty/findutils_4.10.0-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libyaml-tiny-perl all 1.76-1 [29.8 kB]
Fetched 29.8 kB in 0s (564 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpiw0z4y2y/libyaml-tiny-perl_1.76-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 aglfn all 1.7+git20191031.4036a9c-2 [30.5 kB]
Fetched 30.5 kB in 0s (580 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpk3mzhi3p/aglfn_1.7+git20191031.4036a9c-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 sysvinit-utils riscv64 3.15-6 [34.6 kB]
Fetched 34.6 kB in 0s (1707 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp3oq48c_n/sysvinit-utils_3.15-6_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libogg0 riscv64 1.3.6-2 [24.0 kB]
Fetched 24.0 kB in 0s (1171 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmphtjvbn9s/libogg0_1.3.6-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liblog-log4perl-perl all 1.57-1 [367 kB]
Fetched 367 kB in 0s (6108 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpel0hq5ir/liblog-log4perl-perl_1.57-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxext6 riscv64 2:1.3.4-1+b4 [51.1 kB]
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Downloading dependency 244 of 663: autopoint:riscv64=0.23.2-1
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Downloading dependency 364 of 663: libglib2.0-0t64:riscv64=2.87.2-3Get:1 http://deb.debian.org/debian unstable/main riscv64 libglib2.0-0t64 riscv64 2.87.2-3 [1556 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libdata-validate-uri-perl all 0.07-3 [11.0 kB]
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dpkg-name: info: moved 'gfortran_4%3a15.2.0-5_riscv64.deb' to '/srv/rebuilderd/tmp/tmpe0vou4bv/gfortran_15.2.0-5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 dwz riscv64 0.16-2 [115 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libtext-levenshtein-damerau-perl all 0.41-3 [12.3 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 mesa-libgallium riscv64 26.0.0-1 [9789 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libqt6network6 riscv64 6.9.2+dfsg-4 [806 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 ucf all 3.0052 [43.3 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 sed riscv64 4.9-2 [329 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libffi8 riscv64 3.5.2-3+b1 [22.3 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp60k6cnoq/libffi8_3.5.2-3+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfile-basedir-perl all 0.09-2 [15.1 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 appstream riscv64 1.1.2-1 [566 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libcap2 riscv64 1:2.75-10+b5 [29.1 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 init-system-helpers all 1.69 [39.3 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libarray-intspan-perl all 2.004-2 [25.7 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 diffutils riscv64 1:3.12-1 [405 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libcapture-tiny-perl all 0.50-1 [24.6 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libxml-namespacesupport-perl all 1.12-2 [15.1 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libexpat1 riscv64 2.7.4-1 [111 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpc83nxews/libexpat1_2.7.4-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxkbcommon0 riscv64 1.13.1-1 [144 kB]
Fetched 144 kB in 0s (2582 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpath_3x1_/libxkbcommon0_1.13.1-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 nettle-dev riscv64 3.10.2-1 [1557 kB]
Fetched 1557 kB in 0s (29.8 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpijrxi9f7/nettle-dev_3.10.2-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfile-sharedir-perl all 1.118-3 [16.0 kB]
Fetched 16.0 kB in 0s (307 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpi4rvd77j/libfile-sharedir-perl_1.118-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libdevel-stacktrace-perl all 2.0500-1 [26.4 kB]
Fetched 26.4 kB in 0s (502 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpihdiuisq/libdevel-stacktrace-perl_2.0500-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liberror-perl all 0.17030-1 [26.9 kB]
Fetched 26.9 kB in 0s (511 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp9iu2l7nk/liberror-perl_0.17030-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libksba8 riscv64 1.6.7-2+b2 [136 kB]
Fetched 136 kB in 0s (2457 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpy3x8waev/libksba8_1.6.7-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libblas-dev riscv64 3.12.1-7+b1 [291 kB]
Fetched 291 kB in 0s (4986 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp9y9xzi79/libblas-dev_3.12.1-7+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsub-quote-perl all 2.006009-1 [21.3 kB]
Fetched 21.3 kB in 0s (406 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpmj71jkg8/libsub-quote-perl_2.006009-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libncursesw6 riscv64 6.6+20251231-1 [141 kB]
Fetched 141 kB in 0s (6242 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpz4qff998/libncursesw6_6.6+20251231-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libitm1 riscv64 15.2.0-13 [25.5 kB]
Fetched 25.5 kB in 0s (1276 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp89i7dctb/libitm1_15.2.0-13_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxinerama1 riscv64 2:1.1.4-3+b5 [16.0 kB]
Fetched 16.0 kB in 0s (810 kB/s)
dpkg-name: info: moved 'libxinerama1_2%3a1.1.4-3+b5_riscv64.deb' to '/srv/rebuilderd/tmp/tmpsb6qgcfl/libxinerama1_1.1.4-3+b5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libaom3 riscv64 3.13.1-2 [1313 kB]
Fetched 1313 kB in 0s (15.2 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpq65_o__h/libaom3_3.13.1-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libyuv0 riscv64 0.0.1922.20260106-1 [113 kB]
Fetched 113 kB in 0s (2023 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpjmliabw7/libyuv0_0.0.1922.20260106-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmd4c0 riscv64 0.5.2-2+b2 [49.3 kB]
Fetched 49.3 kB in 0s (2395 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp_sz5gbf5/libmd4c0_0.5.2-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmodule-runtime-perl all 0.018-1 [17.8 kB]
Fetched 17.8 kB in 0s (341 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpgurau42m/libmodule-runtime-perl_0.018-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpod-spell-perl all 1.27-1 [32.0 kB]
Fetched 32.0 kB in 0s (604 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp52ag2fob/libpod-spell-perl_1.27-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxml-sax-perl all 1.02+dfsg-4 [53.4 kB]
Fetched 53.4 kB in 0s (999 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpcdo2jdnk/libxml-sax-perl_1.02+dfsg-4_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libavahi-common3 riscv64 0.8-18 [44.4 kB]
Fetched 44.4 kB in 0s (2195 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp1gd8alqe/libavahi-common3_0.8-18_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libperl5.40 riscv64 5.40.1-7 [3952 kB]
Fetched 3952 kB in 0s (29.0 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpdbdvuu32/libperl5.40_5.40.1-7_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libaudit-common all 1:4.1.2-1 [14.3 kB]
Fetched 14.3 kB in 0s (726 kB/s)
dpkg-name: info: moved 'libaudit-common_1%3a4.1.2-1_all.deb' to '/srv/rebuilderd/tmp/tmpgs3v56tl/libaudit-common_4.1.2-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liblzma5 riscv64 5.8.2-2 [329 kB]
Fetched 329 kB in 0s (12.4 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpzqxwn6w2/liblzma5_5.8.2-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libqt6sql6 riscv64 6.9.2+dfsg-4 [149 kB]
Fetched 149 kB in 0s (6517 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp8av8oomp/libqt6sql6_6.9.2+dfsg-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libstring-rewriteprefix-perl all 0.009-1 [7140 B]
Fetched 7140 B in 0s (137 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpzwk13x44/libstring-rewriteprefix-perl_0.009-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgbm1 riscv64 26.0.0-1 [53.7 kB]
Fetched 53.7 kB in 0s (2619 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpnd4j_3st/libgbm1_26.0.0-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libstdc++6 riscv64 15.2.0-13 [715 kB]
Fetched 715 kB in 0s (9911 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp7_8edcv0/libstdc++6_15.2.0-13_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libarpack2t64 riscv64 3.9.1-6+b1 [94.2 kB]
Fetched 94.2 kB in 0s (1729 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpwhtanf6_/libarpack2t64_3.9.1-6+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsub-exporter-progressive-perl all 0.001013-3 [7496 B]
Fetched 7496 B in 0s (144 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmptiiplimw/libsub-exporter-progressive-perl_0.001013-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libhdf5-cpp-310 riscv64 1.14.6+repack-2 [126 kB]
Fetched 126 kB in 0s (5672 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpkjyoctgp/libhdf5-cpp-310_1.14.6+repack-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcb-shm0 riscv64 1.17.0-2+b2 [105 kB]
Fetched 105 kB in 0s (4830 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpc2en756k/libxcb-shm0_1.17.0-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libaec-dev riscv64 1.1.5-1 [51.2 kB]
Fetched 51.2 kB in 0s (2151 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpqer6fcat/libaec-dev_1.1.5-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgraphicsmagick++-q16-12t64 riscv64 1.4+really1.3.46-2 [124 kB]
Fetched 124 kB in 0s (1273 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmplwn1l7xo/libgraphicsmagick++-q16-12t64_1.4+really1.3.46-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libx11-data all 2:1.8.13-1 [346 kB]
Fetched 346 kB in 0s (12.8 MB/s)
dpkg-name: info: moved 'libx11-data_2%3a1.8.13-1_all.deb' to '/srv/rebuilderd/tmp/tmpeop6ayot/libx11-data_1.8.13-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libparams-validate-perl riscv64 1.31-2+b4 [63.5 kB]
Fetched 63.5 kB in 0s (1180 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp_lwxovc1/libparams-validate-perl_1.31-2+b4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libimport-into-perl all 1.002005-2 [11.3 kB]
Fetched 11.3 kB in 0s (216 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpdjiamw6h/libimport-into-perl_1.002005-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libqhull-r8.0 riscv64 2020.2-8 [247 kB]
Fetched 247 kB in 0s (4301 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpkrbunszf/libqhull-r8.0_2020.2-8_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcursor1 riscv64 1:1.2.3-1+b1 [40.3 kB]
Fetched 40.3 kB in 0s (1965 kB/s)
dpkg-name: info: moved 'libxcursor1_1%3a1.2.3-1+b1_riscv64.deb' to '/srv/rebuilderd/tmp/tmpabmau7vj/libxcursor1_1.2.3-1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libintl-perl all 1.37-1 [696 kB]
Fetched 696 kB in 0s (10.1 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpp2b9o5qf/libintl-perl_1.37-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libblas3 riscv64 3.12.1-7+b1 [123 kB]
Fetched 123 kB in 0s (2239 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpto9225ue/libblas3_3.12.1-7+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 binutils riscv64 2.46-2 [285 kB]
Fetched 285 kB in 0s (11.2 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp8652bnp4/binutils_2.46-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libduktape207 riscv64 2.7.0-2+b3 [138 kB]
Fetched 138 kB in 0s (6114 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpnv7jtc5t/libduktape207_2.7.0-2+b3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpsl5t64 riscv64 0.21.2-1.1+b2 [59.8 kB]
Fetched 59.8 kB in 0s (2871 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0jxpe6so/libpsl5t64_0.21.2-1.1+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libregexp-wildcards-perl all 1.05-3 [14.1 kB]
Fetched 14.1 kB in 0s (270 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpmdxnu2wf/libregexp-wildcards-perl_1.05-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libproc2-0 riscv64 2:4.0.4-9+b1 [65.1 kB]
Fetched 65.1 kB in 0s (1207 kB/s)
dpkg-name: info: moved 'libproc2-0_2%3a4.0.4-9+b1_riscv64.deb' to '/srv/rebuilderd/tmp/tmpwt9in208/libproc2-0_4.0.4-9+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libjansson4 riscv64 2.14-2+b4 [40.1 kB]
Fetched 40.1 kB in 0s (1979 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp3s7nnra6/libjansson4_2.14-2+b4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 g++ riscv64 4:15.2.0-5 [1332 B]
Fetched 1332 B in 0s (68.2 kB/s)
dpkg-name: info: moved 'g++_4%3a15.2.0-5_riscv64.deb' to '/srv/rebuilderd/tmp/tmp0lo6ryq_/g++_15.2.0-5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libdata-section-perl all 0.200008-1 [13.1 kB]
Fetched 13.1 kB in 0s (250 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpe0uotofg/libdata-section-perl_0.200008-1_all.deb'
Get:1 http://snapshot.debian.org/archive/debian/20260223T142455Z sid/main riscv64 libdebhelper-perl all 13.30 [92.7 kB]
Fetched 92.7 kB in 0s (4274 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpmy97grk0/libdebhelper-perl_13.30_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liblapack3 riscv64 3.12.1-7+b1 [1971 kB]
Fetched 1971 kB in 0s (21.1 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp7qtq6w63/liblapack3_3.12.1-7+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 netbase all 6.5 [12.4 kB]
Fetched 12.4 kB in 0s (626 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmphvwp9nbn/netbase_6.5_all.deb'
Get:1 http://snapshot.debian.org/archive/debian/20260223T142455Z sid/main riscv64 libsystemd0 riscv64 259.1-1 [481 kB]
Fetched 481 kB in 0s (16.0 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpcj4xkgsg/libsystemd0_259.1-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libppi-perl all 1.284-1 [300 kB]
Fetched 300 kB in 0s (5110 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp4vktydt9/libppi-perl_1.284-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libb-hooks-endofscope-perl all 0.28-2 [17.6 kB]
Fetched 17.6 kB in 0s (335 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpu756613r/libb-hooks-endofscope-perl_0.28-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 perl-base riscv64 5.40.1-7 [1678 kB]
Fetched 1678 kB in 0s (18.4 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp1zgq2_iz/perl-base_5.40.1-7_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libunbound8 riscv64 1.24.2-1 [617 kB]
Fetched 617 kB in 0s (18.7 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp58qnqdsq/libunbound8_1.24.2-1_riscv64.deb'
Get:1 http://snapshot.debian.org/archive/debian/20260223T142455Z sid/main riscv64 debhelper all 13.30 [942 kB]
Fetched 942 kB in 0s (23.7 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp5pvptu1u/debhelper_13.30_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 gpgconf riscv64 2.4.8-5 [128 kB]
Fetched 128 kB in 0s (2325 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp185pxmjd/gpgconf_2.4.8-5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 octave riscv64 10.3.0-3 [9360 kB]
Fetched 9360 kB in 0s (32.3 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpbm3hw5nl/octave_10.3.0-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libemail-address-xs-perl riscv64 1.05-1+b3 [29.4 kB]
Fetched 29.4 kB in 0s (559 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpj24m4vtx/libemail-address-xs-perl_1.05-1+b3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liblingua-en-inflect-perl all 1.905-2 [52.7 kB]
Fetched 52.7 kB in 0s (990 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpmoyu3a4w/liblingua-en-inflect-perl_1.905-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 pkgconf-bin riscv64 2.5.1-4 [35.1 kB]
Fetched 35.1 kB in 0s (1674 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpn3lpv5st/pkgconf-bin_2.5.1-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libqt6widgets6 riscv64 6.9.2+dfsg-4 [2736 kB]
Fetched 2736 kB in 0s (28.1 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpmv4uxyrg/libqt6widgets6_6.9.2+dfsg-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libcairo2 riscv64 1.18.4-3 [546 kB]
Fetched 546 kB in 0s (8555 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp2uf4qb4e/libcairo2_1.18.4-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgraphite2-3 riscv64 1.3.14-11+b1 [79.4 kB]
Fetched 79.4 kB in 0s (3758 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp3ye_8p3_/libgraphite2-3_1.3.14-11+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libnet-domain-tld-perl all 1.75-4 [31.5 kB]
Fetched 31.5 kB in 0s (598 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpry6kieg9/libnet-domain-tld-perl_1.75-4_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmpc3 riscv64 1.3.1-2+b1 [56.8 kB]
Fetched 56.8 kB in 0s (2764 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0h02hc2m/libmpc3_1.3.1-2+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsmartcols1 riscv64 2.41.3-4 [155 kB]
Fetched 155 kB in 0s (2769 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpwoidi54j/libsmartcols1_2.41.3-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libx11-6 riscv64 2:1.8.13-1 [821 kB]
Fetched 821 kB in 0s (22.0 MB/s)
dpkg-name: info: moved 'libx11-6_2%3a1.8.13-1_riscv64.deb' to '/srv/rebuilderd/tmp/tmpvesy8f8m/libx11-6_1.8.13-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpng16-16t64 riscv64 1.6.55-1 [286 kB]
Fetched 286 kB in 0s (11.1 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpvhrfefok/libpng16-16t64_1.6.55-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtext-wrapper-perl all 1.05-4 [10.3 kB]
Fetched 10.3 kB in 0s (198 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpwylzklbe/libtext-wrapper-perl_1.05-4_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxau6 riscv64 1:1.0.11-1+b1 [20.7 kB]
Fetched 20.7 kB in 0s (1046 kB/s)
dpkg-name: info: moved 'libxau6_1%3a1.0.11-1+b1_riscv64.deb' to '/srv/rebuilderd/tmp/tmpixmo1cr4/libxau6_1.0.11-1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgprofng0 riscv64 2.46-2 [722 kB]
Fetched 722 kB in 0s (20.6 MB/s)
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Get:1 http://deb.debian.org/debian unstable/main riscv64 librole-tiny-perl all 2.002004-1 [21.4 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 liblapack-dev riscv64 3.12.1-7+b1 [12.0 MB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 cpp-15-riscv64-linux-gnu riscv64 15.2.0-13 [14.7 MB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libubsan1 riscv64 15.2.0-13 [1178 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libfftw3-single3 riscv64 3.3.10-2+b2 [379 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 tex-common all 6.20 [29.7 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libpcre2-8-0 riscv64 10.46-1+b1 [297 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libamd3 riscv64 1:7.12.2+dfsg-1 [50.3 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 tar riscv64 1.35+dfsg-4 [821 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libxshmfence1 riscv64 1.3.3-1+b1 [11.1 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libio-socket-ssl-perl all 2.098-1 [229 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libice6 riscv64 2:1.1.1-1+b1 [65.5 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 coreutils riscv64 9.7-3 [3036 kB]
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Downloading dependency 365 of 663: libdata-validate-uri-perl:riscv64=0.07-3
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Downloading dependency 485 of 663: libsub-install-perl:riscv64=0.929-1Get:1 http://deb.debian.org/debian unstable/main riscv64 libsub-install-perl all 0.929-1 [10.5 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libreadline-dev riscv64 8.3-4 [407 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 cpp-riscv64-linux-gnu riscv64 4:15.2.0-5 [5320 B]
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dpkg-name: info: moved 'cpp-riscv64-linux-gnu_4%3a15.2.0-5_riscv64.deb' to '/srv/rebuilderd/tmp/tmpw_qw73to/cpp-riscv64-linux-gnu_15.2.0-5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgetopt-long-descriptive-perl all 0.117-1 [29.8 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libtext-unidecode-perl all 1.30-3 [101 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 octave-datatypes riscv64 1.1.8-2 [533 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libclass-xsaccessor-perl riscv64 1.19-4+b4 [36.6 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libstring-license-perl all 0.0.11-1 [34.7 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libtest-exception-perl all 0.43-3 [16.9 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libxml-libxml-perl riscv64 2.0207+dfsg+really+2.0134-7 [315 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libfeature-compat-try-perl all 0.05-1 [10.4 kB]
Fetched 10.4 kB in 0s (197 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpbrgemp2l/libfeature-compat-try-perl_0.05-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 diffstat riscv64 1.68-1 [34.2 kB]
Fetched 34.2 kB in 0s (650 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp03gumvvh/diffstat_1.68-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 fontconfig riscv64 2.17.1-5 [190 kB]
Fetched 190 kB in 0s (8055 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp_ou8xkyr/fontconfig_2.17.1-5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtext-xslate-perl riscv64 3.5.9-2+b2 [174 kB]
Fetched 174 kB in 0s (3028 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpgkm34zpu/libtext-xslate-perl_3.5.9-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 octave-common all 10.3.0-3 [6653 kB]
Fetched 6653 kB in 0s (30.3 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmppxdaq80p/octave-common_10.3.0-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtinfo6 riscv64 6.6+20251231-1 [352 kB]
Fetched 352 kB in 0s (13.0 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpdzbkwjr1/libtinfo6_6.6+20251231-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libyaml-libyaml-perl riscv64 0.904.0+ds-1 [46.6 kB]
Fetched 46.6 kB in 0s (875 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmppzobri0x/libyaml-libyaml-perl_0.904.0+ds-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libberkeleydb-perl riscv64 0.66-2 [118 kB]
Fetched 118 kB in 0s (2028 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpoefc2fep/libberkeleydb-perl_0.66-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcb-keysyms1 riscv64 0.4.1-1+b1 [16.8 kB]
Fetched 16.8 kB in 0s (857 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpvmh7nrkc/libxcb-keysyms1_0.4.1-1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpangocairo-1.0-0 riscv64 1.57.0-1 [35.1 kB]
Fetched 35.1 kB in 0s (665 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpt4d4ilu5/libpangocairo-1.0-0_1.57.0-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libexporter-lite-perl all 0.09-2 [10.7 kB]
Fetched 10.7 kB in 0s (205 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpj8wn19tg/libexporter-lite-perl_0.09-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libio-html-perl all 1.004-3 [16.2 kB]
Fetched 16.2 kB in 0s (310 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmproc6yywf/libio-html-perl_1.004-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 cme all 1.044-2 [72.5 kB]
Fetched 72.5 kB in 0s (1350 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmppggvg_9f/cme_1.044-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libvorbisenc2 riscv64 1.3.7-3+b1 [70.0 kB]
Fetched 70.0 kB in 0s (3321 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpfe4q5af9/libvorbisenc2_1.3.7-3+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libhtml-tagset-perl all 3.24-1 [14.7 kB]
Fetched 14.7 kB in 0s (282 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpte632jv8/libhtml-tagset-perl_3.24-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libstrictures-perl all 2.000006-1 [18.6 kB]
Fetched 18.6 kB in 0s (354 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0ko0qh3y/libstrictures-perl_2.000006-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 util-linux riscv64 2.41.3-4 [1159 kB]
Fetched 1159 kB in 0s (26.2 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpdzdia3h7/util-linux_2.41.3-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libppix-utils-perl all 0.003-2 [28.0 kB]
Fetched 28.0 kB in 0s (533 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpwn0rl8hv/libppix-utils-perl_0.003-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libjbig0 riscv64 2.1-6.1+b3 [32.7 kB]
Fetched 32.7 kB in 0s (623 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmparntnxzu/libjbig0_2.1-6.1+b3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libclone-choose-perl all 0.010-2 [8676 B]
Fetched 8676 B in 0s (163 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpu2i5e1xc/libclone-choose-perl_0.010-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libconfig-model-dpkg-perl all 3.017 [192 kB]
Fetched 192 kB in 0s (3407 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpx2iaz6h3/libconfig-model-dpkg-perl_3.017_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsasl2-modules-db riscv64 2.1.28+dfsg1-10 [20.2 kB]
Fetched 20.2 kB in 0s (1029 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp_3k1zyru/libsasl2-modules-db_2.1.28+dfsg1-10_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libldap2 riscv64 2.6.10+dfsg-1+b1 [196 kB]
Fetched 196 kB in 0s (8220 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpycafaoay/libldap2_2.6.10+dfsg-1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libcxsparse4 riscv64 1:7.12.2+dfsg-1 [90.4 kB]
Fetched 90.4 kB in 0s (1661 kB/s)
dpkg-name: info: moved 'libcxsparse4_1%3a7.12.2+dfsg-1_riscv64.deb' to '/srv/rebuilderd/tmp/tmpww9r9t90/libcxsparse4_7.12.2+dfsg-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libcap-ng0 riscv64 0.9.1-1 [17.0 kB]
Fetched 17.0 kB in 0s (859 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmph98ez709/libcap-ng0_0.9.1-1_riscv64.deb'
Get:1 http://snapshot.debian.org/archive/debian/20260223T142455Z sid/main riscv64 libllvm21 riscv64 1:21.1.8-3 [27.7 MB]
Fetched 27.7 MB in 1s (40.2 MB/s)
dpkg-name: info: moved 'libllvm21_1%3a21.1.8-3_riscv64.deb' to '/srv/rebuilderd/tmp/tmp9xxidu9x/libllvm21_21.1.8-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 gettext-base riscv64 0.23.2-1 [244 kB]
Fetched 244 kB in 0s (9746 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpydcvqqh_/gettext-base_0.23.2-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 perl-modules-5.40 all 5.40.1-7 [3012 kB]
Fetched 3012 kB in 0s (36.7 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp6xyr_r84/perl-modules-5.40_5.40.1-7_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libavif16 riscv64 1.3.0-1+b2 [138 kB]
Fetched 138 kB in 0s (2495 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmph1sqynbe/libavif16_1.3.0-1+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 base-passwd riscv64 3.6.8 [54.8 kB]
Fetched 54.8 kB in 0s (2650 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp7cxro1zb/base-passwd_3.6.8_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libtext-markdown-discount-perl riscv64 0.18-1 [13.0 kB]
Fetched 13.0 kB in 0s (244 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp172lo00t/libtext-markdown-discount-perl_0.18-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libinput10 riscv64 1.31.0-1 [158 kB]
Fetched 158 kB in 0s (6904 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp4pjeswgc/libinput10_1.31.0-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libccolamd3 riscv64 1:7.12.2+dfsg-1 [47.5 kB]
Fetched 47.5 kB in 0s (894 kB/s)
dpkg-name: info: moved 'libccolamd3_1%3a7.12.2+dfsg-1_riscv64.deb' to '/srv/rebuilderd/tmp/tmpe791jxqw/libccolamd3_7.12.2+dfsg-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcb-randr0 riscv64 1.17.0-2+b2 [117 kB]
Fetched 117 kB in 0s (5272 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp8ln8qwji/libxcb-randr0_1.17.0-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxdmcp-dev riscv64 1:1.1.5-2 [54.4 kB]
Fetched 54.4 kB in 0s (2653 kB/s)
dpkg-name: info: moved 'libxdmcp-dev_1%3a1.1.5-2_riscv64.deb' to '/srv/rebuilderd/tmp/tmpdgl_matk/libxdmcp-dev_1.1.5-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libcpanel-json-xs-perl riscv64 4.40-1 [134 kB]
Fetched 134 kB in 0s (2423 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp9lmgpghi/libcpanel-json-xs-perl_4.40-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liblzo2-2 riscv64 2.10-3+b2 [61.3 kB]
Fetched 61.3 kB in 0s (1148 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp91dwgpv_/liblzo2-2_2.10-3+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libbz2-1.0 riscv64 1.0.8-6+b1 [40.1 kB]
Fetched 40.1 kB in 0s (1951 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpti7dg2vt/libbz2-1.0_1.0.8-6+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libdata-validate-domain-perl all 0.15-1 [11.9 kB]
Fetched 11.9 kB in 0s (227 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpn_fxilj4/libdata-validate-domain-perl_0.15-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 debconf all 1.5.92 [123 kB]
Fetched 123 kB in 0s (5518 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpgs8ddbry/debconf_1.5.92_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpcre2-16-0 riscv64 10.46-1+b1 [276 kB]
Fetched 276 kB in 0s (10.8 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp5wzvll19/libpcre2-16-0_10.46-1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 lintian all 2.130.0 [1068 kB]
Fetched 1068 kB in 0s (12.7 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpidzl7c7v/lintian_2.130.0_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 cpp-15 riscv64 15.2.0-13 [1272 B]
Fetched 1272 B in 0s (24.1 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpdaqpktyv/cpp-15_15.2.0-13_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libnghttp3-9 riscv64 1.12.0-1 [68.4 kB]
Fetched 68.4 kB in 0s (3248 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpguegp1dw/libnghttp3-9_1.12.0-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libclass-inspector-perl all 1.36-3 [17.5 kB]
Fetched 17.5 kB in 0s (334 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpvdst3hvg/libclass-inspector-perl_1.36-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libglx-dev riscv64 1.7.0-3 [15.8 kB]
Fetched 15.8 kB in 0s (800 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpoeffirjs/libglx-dev_1.7.0-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libhttp-negotiate-perl all 6.01-2 [13.1 kB]
Fetched 13.1 kB in 0s (250 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp3lv7mu80/libhttp-negotiate-perl_6.01-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libssl-dev riscv64 3.5.5-1 [6304 kB]
Fetched 6304 kB in 0s (42.0 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmptnp3cu79/libssl-dev_3.5.5-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 librav1e0.8 riscv64 0.8.1-7 [655 kB]
Fetched 655 kB in 0s (9904 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpn41pnqgw/librav1e0.8_0.8.1-7_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 librtmp1 riscv64 2.4+20151223.gitfa8646d.1-3+b1 [59.2 kB]
Fetched 59.2 kB in 0s (2879 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp5uvf40tb/librtmp1_2.4+20151223.gitfa8646d.1-3+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libzstd-dev riscv64 1.5.7+dfsg-3+b1 [1653 kB]
Fetched 1653 kB in 0s (30.4 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp1c6ui2iu/libzstd-dev_1.5.7+dfsg-3+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libdata-optlist-perl all 0.114-1 [10.6 kB]
Fetched 10.6 kB in 0s (202 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpwthkm7oi/libdata-optlist-perl_0.114-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 xkb-data all 2.46-2 [830 kB]
Fetched 830 kB in 0s (22.4 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpa7u9l7_6/xkb-data_2.46-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 gfortran-15 riscv64 15.2.0-13 [18.5 kB]
Fetched 18.5 kB in 0s (353 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0nrrm7jb/gfortran-15_15.2.0-13_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfreetype6 riscv64 2.14.1+dfsg-2 [504 kB]
Fetched 504 kB in 0s (7780 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpjp21vsiy/libfreetype6_2.14.1+dfsg-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libhttp-cookies-perl all 6.11-1 [19.1 kB]
Fetched 19.1 kB in 0s (365 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpaz5optwk/libhttp-cookies-perl_6.11-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmount1 riscv64 2.41.3-4 [227 kB]
Fetched 227 kB in 0s (9300 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpcj9awdfp/libmount1_2.41.3-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libportaudio2 riscv64 19.7.0-1 [62.7 kB]
Fetched 62.7 kB in 0s (1168 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0ato6udx/libportaudio2_19.7.0-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 openssl-provider-legacy riscv64 3.5.5-1 [313 kB]
Fetched 313 kB in 0s (12.0 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmps6fi0sdz/openssl-provider-legacy_3.5.5-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpam-runtime all 1.7.0-5 [249 kB]
Fetched 249 kB in 0s (9994 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpx95m_ydj/libpam-runtime_1.7.0-5_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 x11proto-dev all 2025.1-1 [605 kB]
Fetched 605 kB in 0s (9049 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpzyi8igt9/x11proto-dev_2025.1-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxkbcommon-x11-0 riscv64 1.13.1-1 [19.9 kB]
Fetched 19.9 kB in 0s (1005 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp9mvbqt88/libxkbcommon-x11-0_1.13.1-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libkeyutils1 riscv64 1.6.3-6+b1 [9672 B]
Fetched 9672 B in 0s (491 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpc636trhj/libkeyutils1_1.6.3-6+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libthai-data all 0.1.30-1 [172 kB]
Fetched 172 kB in 0s (3079 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpg0lvvbgv/libthai-data_0.1.30-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfile-stripnondeterminism-perl all 1.15.0-1 [19.9 kB]
Fetched 19.9 kB in 0s (1007 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpffb3gtv9/libfile-stripnondeterminism-perl_1.15.0-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgl-dev riscv64 1.7.0-3 [101 kB]
Fetched 101 kB in 0s (4641 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpz4452zge/libgl-dev_1.7.0-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfftw3-dev riscv64 3.3.10-2+b2 [1479 kB]
Fetched 1479 kB in 0s (12.4 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp7r81e60o/libfftw3-dev_3.3.10-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgl1 riscv64 1.7.0-3 [113 kB]
Fetched 113 kB in 0s (5169 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpid3wlnvn/libgl1_1.7.0-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libperlio-utf8-strict-perl riscv64 0.010-1+b3 [11.4 kB]
Fetched 11.4 kB in 0s (218 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmponb_r_ni/libperlio-utf8-strict-perl_0.010-1+b3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libhtml-tokeparser-simple-perl all 3.16-4 [39.1 kB]
Fetched 39.1 kB in 0s (734 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpxl0guds0/libhtml-tokeparser-simple-perl_3.16-4_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libnet-ipv6addr-perl all 1.02-1 [21.7 kB]
Fetched 21.7 kB in 0s (415 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmprfxl75ax/libnet-ipv6addr-perl_1.02-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libio-stringy-perl all 2.113-2 [48.3 kB]
Fetched 48.3 kB in 0s (910 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpg0kwareo/libio-stringy-perl_2.113-2_all.deb'
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libfltk1.3t64 riscv64 1.3.11-3 [551 kB]
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Get:1 http://snapshot.debian.org/archive/debian/20260223T142455Z sid/main riscv64 dh-autoreconf all 21+nmu1 [11.7 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libc-bin riscv64 2.42-13 [610 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 automake all 1:1.18.1-3 [878 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libimagequant0 riscv64 4.4.1-1+b1 [259 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libclone-perl riscv64 0.47-1+b2 [13.7 kB]
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Get:1 http://deb.debian.org/debian unstable/main riscv64 libppix-regexp-perl all 0.091-1 [248 kB]
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Downloading dependency 486 of 663: libreadline-dev:riscv64=8.3-4
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Downloading dependency 603 of 663: libhtml-html5-entities-perl:riscv64=0.004-3
Downloading dependency 604 of 663: libindirect-perl:riscv64=0.39-2+b3Get:1 http://deb.debian.org/debian unstable/main riscv64 libindirect-perl riscv64 0.39-2+b3 [27.1 kB]
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dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp257ivxns/libindirect-perl_0.39-2+b3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxs-parse-keyword-perl riscv64 0.49-1 [65.7 kB]
Fetched 65.7 kB in 0s (1222 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpywtdvh_o/libxs-parse-keyword-perl_0.49-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 readline-common all 8.3-4 [74.8 kB]
Fetched 74.8 kB in 0s (3556 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp5pvhuhcp/readline-common_8.3-4_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfribidi0 riscv64 1.0.16-5 [27.8 kB]
Fetched 27.8 kB in 0s (1386 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp8zm29wnt/libfribidi0_1.0.16-5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 g++-15 riscv64 15.2.0-13 [26.0 kB]
Fetched 26.0 kB in 0s (1284 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpbcfd9urw/g++-15_15.2.0-13_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libaliased-perl all 0.34-3 [13.5 kB]
Fetched 13.5 kB in 0s (254 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpe63ifc8v/libaliased-perl_0.34-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libssh2-1-dev riscv64 1.11.1-1+b1 [593 kB]
Fetched 593 kB in 0s (18.3 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp69h5imjj/libssh2-1-dev_1.11.1-1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 autotools-dev all 20240727.1 [60.2 kB]
Fetched 60.2 kB in 0s (2902 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpx0_d5_nd/autotools-dev_20240727.1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libcurl4-openssl-dev riscv64 8.19.0~rc2-2 [1337 kB]
Fetched 1337 kB in 0s (15.9 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpztmtp0il/libcurl4-openssl-dev_8.19.0~rc2-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxml2-16 riscv64 2.15.1+dfsg-2+b1 [637 kB]
Fetched 637 kB in 0s (19.1 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp7aum3edo/libxml2-16_2.15.1+dfsg-2+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 fontconfig-config riscv64 2.17.1-5 [56.0 kB]
Fetched 56.0 kB in 0s (2694 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpz8kxjw7t/fontconfig-config_2.17.1-5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libdata-validate-ip-perl all 0.31-1 [20.6 kB]
Fetched 20.6 kB in 0s (1037 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpof_6vq8t/libdata-validate-ip-perl_0.31-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libts0t64 riscv64 1.22-1.1+b2 [62.0 kB]
Fetched 62.0 kB in 0s (2968 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpfoa1ogvp/libts0t64_1.22-1.1+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcb-sync1 riscv64 1.17.0-2+b2 [109 kB]
Fetched 109 kB in 0s (4354 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp9_p8lpcf/libxcb-sync1_1.17.0-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmro-compat-perl all 0.15-2 [11.8 kB]
Fetched 11.8 kB in 0s (575 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp7qxdebfk/libmro-compat-perl_0.15-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgnutls28-dev riscv64 3.8.12-3 [2880 kB]
Fetched 2880 kB in 0s (24.8 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp0q5ponap/libgnutls28-dev_3.8.12-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libb2-1 riscv64 0.98.1-1.1+b3 [20.3 kB]
Fetched 20.3 kB in 0s (1020 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp7wgmhwn_/libb2-1_0.98.1-1.1+b3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libevent-2.1-7t64 riscv64 2.1.12-stable-10+b2 [183 kB]
Fetched 183 kB in 0s (7715 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp1rm4zoqd/libevent-2.1-7t64_2.1.12-stable-10+b2_riscv64.deb'
Get:1 http://snapshot.debian.org/archive/debian/20260223T142455Z sid/main riscv64 libnghttp2-14 riscv64 1.68.0-1 [79.5 kB]
Fetched 79.5 kB in 0s (3743 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpg2xyd88k/libnghttp2-14_1.68.0-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libstring-format-perl all 1.18-1 [9408 B]
Fetched 9408 B in 0s (180 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp3gl87vlo/libstring-format-perl_1.18-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libvorbis0a riscv64 1.3.7-3+b1 [89.8 kB]
Fetched 89.8 kB in 0s (4004 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpjemlshb7/libvorbis0a_1.3.7-3+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libopengl0 riscv64 1.7.0-3 [41.0 kB]
Fetched 41.0 kB in 0s (2026 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpbvc2yi1_/libopengl0_1.7.0-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsub-exporter-perl all 0.990-1 [50.6 kB]
Fetched 50.6 kB in 0s (951 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp8fauohs1/libsub-exporter-perl_0.990-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 binutils-common riscv64 2.46-2 [2635 kB]
Fetched 2635 kB in 0s (23.6 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpj82hbtwu/binutils-common_2.46-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgomp1 riscv64 15.2.0-13 [131 kB]
Fetched 131 kB in 0s (5912 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpdl6ale12/libgomp1_15.2.0-13_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libabsl20240722 riscv64 20240722.0-4 [494 kB]
Fetched 494 kB in 0s (16.5 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp4b4nuggz/libabsl20240722_20240722.0-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libfile-homedir-perl all 1.006-2 [42.4 kB]
Fetched 42.4 kB in 0s (802 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpwv6my0c0/libfile-homedir-perl_1.006-2_all.deb'
Get:1 http://snapshot.debian.org/archive/debian/20260223T142455Z sid/main riscv64 libudev1 riscv64 259.1-1 [159 kB]
Fetched 159 kB in 0s (6935 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp5hk0ebti/libudev1_259.1-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libcurl4t64 riscv64 8.19.0~rc2-2 [420 kB]
Fetched 420 kB in 0s (14.5 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp7ejpitkh/libcurl4t64_8.19.0~rc2-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgfortran-15-dev riscv64 15.2.0-13 [1311 kB]
Fetched 1311 kB in 0s (16.3 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpl112vaon/libgfortran-15-dev_15.2.0-13_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 linux-libc-dev all 6.18.12-1 [2574 kB]
Fetched 2574 kB in 0s (35.0 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmppcgojsug/linux-libc-dev_6.18.12-1_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsub-uplevel-perl all 0.2800-3 [14.0 kB]
Fetched 14.0 kB in 0s (265 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpze7me573/libsub-uplevel-perl_0.2800-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libopus0 riscv64 1.6.1-1 [3447 kB]
Fetched 3447 kB in 0s (38.1 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp5vjd7kep/libopus0_1.6.1-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libqt6dbus6 riscv64 6.9.2+dfsg-4 [271 kB]
Fetched 271 kB in 0s (10.6 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpy109m8jf/libqt6dbus6_6.9.2+dfsg-4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libterm-readkey-perl riscv64 2.38-2+b3 [24.8 kB]
Fetched 24.8 kB in 0s (471 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp4ex02h4t/libterm-readkey-perl_2.38-2+b3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 gfortran-riscv64-linux-gnu riscv64 4:15.2.0-5 [1284 B]
Fetched 1284 B in 0s (24.6 kB/s)
dpkg-name: info: moved 'gfortran-riscv64-linux-gnu_4%3a15.2.0-5_riscv64.deb' to '/srv/rebuilderd/tmp/tmpcq_gqpp0/gfortran-riscv64-linux-gnu_15.2.0-5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libk5crypto3 riscv64 1.22.1-2 [98.4 kB]
Fetched 98.4 kB in 0s (4544 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmprhplnckc/libk5crypto3_1.22.1-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libctf-nobfd0 riscv64 2.46-2 [163 kB]
Fetched 163 kB in 0s (7007 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp6v9h5yyw/libctf-nobfd0_2.46-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgav1-2 riscv64 0.20.0-2 [344 kB]
Fetched 344 kB in 0s (5798 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp_v_axvbo/libgav1-2_0.20.0-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 gpg riscv64 2.4.8-5 [631 kB]
Fetched 631 kB in 0s (9622 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp8rnq5ypz/gpg_2.4.8-5_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libcurl3t64-gnutls riscv64 8.19.0~rc2-2 [412 kB]
Fetched 412 kB in 0s (14.5 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp9bpq_uns/libcurl3t64-gnutls_8.19.0~rc2-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 liblerc4 riscv64 4.0.0+ds-5+b1 [177 kB]
Fetched 177 kB in 0s (3091 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmprvz2apy9/liblerc4_4.0.0+ds-5+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpod-constants-perl all 0.19-2 [17.3 kB]
Fetched 17.3 kB in 0s (330 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpmkvv8om6/libpod-constants-perl_0.19-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libevdev2 riscv64 1.13.6+dfsg-1 [29.9 kB]
Fetched 29.9 kB in 0s (1491 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp3qev0e9u/libevdev2_1.13.6+dfsg-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libdevel-size-perl riscv64 0.86-1 [24.2 kB]
Fetched 24.2 kB in 0s (460 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpm2qem_km/libdevel-size-perl_0.86-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libavahi-client3 riscv64 0.8-18 [48.4 kB]
Fetched 48.4 kB in 0s (2046 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpzpxo7lph/libavahi-client3_0.8-18_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpath-iterator-rule-perl all 1.015-2 [41.7 kB]
Fetched 41.7 kB in 0s (787 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmpraaqbcyh/libpath-iterator-rule-perl_1.015-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libkrb5-3 riscv64 1.22.1-2 [346 kB]
Fetched 346 kB in 0s (5744 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmps9b5nkiz/libkrb5-3_1.22.1-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libpod-pom-perl all 2.01-4 [65.0 kB]
Fetched 65.0 kB in 0s (1214 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp9gob1y5z/libpod-pom-perl_2.01-4_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsz2 riscv64 1.1.5-1 [19.2 kB]
Fetched 19.2 kB in 0s (964 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp8ur8gamn/libsz2_1.1.5-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 bash riscv64 5.3-2 [1561 kB]
Fetched 1561 kB in 0s (29.8 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp61cleagy/bash_5.3-2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 octave-dev riscv64 10.3.0-3 [1068 kB]
Fetched 1068 kB in 0s (14.3 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmphwccw861/octave-dev_10.3.0-3_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libgdbm-compat4t64 riscv64 1.26-1+b1 [53.1 kB]
Fetched 53.1 kB in 0s (2450 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp3r2kfwn7/libgdbm-compat4t64_1.26-1+b1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsereal-decoder-perl riscv64 5.004+ds-1+b4 [104 kB]
Fetched 104 kB in 0s (1907 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp_qxnm505/libsereal-decoder-perl_5.004+ds-1+b4_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libssl3t64 riscv64 3.5.5-1 [2211 kB]
Fetched 2211 kB in 0s (33.5 MB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmptbp9t0f9/libssl3t64_3.5.5-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libmousex-nativetraits-perl all 1.09-3 [53.5 kB]
Fetched 53.5 kB in 0s (1004 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp4vfsk5_f/libmousex-nativetraits-perl_1.09-3_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libxcb-xfixes0 riscv64 1.17.0-2+b2 [109 kB]
Fetched 109 kB in 0s (5020 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp7pvg__xa/libxcb-xfixes0_1.17.0-2+b2_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libumfpack6 riscv64 1:7.12.2+dfsg-1 [262 kB]
Fetched 262 kB in 0s (4531 kB/s)
dpkg-name: info: moved 'libumfpack6_1%3a7.12.2+dfsg-1_riscv64.deb' to '/srv/rebuilderd/tmp/tmplxfv2iup/libumfpack6_7.12.2+dfsg-1_riscv64.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libsensors-config all 1:3.6.2-2 [16.2 kB]
Fetched 16.2 kB in 0s (826 kB/s)
dpkg-name: info: moved 'libsensors-config_1%3a3.6.2-2_all.deb' to '/srv/rebuilderd/tmp/tmp7defk44m/libsensors-config_3.6.2-2_all.deb'
Get:1 http://deb.debian.org/debian unstable/main riscv64 libdebconfclient0 riscv64 0.282+b2 [11.0 kB]
Fetched 11.0 kB in 0s (192 kB/s)
dpkg-name: warning: skipping '/srv/rebuilderd/tmp/tmp9vj4iyrg/libdebconfclient0_0.282+b2_riscv64.deb'
dpkg-buildpackage: info: source package debootsnap-dummy
dpkg-buildpackage: info: source version 1.0
dpkg-buildpackage: info: source distribution unstable
dpkg-buildpackage: info: source changed by Equivs Dummy Package Generator <rebuilderd@riscv64-33.debian.net>
 dpkg-source --before-build .
dpkg-buildpackage: info: host architecture riscv64
 debian/rules clean
dh clean
   dh_clean
 debian/rules binary
dh binary
   dh_update_autotools_config
   dh_autoreconf
   create-stamp debian/debhelper-build-stamp
   dh_prep
   dh_auto_install --destdir=debian/debootsnap-dummy/
   dh_install
   dh_installdocs
   dh_installchangelogs
   dh_perl
   dh_link
   dh_strip_nondeterminism
   dh_compress
   dh_fixperms
   dh_missing
   dh_installdeb
   dh_gencontrol
   dh_md5sums
   dh_builddeb
dpkg-deb: building package 'debootsnap-dummy' in '../debootsnap-dummy_1.0_all.deb'.
 dpkg-genbuildinfo --build=binary -O../debootsnap-dummy_1.0_riscv64.buildinfo
 dpkg-genchanges --build=binary -O../debootsnap-dummy_1.0_riscv64.changes
dpkg-genchanges: info: binary-only upload (no source code included)
 dpkg-source --after-build .
dpkg-buildpackage: info: binary-only upload (no source included)

The package has been created.
Attention, the package has been created in the /srv/rebuilderd/tmp/tmptplsigku/cache directory,
not in ".." as indicated by the message above!
I: automatically chosen mode: unshare
I: chroot architecture riscv64 is equal to the host's architecture
I: using /srv/rebuilderd/tmp/mmdebstrap.xnwdmcGr71 as tempdir
I: running --setup-hook directly: /usr/share/mmdebstrap/hooks/maybe-merged-usr/setup00.sh /srv/rebuilderd/tmp/mmdebstrap.xnwdmcGr71
127.0.0.1 - - [27/Feb/2026 07:23:06] code 404, message File not found
127.0.0.1 - - [27/Feb/2026 07:23:06] "GET /./InRelease HTTP/1.1" 404 -
Ign:1 http://localhost:42467 ./ InRelease
127.0.0.1 - - [27/Feb/2026 07:23:06] "GET /./Release HTTP/1.1" 200 -
Get:2 http://localhost:42467 ./ Release [462 B]
127.0.0.1 - - [27/Feb/2026 07:23:06] code 404, message File not found
127.0.0.1 - - [27/Feb/2026 07:23:06] "GET /./Release.gpg HTTP/1.1" 404 -
Ign:3 http://localhost:42467 ./ Release.gpg
127.0.0.1 - - [27/Feb/2026 07:23:06] "GET /./Packages HTTP/1.1" 200 -
Get:4 http://localhost:42467 ./ Packages [822 kB]
Fetched 823 kB in 0s (11.9 MB/s)
Reading package lists...
usr-is-merged found but not real -- not running merged-usr setup hook
I: skipping apt-get update because it was already run
I: downloading packages with apt...
127.0.0.1 - - [27/Feb/2026 07:23:06] "GET /./gcc-15-base_15.2.0-13_riscv64.deb HTTP/1.1" 200 -
127.0.0.1 - - [27/Feb/2026 07:23:06] "GET /./libc-gconv-modules-extra_2.42-13_riscv64.deb HTTP/1.1" 200 -
127.0.0.1 - - [27/Feb/2026 07:23:06] "GET /./libc6_2.42-13_riscv64.deb HTTP/1.1" 200 -
127.0.0.1 - - [27/Feb/2026 07:23:06] "GET /./libgcc-s1_15.2.0-13_riscv64.deb HTTP/1.1" 200 -
127.0.0.1 - - [27/Feb/2026 07:23:06] "GET /./mawk_1.3.4.20260129-1_riscv64.deb HTTP/1.1" 200 -
127.0.0.1 - - [27/Feb/2026 07:23:06] "GET /./base-files_14_riscv64.deb HTTP/1.1" 200 -
127.0.0.1 - - [27/Feb/2026 07:23:06] "GET /./libtinfo6_6.6%2b20251231-1_riscv64.deb HTTP/1.1" 200 -
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127.0.0.1 - - [27/Feb/2026 07:23:07] "GET /./util-linux_2.41.3-4_riscv64.deb HTTP/1.1" 200 -
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127.0.0.1 - - [27/Feb/2026 07:23:07] "GET /./init-system-helpers_1.69_all.deb HTTP/1.1" 200 -
127.0.0.1 - - [27/Feb/2026 07:23:07] "GET /./libc-bin_2.42-13_riscv64.deb HTTP/1.1" 200 -
127.0.0.1 - - [27/Feb/2026 07:23:07] "GET /./ncurses-base_6.6%2b20251231-1_all.deb HTTP/1.1" 200 -
127.0.0.1 - - [27/Feb/2026 07:23:07] "GET /./sysvinit-utils_3.15-6_riscv64.deb HTTP/1.1" 200 -
I: extracting archives...
I: running --extract-hook directly: /usr/share/mmdebstrap/hooks/maybe-merged-usr/extract00.sh /srv/rebuilderd/tmp/mmdebstrap.xnwdmcGr71
127.0.0.1 - - [27/Feb/2026 07:23:10] code 404, message File not found
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Ign:1 http://localhost:42467 ./ InRelease
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Hit:2 http://localhost:42467 ./ Release
127.0.0.1 - - [27/Feb/2026 07:23:10] code 404, message File not found
127.0.0.1 - - [27/Feb/2026 07:23:10] "GET /./Release.gpg HTTP/1.1" 404 -
Ign:3 http://localhost:42467 ./ Release.gpg
Reading package lists...
usr-is-merged found but not real -- not running merged-usr extract hook
I: installing essential packages...
I: running --essential-hook directly: /usr/share/mmdebstrap/hooks/maybe-merged-usr/essential00.sh /srv/rebuilderd/tmp/mmdebstrap.xnwdmcGr71
usr-is-merged was not installed in a previous hook -- not running merged-usr essential hook
I: installing remaining packages inside the chroot...
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127.0.0.1 - - [27/Feb/2026 07:23:21] "GET /./netbase_6.5_all.deb HTTP/1.1" 200 -
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127.0.0.1 - - [27/Feb/2026 07:23:21] "GET /./libkeyutils1_1.6.3-6%2bb1_riscv64.deb HTTP/1.1" 200 -
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I: running --customize-hook directly: /srv/rebuilderd/tmp/tmptplsigku/apt_install.sh /srv/rebuilderd/tmp/mmdebstrap.xnwdmcGr71
Reading package lists...
Building dependency tree...
Reading state information...
libregexp-common-perl is already the newest version (2024080801-1).
libregexp-common-perl set to manually installed.
libxfixes3 is already the newest version (1:6.0.0-2+b5).
libxfixes3 set to manually installed.
gcc-riscv64-linux-gnu is already the newest version (4:15.2.0-5).
gcc-riscv64-linux-gnu set to manually installed.
libpackage-stash-perl is already the newest version (0.40-1).
libpackage-stash-perl set to manually installed.
libselinux1 is already the newest version (3.9-4+b1).
libipc-system-simple-perl is already the newest version (1.30-2).
libipc-system-simple-perl set to manually installed.
libncurses-dev is already the newest version (6.6+20251231-1).
libncurses-dev set to manually installed.
po-debconf is already the newest version (1.0.22).
po-debconf set to manually installed.
gfortran-15-riscv64-linux-gnu is already the newest version (15.2.0-13).
gfortran-15-riscv64-linux-gnu set to manually installed.
libdevel-callchecker-perl is already the newest version (0.009-3).
libdevel-callchecker-perl set to manually installed.
libde265-0 is already the newest version (1.0.16-1+b1).
libde265-0 set to manually installed.
libsoftware-licensemoreutils-perl is already the newest version (1.009-1).
libsoftware-licensemoreutils-perl set to manually installed.
libwww-perl is already the newest version (6.81-1).
libwww-perl set to manually installed.
mawk is already the newest version (1.3.4.20260129-1).
gzip is already the newest version (1.13-1).
libqt6opengl6 is already the newest version (6.9.2+dfsg-4).
libqt6opengl6 set to manually installed.
libxml-sax-base-perl is already the newest version (1.09-3).
libxml-sax-base-perl set to manually installed.
libsframe3 is already the newest version (2.46-2).
libsframe3 set to manually installed.
libclass-method-modifiers-perl is already the newest version (2.15-1).
libclass-method-modifiers-perl set to manually installed.
libqt6gui6 is already the newest version (6.9.2+dfsg-4).
libqt6gui6 set to manually installed.
libnet-smtp-ssl-perl is already the newest version (1.04-2).
libnet-smtp-ssl-perl set to manually installed.
libio-string-perl is already the newest version (1.08-4).
libio-string-perl set to manually installed.
autoconf is already the newest version (2.72-3.1).
autoconf set to manually installed.
libgdbm6t64 is already the newest version (1.26-1+b1).
libgdbm6t64 set to manually installed.
libnet-netmask-perl is already the newest version (2.0003-1).
libnet-netmask-perl set to manually installed.
libtime-duration-perl is already the newest version (1.21-2).
libtime-duration-perl set to manually installed.
libc6-dev is already the newest version (2.42-13).
libc6-dev set to manually installed.
file is already the newest version (1:5.46-5+b1).
file set to manually installed.
libnet-ssleay-perl is already the newest version (1.94-3+b1).
libnet-ssleay-perl set to manually installed.
libsharpyuv0 is already the newest version (1.5.0-0.1+b1).
libsharpyuv0 set to manually installed.
libsensors5 is already the newest version (1:3.6.2-2+b1).
libsensors5 set to manually installed.
bzip2 is already the newest version (1.0.8-6+b1).
bzip2 set to manually installed.
libjack-jackd2-0 is already the newest version (1.9.22~dfsg-5+b1).
libjack-jackd2-0 set to manually installed.
libtext-charwidth-perl is already the newest version (0.04-11+b5).
libtext-charwidth-perl set to manually installed.
libxcb-glx0 is already the newest version (1.17.0-2+b2).
libxcb-glx0 set to manually installed.
libgl2ps1.4 is already the newest version (1.4.2+dfsg1-4).
libgl2ps1.4 set to manually installed.
libgmpxx4ldbl is already the newest version (2:6.3.0+dfsg-5+b1).
libgmpxx4ldbl set to manually installed.
libbsd0 is already the newest version (0.12.2-2+b1).
libbsd0 set to manually installed.
libfile-find-rule-perl is already the newest version (0.35-1).
libfile-find-rule-perl set to manually installed.
libgudev-1.0-0 is already the newest version (238-7+b1).
libgudev-1.0-0 set to manually installed.
libtime-moment-perl is already the newest version (0.46-1).
libtime-moment-perl set to manually installed.
libwww-mechanize-perl is already the newest version (2.20-1).
libwww-mechanize-perl set to manually installed.
libpixman-1-0 is already the newest version (0.46.4-1+b1).
libpixman-1-0 set to manually installed.
libfftw3-double3 is already the newest version (3.3.10-2+b2).
libfftw3-double3 set to manually installed.
libxpm4 is already the newest version (1:3.5.17-1+b4).
libxpm4 set to manually installed.
libpkgconf7 is already the newest version (2.5.1-4).
libpkgconf7 set to manually installed.
libbrotli-dev is already the newest version (1.2.0-3).
libbrotli-dev set to manually installed.
gnuplot-data is already the newest version (6.0.3+dfsg1-1).
gnuplot-data set to manually installed.
x11-common is already the newest version (1:7.7+26).
x11-common set to manually installed.
libjpeg62-turbo is already the newest version (1:2.1.5-4).
libjpeg62-turbo set to manually installed.
libsvtav1enc2 is already the newest version (2.3.0+dfsg-1).
libsvtav1enc2 set to manually installed.
liblist-moreutils-xs-perl is already the newest version (0.430-4+b2).
liblist-moreutils-xs-perl set to manually installed.
t1utils is already the newest version (1.41-4+b1).
t1utils set to manually installed.
libxrender1 is already the newest version (1:0.9.12-1+b1).
libxrender1 set to manually installed.
xorg-sgml-doctools is already the newest version (1:1.11-1.1).
xorg-sgml-doctools set to manually installed.
libnetaddr-ip-perl is already the newest version (4.079+dfsg-2+b4).
libnetaddr-ip-perl set to manually installed.
fonts-freefont-otf is already the newest version (20211204+svn4273-4).
fonts-freefont-otf set to manually installed.
libapt-pkg7.0 is already the newest version (3.1.16).
libapt-pkg7.0 set to manually installed.
libcc1-0 is already the newest version (15.2.0-13).
libcc1-0 set to manually installed.
libfontconfig1 is already the newest version (2.17.1-5).
libfontconfig1 set to manually installed.
libgl1-mesa-dri is already the newest version (26.0.0-1).
libgl1-mesa-dri set to manually installed.
libjpeg-dev is already the newest version (1:2.1.5-4).
libjpeg-dev set to manually installed.
libaudit1 is already the newest version (1:4.1.2-1+b1).
libqt6core5compat6 is already the newest version (6.9.2-3).
libqt6core5compat6 set to manually installed.
libdb5.3t64 is already the newest version (5.3.28+dfsg2-11).
pkgconf is already the newest version (2.5.1-4).
pkgconf set to manually installed.
libtext-wrapi18n-perl is already the newest version (0.06-10).
libtext-wrapi18n-perl set to manually installed.
libgssrpc4t64 is already the newest version (1.22.1-2).
libgssrpc4t64 set to manually installed.
libmd0 is already the newest version (1.1.0-2+b2).
libwebpmux3 is already the newest version (1.5.0-0.1+b1).
libwebpmux3 set to manually installed.
dh-octave-autopkgtest is already the newest version (1.14.1).
dh-octave-autopkgtest set to manually installed.
dpkg is already the newest version (1.23.5).
libx11-dev is already the newest version (2:1.8.13-1).
libx11-dev set to manually installed.
libsort-versions-perl is already the newest version (1.62-3).
libsort-versions-perl set to manually installed.
dash is already the newest version (0.5.12-12).
libmpg123-0t64 is already the newest version (1.33.3-2).
libmpg123-0t64 set to manually installed.
libngtcp2-crypto-ossl-dev is already the newest version (1.16.0-1).
libngtcp2-crypto-ossl-dev set to manually installed.
libharfbuzz0b is already the newest version (12.3.2-2).
libharfbuzz0b set to manually installed.
libxcb-shape0 is already the newest version (1.17.0-2+b2).
libxcb-shape0 set to manually installed.
libio-interactive-perl is already the newest version (1.027-1).
libio-interactive-perl set to manually installed.
libjson-perl is already the newest version (4.10000-1).
libjson-perl set to manually installed.
libmoox-aliases-perl is already the newest version (0.001006-3).
libmoox-aliases-perl set to manually installed.
libuchardet0 is already the newest version (0.0.8-2+b1).
libuchardet0 set to manually installed.
debianutils is already the newest version (5.23.2).
libcups2t64 is already the newest version (2.4.16-1).
libcups2t64 set to manually installed.
libnpth0t64 is already the newest version (1.8-3+b1).
libnpth0t64 set to manually installed.
libalgorithm-c3-perl is already the newest version (0.11-2).
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libfyaml0 is already the newest version (0.9.4-1).
libfyaml0 set to manually installed.
libiterator-perl is already the newest version (0.03+ds1-2).
libiterator-perl set to manually installed.
libnettle8t64 is already the newest version (3.10.2-1).
libnettle8t64 set to manually installed.
libfltk-gl1.3t64 is already the newest version (1.3.11-3).
libfltk-gl1.3t64 set to manually installed.
libxcb-dri3-0 is already the newest version (1.17.0-2+b2).
libxcb-dri3-0 set to manually installed.
libpath-tiny-perl is already the newest version (0.148-1).
libpath-tiny-perl set to manually installed.
licensecheck is already the newest version (3.3.9-1).
licensecheck set to manually installed.
libdpkg-perl is already the newest version (1.23.5).
libdpkg-perl set to manually installed.
libdeflate0 is already the newest version (1.23-2+b1).
libdeflate0 set to manually installed.
texinfo is already the newest version (7.2-5).
texinfo set to manually installed.
libxcb-cursor0 is already the newest version (0.1.6-1).
libxcb-cursor0 set to manually installed.
libhdf5-fortran-310 is already the newest version (1.14.6+repack-2).
libhdf5-fortran-310 set to manually installed.
lzop is already the newest version (1.04-2+b1).
lzop set to manually installed.
libsamplerate0 is already the newest version (0.2.2-4+b3).
libsamplerate0 set to manually installed.
libzstd1 is already the newest version (1.5.7+dfsg-3+b1).
groff-base is already the newest version (1.23.0-10).
groff-base set to manually installed.
libasound2t64 is already the newest version (1.2.15.3-1).
libasound2t64 set to manually installed.
libdata-dpath-perl is already the newest version (0.60-1).
libdata-dpath-perl set to manually installed.
libtext-reform-perl is already the newest version (1.20-5).
libtext-reform-perl set to manually installed.
libconfig-inifiles-perl is already the newest version (3.000003-4).
libconfig-inifiles-perl set to manually installed.
libx11-xcb1 is already the newest version (2:1.8.13-1).
libx11-xcb1 set to manually installed.
libtext-levenshteinxs-perl is already the newest version (0.03-5+b3).
libtext-levenshteinxs-perl set to manually installed.
liblist-compare-perl is already the newest version (0.55-2).
liblist-compare-perl set to manually installed.
libqt6core6t64 is already the newest version (6.9.2+dfsg-4).
libqt6core6t64 set to manually installed.
libtiff6 is already the newest version (4.7.1-1).
libtiff6 set to manually installed.
libwacom-common is already the newest version (2.18.0-1).
libwacom-common set to manually installed.
libhtml-form-perl is already the newest version (6.13-1).
libhtml-form-perl set to manually installed.
libhttp-message-perl is already the newest version (7.01-1).
libhttp-message-perl set to manually installed.
libmagic-mgc is already the newest version (1:5.46-5+b1).
libmagic-mgc set to manually installed.
gcc is already the newest version (4:15.2.0-5).
gcc set to manually installed.
zlib1g-dev is already the newest version (1:1.3.dfsg+really1.3.1-3).
zlib1g-dev set to manually installed.
libthai0 is already the newest version (0.1.30-1).
libthai0 set to manually installed.
plzip is already the newest version (1.13~rc1-3).
plzip set to manually installed.
libcgi-pm-perl is already the newest version (4.71-1).
libcgi-pm-perl set to manually installed.
libxmlb2 is already the newest version (0.3.24-2).
libxmlb2 set to manually installed.
libwmflite-0.2-7 is already the newest version (0.2.13-2).
libwmflite-0.2-7 set to manually installed.
libb-keywords-perl is already the newest version (1.29-1).
libb-keywords-perl set to manually installed.
liblwp-mediatypes-perl is already the newest version (6.04-2).
liblwp-mediatypes-perl set to manually installed.
libstring-copyright-perl is already the newest version (0.003014-1).
libstring-copyright-perl set to manually installed.
libparams-classify-perl is already the newest version (0.015-2+b5).
libparams-classify-perl set to manually installed.
libheif-plugin-libde265 is already the newest version (1.21.2-3).
libheif-plugin-libde265 set to manually installed.
liblog-any-adapter-screen-perl is already the newest version (0.141-2).
liblog-any-adapter-screen-perl set to manually installed.
libxcb1 is already the newest version (1.17.0-2+b2).
libxcb1 set to manually installed.
libgcc-15-dev is already the newest version (15.2.0-13).
libgcc-15-dev set to manually installed.
libdrm2 is already the newest version (2.4.131-1).
libdrm2 set to manually installed.
libstring-escape-perl is already the newest version (2010.002-3).
libstring-escape-perl set to manually installed.
libvariable-magic-perl is already the newest version (0.64-1+b1).
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bsdextrautils is already the newest version (2.41.3-4).
bsdextrautils set to manually installed.
libtask-weaken-perl is already the newest version (1.06-2).
libtask-weaken-perl set to manually installed.
libp11-kit0 is already the newest version (0.26.2-2).
libp11-kit0 set to manually installed.
libxcb-xkb1 is already the newest version (1.17.0-2+b2).
libxcb-xkb1 set to manually installed.
perltidy is already the newest version (20250105-1).
perltidy set to manually installed.
libnamespace-clean-perl is already the newest version (0.27-2).
libnamespace-clean-perl set to manually installed.
libwacom9 is already the newest version (2.18.0-1).
libwacom9 set to manually installed.
libmldbm-perl is already the newest version (2.05-4).
libmldbm-perl set to manually installed.
libelf1t64 is already the newest version (0.194-1).
libelf1t64 set to manually installed.
libjxl0.11 is already the newest version (0.11.1-6).
libjxl0.11 set to manually installed.
libhdf5-hl-fortran-310 is already the newest version (1.14.6+repack-2).
libhdf5-hl-fortran-310 set to manually installed.
libbrotli1 is already the newest version (1.2.0-3).
libbrotli1 set to manually installed.
libdrm-common is already the newest version (2.4.131-1).
libdrm-common set to manually installed.
liblua5.4-0 is already the newest version (5.4.8-1+b1).
liblua5.4-0 set to manually installed.
libc-gconv-modules-extra is already the newest version (2.42-13).
libcarp-assert-more-perl is already the newest version (2.9.0-1).
libcarp-assert-more-perl set to manually installed.
dh-strip-nondeterminism is already the newest version (1.15.0-1).
dh-strip-nondeterminism set to manually installed.
libasan8 is already the newest version (15.2.0-13).
libasan8 set to manually installed.
libcom-err2 is already the newest version (1.47.2-3+b8).
libcom-err2 set to manually installed.
libsndfile1 is already the newest version (1.2.2-4).
libsndfile1 set to manually installed.
libhdf5-dev is already the newest version (1.14.6+repack-2).
libhdf5-dev set to manually installed.
libaec0 is already the newest version (1.1.5-1).
libaec0 set to manually installed.
libuuid1 is already the newest version (2.41.3-4).
libpam-modules is already the newest version (1.7.0-5+b1).
libset-intspan-perl is already the newest version (1.19-3).
libset-intspan-perl set to manually installed.
libxdmcp6 is already the newest version (1:1.1.5-2).
libxdmcp6 set to manually installed.
libqt6help6 is already the newest version (6.9.2-5).
libqt6help6 set to manually installed.
libparse-recdescent-perl is already the newest version (1.967015+dfsg-4).
libparse-recdescent-perl set to manually installed.
libatomic1 is already the newest version (15.2.0-13).
libatomic1 set to manually installed.
libjson-maybexs-perl is already the newest version (1.004008-1).
libjson-maybexs-perl set to manually installed.
libattr1 is already the newest version (1:2.5.2-4).
libwebp7 is already the newest version (1.5.0-0.1+b1).
libwebp7 set to manually installed.
libclass-load-perl is already the newest version (0.25-2).
libclass-load-perl set to manually installed.
libpod-parser-perl is already the newest version (1.67-1).
libpod-parser-perl set to manually installed.
libasound2-data is already the newest version (1.2.15.3-1).
libasound2-data set to manually installed.
libmarkdown2 is already the newest version (2.2.7-2.1+b1).
libmarkdown2 set to manually installed.
libxxf86vm1 is already the newest version (1:1.1.4-2).
libxxf86vm1 set to manually installed.
libassuan9 is already the newest version (3.0.2-2+b1).
libassuan9 set to manually installed.
liblsan0 is already the newest version (15.2.0-13).
liblsan0 set to manually installed.
libtext-autoformat-perl is already the newest version (1.750000-2).
libtext-autoformat-perl set to manually installed.
libconfig-model-perl is already the newest version (2.155-1).
libconfig-model-perl set to manually installed.
libavahi-common-data is already the newest version (0.8-18).
libavahi-common-data set to manually installed.
libmodule-implementation-perl is already the newest version (0.09-2).
libmodule-implementation-perl set to manually installed.
libarchive-zip-perl is already the newest version (1.68-1).
libarchive-zip-perl set to manually installed.
libboolean-perl is already the newest version (0.46-3).
libboolean-perl set to manually installed.
libapt-pkg-perl is already the newest version (0.1.43).
libapt-pkg-perl set to manually installed.
libfftw3-bin is already the newest version (3.3.10-2+b2).
libfftw3-bin set to manually installed.
libgmp10 is already the newest version (2:6.3.0+dfsg-5+b1).
libheif-plugin-dav1d is already the newest version (1.21.2-3).
libheif-plugin-dav1d set to manually installed.
libmouse-perl is already the newest version (2.6.1-1).
libmouse-perl set to manually installed.
libxcb-icccm4 is already the newest version (0.4.2-1+b1).
libxcb-icccm4 set to manually installed.
libpsl-dev is already the newest version (0.21.2-1.1+b2).
libpsl-dev set to manually installed.
libyaml-0-2 is already the newest version (0.2.5-2+b1).
libyaml-0-2 set to manually installed.
man-db is already the newest version (2.13.1-1).
man-db set to manually installed.
ca-certificates is already the newest version (20250419).
ca-certificates set to manually installed.
libstdc++-15-dev is already the newest version (15.2.0-13).
libstdc++-15-dev set to manually installed.
libfile-listing-perl is already the newest version (6.16-1).
libfile-listing-perl set to manually installed.
libmailtools-perl is already the newest version (2.22-1).
libmailtools-perl set to manually installed.
libperl-critic-perl is already the newest version (1.156-1).
libperl-critic-perl set to manually installed.
xz-utils is already the newest version (5.8.2-2).
xz-utils set to manually installed.
libglu1-mesa is already the newest version (9.0.2-1.1+b4).
libglu1-mesa set to manually installed.
libglpk40 is already the newest version (5.0-2+b1).
libglpk40 set to manually installed.
comerr-dev is already the newest version (2.1-1.47.2-3+b8).
comerr-dev set to manually installed.
libnet-http-perl is already the newest version (6.24-1).
libnet-http-perl set to manually installed.
libpango-1.0-0 is already the newest version (1.57.0-1).
libpango-1.0-0 set to manually installed.
liblz4-1 is already the newest version (1.10.0-6).
liblz4-1 set to manually installed.
libmodule-pluggable-perl is already the newest version (6.3-1).
libmodule-pluggable-perl set to manually installed.
libtext-glob-perl is already the newest version (0.11-3).
libtext-glob-perl set to manually installed.
libconvert-binhex-perl is already the newest version (1.125-3).
libconvert-binhex-perl set to manually installed.
libdbus-1-3 is already the newest version (1.16.2-4).
libdbus-1-3 set to manually installed.
liblist-moreutils-perl is already the newest version (0.430-2).
liblist-moreutils-perl set to manually installed.
libwayland-client0 is already the newest version (1.24.0-2+b2).
libwayland-client0 set to manually installed.
hostname is already the newest version (3.25).
libcrypt1 is already the newest version (1:4.5.1-1).
libsqlite3-0 is already the newest version (3.46.1-9).
libsqlite3-0 set to manually installed.
libhash-merge-perl is already the newest version (0.302-1).
libhash-merge-perl set to manually installed.
libnghttp2-dev is already the newest version (1.68.0-1).
libnghttp2-dev set to manually installed.
perl-openssl-defaults is already the newest version (7+b2).
perl-openssl-defaults set to manually installed.
liburi-perl is already the newest version (5.34-2).
liburi-perl set to manually installed.
libparse-debcontrol-perl is already the newest version (2.005-6).
libparse-debcontrol-perl set to manually installed.
libtry-tiny-perl is already the newest version (0.32-1).
libtry-tiny-perl set to manually installed.
libxxhash0 is already the newest version (0.8.3-2+b1).
libxxhash0 set to manually installed.
unzip is already the newest version (6.0-29).
unzip set to manually installed.
libxcb-xinput0 is already the newest version (1.17.0-2+b2).
libxcb-xinput0 set to manually installed.
libhdf5-310 is already the newest version (1.14.6+repack-2).
libhdf5-310 set to manually installed.
liblist-someutils-perl is already the newest version (0.59-1).
liblist-someutils-perl set to manually installed.
libtool is already the newest version (2.5.4-9).
libtool set to manually installed.
libapp-cmd-perl is already the newest version (0.339-1).
libapp-cmd-perl set to manually installed.
libtasn1-6 is already the newest version (4.21.0-2).
libtasn1-6 set to manually installed.
gcc-15-riscv64-linux-gnu is already the newest version (15.2.0-13).
gcc-15-riscv64-linux-gnu set to manually installed.
libencode-locale-perl is already the newest version (1.05-3).
libencode-locale-perl set to manually installed.
libwww-robotrules-perl is already the newest version (6.02-1).
libwww-robotrules-perl set to manually installed.
libqt6xml6 is already the newest version (6.9.2+dfsg-4).
libqt6xml6 set to manually installed.
libgnutls30t64 is already the newest version (3.8.12-3).
libgnutls30t64 set to manually installed.
libappstream5 is already the newest version (1.1.2-1).
libappstream5 set to manually installed.
liblog-any-perl is already the newest version (1.718-1).
liblog-any-perl set to manually installed.
libqrupdate1 is already the newest version (1.1.5-3).
libqrupdate1 set to manually installed.
libgcrypt20 is already the newest version (1.11.2-3+b1).
libgcrypt20 set to manually installed.
libsyntax-keyword-try-perl is already the newest version (0.31-1).
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patchutils is already the newest version (0.4.3-1).
patchutils set to manually installed.
libsub-identify-perl is already the newest version (0.14-4).
libsub-identify-perl set to manually installed.
libldap-dev is already the newest version (2.6.10+dfsg-1+b1).
libldap-dev set to manually installed.
libpangoft2-1.0-0 is already the newest version (1.57.0-1).
libpangoft2-1.0-0 set to manually installed.
xtrans-dev is already the newest version (1.6.0-1).
xtrans-dev set to manually installed.
libtoml-tiny-perl is already the newest version (0.20-1).
libtoml-tiny-perl set to manually installed.
libgpg-error0 is already the newest version (1.58-2).
libgpg-error0 set to manually installed.
libmpfr6 is already the newest version (4.2.2-2+b1).
libmpfr6 set to manually installed.
libc6 is already the newest version (2.42-13).
gnuplot-nox is already the newest version (6.0.3+dfsg1-1).
gnuplot-nox set to manually installed.
autopoint is already the newest version (0.23.2-1).
autopoint set to manually installed.
libhogweed6t64 is already the newest version (3.10.2-1).
libhogweed6t64 set to manually installed.
libfont-ttf-perl is already the newest version (1.06-2).
libfont-ttf-perl set to manually installed.
libkadm5srv-mit12 is already the newest version (1.22.1-2).
libkadm5srv-mit12 set to manually installed.
libspqr4 is already the newest version (1:7.12.2+dfsg-1).
libspqr4 set to manually installed.
liblz1 is already the newest version (1.16~rc1-3).
liblz1 set to manually installed.
libexception-class-perl is already the newest version (1.45-1).
libexception-class-perl set to manually installed.
libhdf5-hl-cpp-310 is already the newest version (1.14.6+repack-2).
libhdf5-hl-cpp-310 set to manually installed.
libp11-kit-dev is already the newest version (0.26.2-2).
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libsub-name-perl is already the newest version (0.28-1+b1).
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libtimedate-perl is already the newest version (2.3300-2).
libtimedate-perl set to manually installed.
libppix-quotelike-perl is already the newest version (0.023-1).
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libqt6openglwidgets6 is already the newest version (6.9.2+dfsg-4).
libqt6openglwidgets6 set to manually installed.
libpam-modules-bin is already the newest version (1.7.0-5+b1).
libsafe-isa-perl is already the newest version (1.000010-1).
libsafe-isa-perl set to manually installed.
libgcc-s1 is already the newest version (15.2.0-13).
libglvnd0 is already the newest version (1.7.0-3).
libglvnd0 set to manually installed.
libtsan2 is already the newest version (15.2.0-13).
libtsan2 set to manually installed.
libmagic1t64 is already the newest version (1:5.46-5+b1).
libmagic1t64 set to manually installed.
libproc-processtable-perl is already the newest version (0.637-1+b1).
libproc-processtable-perl set to manually installed.
libxau-dev is already the newest version (1:1.0.11-1+b1).
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libdata-messagepack-perl is already the newest version (1.02-3).
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libhtml-tree-perl is already the newest version (5.07-3).
libhtml-tree-perl set to manually installed.
libnghttp3-dev is already the newest version (1.12.0-1).
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libdynaloader-functions-perl is already the newest version (0.004-2).
libdynaloader-functions-perl set to manually installed.
libb-hooks-op-check-perl is already the newest version (0.22-3+b3).
libb-hooks-op-check-perl set to manually installed.
intltool-debian is already the newest version (0.35.0+20060710.6).
intltool-debian set to manually installed.
libexporter-tiny-perl is already the newest version (1.006003-1).
libexporter-tiny-perl set to manually installed.
libltdl7 is already the newest version (2.5.4-9).
libltdl7 set to manually installed.
libxcb-render0 is already the newest version (1.17.0-2+b2).
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libegl-mesa0 is already the newest version (26.0.0-1).
libegl-mesa0 set to manually installed.
libkrb5-dev is already the newest version (1.22.1-2).
libkrb5-dev set to manually installed.
libgfortran5 is already the newest version (15.2.0-13).
libgfortran5 set to manually installed.
libunicode-utf8-perl is already the newest version (0.64-1).
libunicode-utf8-perl set to manually installed.
iso-codes is already the newest version (4.20.1-1).
iso-codes set to manually installed.
libmp3lame0 is already the newest version (3.101~svn6525+dfsg-2).
libmp3lame0 set to manually installed.
libfile-libmagic-perl is already the newest version (1.23-2+b2).
libfile-libmagic-perl set to manually installed.
libbinutils is already the newest version (2.46-2).
libbinutils set to manually installed.
libyaml-pp-perl is already the newest version (0.39.0-1).
libyaml-pp-perl set to manually installed.
libproxy1v5 is already the newest version (0.5.12-1).
libproxy1v5 set to manually installed.
gettext is already the newest version (0.23.2-1).
gettext set to manually installed.
libregexp-pattern-license-perl is already the newest version (3.11.2-1).
libregexp-pattern-license-perl set to manually installed.
libngtcp2-crypto-ossl0 is already the newest version (1.16.0-1).
libngtcp2-crypto-ossl0 set to manually installed.
libtasn1-6-dev is already the newest version (4.21.0-2).
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libxcb-util1 is already the newest version (0.4.1-1+b1).
libxcb-util1 set to manually installed.
libncurses6 is already the newest version (6.6+20251231-1).
libncurses6 set to manually installed.
libsm6 is already the newest version (2:1.2.6-1+b1).
libsm6 set to manually installed.
libpam0g is already the newest version (1.7.0-5+b1).
libxcb-image0 is already the newest version (0.4.0-2+b3).
libxcb-image0 set to manually installed.
dpkg-dev is already the newest version (1.23.5).
dpkg-dev set to manually installed.
libfeature-compat-class-perl is already the newest version (0.08-1).
libfeature-compat-class-perl set to manually installed.
libflac14 is already the newest version (1.5.0+ds-5).
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libunistring5 is already the newest version (1.3-2+b1).
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libdatrie1 is already the newest version (0.2.14-1).
libdatrie1 set to manually installed.
zlib1g is already the newest version (1:1.3.dfsg+really1.3.1-3).
libqt6printsupport6 is already the newest version (6.9.2+dfsg-4).
libqt6printsupport6 set to manually installed.
libkadm5clnt-mit12 is already the newest version (1.22.1-2).
libkadm5clnt-mit12 set to manually installed.
libsoftware-license-perl is already the newest version (0.104007-1).
libsoftware-license-perl set to manually installed.
build-essential is already the newest version (12.12).
build-essential set to manually installed.
base-files is already the newest version (14).
cpp is already the newest version (4:15.2.0-5).
cpp set to manually installed.
libinput-bin is already the newest version (1.31.0-1).
libinput-bin set to manually installed.
libfftw3-long3 is already the newest version (3.3.10-2+b2).
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libicu76 is already the newest version (76.1-4+b1).
libicu76 set to manually installed.
libhdf5-hl-310 is already the newest version (1.14.6+repack-2).
libhdf5-hl-310 set to manually installed.
libclass-data-inheritable-perl is already the newest version (0.10-1).
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libheif1 is already the newest version (1.21.2-3).
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liblist-utilsby-perl is already the newest version (0.12-2).
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shared-mime-info is already the newest version (2.4-5+b3).
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libkdb5-10t64 is already the newest version (1.22.1-2).
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patch is already the newest version (2.8-2).
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libparams-util-perl is already the newest version (1.102-3+b1).
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libpipeline1 is already the newest version (1.5.8-2).
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libtext-template-perl is already the newest version (1.61-1).
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libisl23 is already the newest version (0.27-1+b1).
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g++-15-riscv64-linux-gnu is already the newest version (15.2.0-13).
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libsuitesparseconfig7 is already the newest version (1:7.12.2+dfsg-1).
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dh-octave is already the newest version (1.14.1).
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libmoo-perl is already the newest version (2.005005-1).
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libedit2 is already the newest version (3.1-20251016-1).
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libobject-pad-perl is already the newest version (0.823-2).
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librtmp-dev is already the newest version (2.4+20151223.gitfa8646d.1-3+b1).
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libgd3 is already the newest version (2.3.3-13+b1).
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libclass-tiny-perl is already the newest version (1.008-2).
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gcc-15-base is already the newest version (15.2.0-13).
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libxcb-present0 is already the newest version (1.17.0-2+b2).
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libngtcp2-16 is already the newest version (1.16.0-1).
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libconfig-tiny-perl is already the newest version (2.30-1).
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libgraphicsmagick-q16-3t64 is already the newest version (1.4+really1.3.46-2).
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libgssapi-krb5-2 is already the newest version (1.22.1-2).
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liblcms2-2 is already the newest version (2.17-1).
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texinfo-lib is already the newest version (7.2-5).
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findutils is already the newest version (4.10.0-3).
libyaml-tiny-perl is already the newest version (1.76-1).
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aglfn is already the newest version (1.7+git20191031.4036a9c-2).
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sysvinit-utils is already the newest version (3.15-6).
libogg0 is already the newest version (1.3.6-2).
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liblog-log4perl-perl is already the newest version (1.57-1).
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libxext6 is already the newest version (2:1.3.4-1+b4).
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make is already the newest version (4.4.1-3).
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perl is already the newest version (5.40.1-7).
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grep is already the newest version (3.12-1).
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libhttp-date-perl is already the newest version (6.06-1).
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libidn2-dev is already the newest version (2.3.8-4+b1).
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libblkid1 is already the newest version (2.41.3-4).
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libdrm-amdgpu1 is already the newest version (2.4.131-1).
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libcamd3 is already the newest version (1:7.12.2+dfsg-1).
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libmtdev1t64 is already the newest version (1.1.7-1+b1).
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libreadline8t64 is already the newest version (8.3-4).
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openssl is already the newest version (3.5.5-1).
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libxs-parse-sublike-perl is already the newest version (0.41-1).
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libngtcp2-dev is already the newest version (1.16.0-1).
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libz3-4 is already the newest version (4.13.3-1+b1).
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libssh2-1t64 is already the newest version (1.11.1-1+b1).
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libglib2.0-0t64 is already the newest version (2.87.2-3).
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libdata-validate-uri-perl is already the newest version (0.07-3).
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gfortran is already the newest version (4:15.2.0-5).
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dwz is already the newest version (0.16-2).
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libtext-levenshtein-damerau-perl is already the newest version (0.41-3).
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mesa-libgallium is already the newest version (26.0.0-1).
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libqt6network6 is already the newest version (6.9.2+dfsg-4).
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ucf is already the newest version (3.0052).
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sed is already the newest version (4.9-2).
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libfile-basedir-perl is already the newest version (0.09-2).
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appstream is already the newest version (1.1.2-1).
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libcap2 is already the newest version (1:2.75-10+b5).
init-system-helpers is already the newest version (1.69).
libarray-intspan-perl is already the newest version (2.004-2).
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diffutils is already the newest version (1:3.12-1).
libcapture-tiny-perl is already the newest version (0.50-1).
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libxml-namespacesupport-perl is already the newest version (1.12-2).
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octave-io is already the newest version (2.7.1-1).
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procps is already the newest version (2:4.0.4-9+b1).
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libexpat1 is already the newest version (2.7.4-1).
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libxkbcommon0 is already the newest version (1.13.1-1).
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nettle-dev is already the newest version (3.10.2-1).
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libfile-sharedir-perl is already the newest version (1.118-3).
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libdevel-stacktrace-perl is already the newest version (2.0500-1).
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liberror-perl is already the newest version (0.17030-1).
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libksba8 is already the newest version (1.6.7-2+b2).
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libsub-quote-perl is already the newest version (2.006009-1).
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libncursesw6 is already the newest version (6.6+20251231-1).
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libitm1 is already the newest version (15.2.0-13).
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libxinerama1 is already the newest version (2:1.1.4-3+b5).
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libaom3 is already the newest version (3.13.1-2).
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libyuv0 is already the newest version (0.0.1922.20260106-1).
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libmd4c0 is already the newest version (0.5.2-2+b2).
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libmodule-runtime-perl is already the newest version (0.018-1).
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libpod-spell-perl is already the newest version (1.27-1).
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libxml-sax-perl is already the newest version (1.02+dfsg-4).
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libavahi-common3 is already the newest version (0.8-18).
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libperl5.40 is already the newest version (5.40.1-7).
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libaudit-common is already the newest version (1:4.1.2-1).
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libqt6sql6 is already the newest version (6.9.2+dfsg-4).
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libstring-rewriteprefix-perl is already the newest version (0.009-1).
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libgbm1 is already the newest version (26.0.0-1).
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libstdc++6 is already the newest version (15.2.0-13).
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libarpack2t64 is already the newest version (3.9.1-6+b1).
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libsub-exporter-progressive-perl is already the newest version (0.001013-3).
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libhdf5-cpp-310 is already the newest version (1.14.6+repack-2).
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libxcb-shm0 is already the newest version (1.17.0-2+b2).
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libaec-dev is already the newest version (1.1.5-1).
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libgraphicsmagick++-q16-12t64 is already the newest version (1.4+really1.3.46-2).
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libx11-data is already the newest version (2:1.8.13-1).
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libparams-validate-perl is already the newest version (1.31-2+b4).
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libimport-into-perl is already the newest version (1.002005-2).
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libqhull-r8.0 is already the newest version (2020.2-8).
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libxcursor1 is already the newest version (1:1.2.3-1+b1).
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libintl-perl is already the newest version (1.37-1).
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libblas3 is already the newest version (3.12.1-7+b1).
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binutils is already the newest version (2.46-2).
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libduktape207 is already the newest version (2.7.0-2+b3).
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libpsl5t64 is already the newest version (0.21.2-1.1+b2).
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libregexp-wildcards-perl is already the newest version (1.05-3).
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libproc2-0 is already the newest version (2:4.0.4-9+b1).
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libjansson4 is already the newest version (2.14-2+b4).
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g++ is already the newest version (4:15.2.0-5).
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libdata-section-perl is already the newest version (0.200008-1).
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libdebhelper-perl is already the newest version (13.30).
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liblapack3 is already the newest version (3.12.1-7+b1).
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netbase is already the newest version (6.5).
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libsystemd0 is already the newest version (259.1-1).
libppi-perl is already the newest version (1.284-1).
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libb-hooks-endofscope-perl is already the newest version (0.28-2).
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perl-base is already the newest version (5.40.1-7).
libunbound8 is already the newest version (1.24.2-1).
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debhelper is already the newest version (13.30).
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gpgconf is already the newest version (2.4.8-5).
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octave is already the newest version (10.3.0-3).
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libemail-address-xs-perl is already the newest version (1.05-1+b3).
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liblingua-en-inflect-perl is already the newest version (1.905-2).
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pkgconf-bin is already the newest version (2.5.1-4).
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libqt6widgets6 is already the newest version (6.9.2+dfsg-4).
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libcairo2 is already the newest version (1.18.4-3).
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libgraphite2-3 is already the newest version (1.3.14-11+b1).
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libnet-domain-tld-perl is already the newest version (1.75-4).
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libmpc3 is already the newest version (1.3.1-2+b1).
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libsmartcols1 is already the newest version (2.41.3-4).
libx11-6 is already the newest version (2:1.8.13-1).
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libpng16-16t64 is already the newest version (1.6.55-1).
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libtext-wrapper-perl is already the newest version (1.05-4).
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libxau6 is already the newest version (1:1.0.11-1+b1).
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libgprofng0 is already the newest version (2.46-2).
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librole-tiny-perl is already the newest version (2.002004-1).
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libperlio-gzip-perl is already the newest version (0.20-1+b3).
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liblapack-dev is already the newest version (3.12.1-7+b1).
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libconfig-model-backend-yaml-perl is already the newest version (2.134-2).
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libregexp-pattern-perl is already the newest version (0.2.14-3).
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cpp-15-riscv64-linux-gnu is already the newest version (15.2.0-13).
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libubsan1 is already the newest version (15.2.0-13).
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libipc-run3-perl is already the newest version (0.049-1).
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libfile-which-perl is already the newest version (1.27-2).
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libfftw3-single3 is already the newest version (3.3.10-2+b2).
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tex-common is already the newest version (6.20).
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libpcre2-8-0 is already the newest version (10.46-1+b1).
libamd3 is already the newest version (1:7.12.2+dfsg-1).
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tar is already the newest version (1.35+dfsg-4).
libxshmfence1 is already the newest version (1.3.3-1+b1).
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libmousex-strictconstructor-perl is already the newest version (0.02-3).
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libio-socket-ssl-perl is already the newest version (2.098-1).
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libice6 is already the newest version (2:1.1.1-1+b1).
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coreutils is already the newest version (9.7-3).
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libglx0 is already the newest version (1.7.0-3).
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libxcb-render-util0 is already the newest version (0.3.10-1+b1).
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libc-dev-bin is already the newest version (2.42-13).
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libidn2-0 is already the newest version (2.3.8-4+b1).
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libiterator-util-perl is already the newest version (0.02+ds1-2).
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libvulkan1 is already the newest version (1.4.341.0-1).
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liblwp-protocol-https-perl is already the newest version (6.14-1).
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ncurses-bin is already the newest version (6.6+20251231-1).
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libreadline-dev is already the newest version (8.3-4).
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cpp-riscv64-linux-gnu is already the newest version (4:15.2.0-5).
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libgetopt-long-descriptive-perl is already the newest version (0.117-1).
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libtext-unidecode-perl is already the newest version (1.30-3).
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octave-datatypes is already the newest version (1.1.8-2).
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libclass-xsaccessor-perl is already the newest version (1.19-4+b4).
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libtest-exception-perl is already the newest version (0.43-3).
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libxml-libxml-perl is already the newest version (2.0207+dfsg+really+2.0134-7).
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libfeature-compat-try-perl is already the newest version (0.05-1).
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diffstat is already the newest version (1.68-1).
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fontconfig is already the newest version (2.17.1-5).
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libtext-xslate-perl is already the newest version (3.5.9-2+b2).
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octave-common is already the newest version (10.3.0-3).
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libtinfo6 is already the newest version (6.6+20251231-1).
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libberkeleydb-perl is already the newest version (0.66-2).
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libxcb-keysyms1 is already the newest version (0.4.1-1+b1).
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libpangocairo-1.0-0 is already the newest version (1.57.0-1).
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libexporter-lite-perl is already the newest version (0.09-2).
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libio-html-perl is already the newest version (1.004-3).
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cme is already the newest version (1.044-2).
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libvorbisenc2 is already the newest version (1.3.7-3+b1).
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libhtml-tagset-perl is already the newest version (3.24-1).
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libstrictures-perl is already the newest version (2.000006-1).
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util-linux is already the newest version (2.41.3-4).
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libjbig0 is already the newest version (2.1-6.1+b3).
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libconfig-model-dpkg-perl is already the newest version (3.017).
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libsasl2-modules-db is already the newest version (2.1.28+dfsg1-10).
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libldap2 is already the newest version (2.6.10+dfsg-1+b1).
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libcxsparse4 is already the newest version (1:7.12.2+dfsg-1).
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libcap-ng0 is already the newest version (0.9.1-1).
libllvm21 is already the newest version (1:21.1.8-3).
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gettext-base is already the newest version (0.23.2-1).
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perl-modules-5.40 is already the newest version (5.40.1-7).
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libavif16 is already the newest version (1.3.0-1+b2).
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base-passwd is already the newest version (3.6.8).
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libinput10 is already the newest version (1.31.0-1).
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libxcb-randr0 is already the newest version (1.17.0-2+b2).
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libxdmcp-dev is already the newest version (1:1.1.5-2).
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libcpanel-json-xs-perl is already the newest version (4.40-1).
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liblzo2-2 is already the newest version (2.10-3+b2).
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libbz2-1.0 is already the newest version (1.0.8-6+b1).
libdata-validate-domain-perl is already the newest version (0.15-1).
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debconf is already the newest version (1.5.92).
libpcre2-16-0 is already the newest version (10.46-1+b1).
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lintian is already the newest version (2.130.0).
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cpp-15 is already the newest version (15.2.0-13).
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libclass-inspector-perl is already the newest version (1.36-3).
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libglx-dev is already the newest version (1.7.0-3).
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libhttp-negotiate-perl is already the newest version (6.01-2).
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libssl-dev is already the newest version (3.5.5-1).
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librav1e0.8 is already the newest version (0.8.1-7).
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librtmp1 is already the newest version (2.4+20151223.gitfa8646d.1-3+b1).
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libzstd-dev is already the newest version (1.5.7+dfsg-3+b1).
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libdata-optlist-perl is already the newest version (0.114-1).
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xkb-data is already the newest version (2.46-2).
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gfortran-15 is already the newest version (15.2.0-13).
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libfreetype6 is already the newest version (2.14.1+dfsg-2).
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libhttp-cookies-perl is already the newest version (6.11-1).
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libmount1 is already the newest version (2.41.3-4).
libportaudio2 is already the newest version (19.7.0-1).
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openssl-provider-legacy is already the newest version (3.5.5-1).
libpam-runtime is already the newest version (1.7.0-5).
x11proto-dev is already the newest version (2025.1-1).
x11proto-dev set to manually installed.
libxkbcommon-x11-0 is already the newest version (1.13.1-1).
libxkbcommon-x11-0 set to manually installed.
libkeyutils1 is already the newest version (1.6.3-6+b1).
libkeyutils1 set to manually installed.
libthai-data is already the newest version (0.1.30-1).
libthai-data set to manually installed.
libfile-stripnondeterminism-perl is already the newest version (1.15.0-1).
libfile-stripnondeterminism-perl set to manually installed.
libgl-dev is already the newest version (1.7.0-3).
libgl-dev set to manually installed.
libfftw3-dev is already the newest version (3.3.10-2+b2).
libfftw3-dev set to manually installed.
libgl1 is already the newest version (1.7.0-3).
libgl1 set to manually installed.
libperlio-utf8-strict-perl is already the newest version (0.010-1+b3).
libperlio-utf8-strict-perl set to manually installed.
libhtml-tokeparser-simple-perl is already the newest version (3.16-4).
libhtml-tokeparser-simple-perl set to manually installed.
libnet-ipv6addr-perl is already the newest version (1.02-1).
libnet-ipv6addr-perl set to manually installed.
libio-stringy-perl is already the newest version (2.113-2).
libio-stringy-perl set to manually installed.
libnumber-compare-perl is already the newest version (0.03-3).
libnumber-compare-perl set to manually installed.
libqscintilla2-qt6-15 is already the newest version (2.14.1+dfsg-2).
libqscintilla2-qt6-15 set to manually installed.
ncurses-base is already the newest version (6.6+20251231-1).
libdav1d7 is already the newest version (1.5.3-1+b1).
libdav1d7 set to manually installed.
hdf5-helpers is already the newest version (1.14.6+repack-2).
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libstemmer0d is already the newest version (3.0.1-1+b1).
libstemmer0d set to manually installed.
libreadonly-perl is already the newest version (2.050-3).
libreadonly-perl set to manually installed.
libacl1 is already the newest version (2.3.2-3).
libdouble-conversion3 is already the newest version (3.4.0-1).
libdouble-conversion3 set to manually installed.
gcc-15 is already the newest version (15.2.0-13).
gcc-15 set to manually installed.
libglx-mesa0 is already the newest version (26.0.0-1).
libglx-mesa0 set to manually installed.
krb5-multidev is already the newest version (1.22.1-2).
krb5-multidev set to manually installed.
libhtml-parser-perl is already the newest version (3.83-1+b3).
libhtml-parser-perl set to manually installed.
libio-tiecombine-perl is already the newest version (1.005-3).
libio-tiecombine-perl set to manually installed.
libgnutls-dane0t64 is already the newest version (3.8.12-3).
libgnutls-dane0t64 set to manually installed.
libfltk1.3t64 is already the newest version (1.3.11-3).
libfltk1.3t64 set to manually installed.
libsasl2-2 is already the newest version (2.1.28+dfsg1-10).
libsasl2-2 set to manually installed.
libxft2 is already the newest version (2.3.6-1+b5).
libxft2 set to manually installed.
dh-autoreconf is already the newest version (21+nmu1).
dh-autoreconf set to manually installed.
binutils-riscv64-linux-gnu is already the newest version (2.46-2).
binutils-riscv64-linux-gnu set to manually installed.
g++-riscv64-linux-gnu is already the newest version (4:15.2.0-5).
g++-riscv64-linux-gnu set to manually installed.
libc-bin is already the newest version (2.42-13).
libxcb1-dev is already the newest version (1.17.0-2+b2).
libxcb1-dev set to manually installed.
libcolamd3 is already the newest version (1:7.12.2+dfsg-1).
libcolamd3 set to manually installed.
automake is already the newest version (1:1.18.1-3).
automake set to manually installed.
sensible-utils is already the newest version (0.0.26).
sensible-utils set to manually installed.
libjpeg62-turbo-dev is already the newest version (1:2.1.5-4).
libjpeg62-turbo-dev set to manually installed.
libimagequant0 is already the newest version (4.4.1-1+b1).
libimagequant0 set to manually installed.
libcholmod5 is already the newest version (1:7.12.2+dfsg-1).
libcholmod5 set to manually installed.
libclone-perl is already the newest version (0.47-1+b2).
libclone-perl set to manually installed.
libsereal-encoder-perl is already the newest version (5.004+ds-1+b3).
libsereal-encoder-perl set to manually installed.
rpcsvc-proto is already the newest version (1.4.3-1+b2).
rpcsvc-proto set to manually installed.
libngtcp2-crypto-gnutls8 is already the newest version (1.16.0-1).
libngtcp2-crypto-gnutls8 set to manually installed.
m4 is already the newest version (1.4.21-1).
m4 set to manually installed.
libgmp-dev is already the newest version (2:6.3.0+dfsg-5+b1).
libgmp-dev set to manually installed.
libppix-regexp-perl is already the newest version (0.091-1).
libppix-regexp-perl set to manually installed.
libhtml-html5-entities-perl is already the newest version (0.004-3).
libhtml-html5-entities-perl set to manually installed.
libindirect-perl is already the newest version (0.39-2+b3).
libindirect-perl set to manually installed.
libxs-parse-keyword-perl is already the newest version (0.49-1).
libxs-parse-keyword-perl set to manually installed.
readline-common is already the newest version (8.3-4).
readline-common set to manually installed.
libfribidi0 is already the newest version (1.0.16-5).
libfribidi0 set to manually installed.
g++-15 is already the newest version (15.2.0-13).
g++-15 set to manually installed.
libaliased-perl is already the newest version (0.34-3).
libaliased-perl set to manually installed.
libssh2-1-dev is already the newest version (1.11.1-1+b1).
libssh2-1-dev set to manually installed.
autotools-dev is already the newest version (20240727.1).
autotools-dev set to manually installed.
libcurl4-openssl-dev is already the newest version (8.19.0~rc2-2).
libcurl4-openssl-dev set to manually installed.
libxml2-16 is already the newest version (2.15.1+dfsg-2+b1).
libxml2-16 set to manually installed.
fontconfig-config is already the newest version (2.17.1-5).
fontconfig-config set to manually installed.
libdata-validate-ip-perl is already the newest version (0.31-1).
libdata-validate-ip-perl set to manually installed.
libts0t64 is already the newest version (1.22-1.1+b2).
libts0t64 set to manually installed.
libxcb-sync1 is already the newest version (1.17.0-2+b2).
libxcb-sync1 set to manually installed.
libmro-compat-perl is already the newest version (0.15-2).
libmro-compat-perl set to manually installed.
libgnutls28-dev is already the newest version (3.8.12-3).
libgnutls28-dev set to manually installed.
libb2-1 is already the newest version (0.98.1-1.1+b3).
libb2-1 set to manually installed.
libevent-2.1-7t64 is already the newest version (2.1.12-stable-10+b2).
libevent-2.1-7t64 set to manually installed.
libnghttp2-14 is already the newest version (1.68.0-1).
libnghttp2-14 set to manually installed.
libstring-format-perl is already the newest version (1.18-1).
libstring-format-perl set to manually installed.
libvorbis0a is already the newest version (1.3.7-3+b1).
libvorbis0a set to manually installed.
libopengl0 is already the newest version (1.7.0-3).
libopengl0 set to manually installed.
libsub-exporter-perl is already the newest version (0.990-1).
libsub-exporter-perl set to manually installed.
binutils-common is already the newest version (2.46-2).
binutils-common set to manually installed.
libgomp1 is already the newest version (15.2.0-13).
libgomp1 set to manually installed.
libabsl20240722 is already the newest version (20240722.0-4).
libabsl20240722 set to manually installed.
libfile-homedir-perl is already the newest version (1.006-2).
libfile-homedir-perl set to manually installed.
libudev1 is already the newest version (259.1-1).
libcurl4t64 is already the newest version (8.19.0~rc2-2).
libcurl4t64 set to manually installed.
libgfortran-15-dev is already the newest version (15.2.0-13).
libgfortran-15-dev set to manually installed.
linux-libc-dev is already the newest version (6.18.12-1).
linux-libc-dev set to manually installed.
libsub-uplevel-perl is already the newest version (0.2800-3).
libsub-uplevel-perl set to manually installed.
libopus0 is already the newest version (1.6.1-1).
libopus0 set to manually installed.
libqt6dbus6 is already the newest version (6.9.2+dfsg-4).
libqt6dbus6 set to manually installed.
libterm-readkey-perl is already the newest version (2.38-2+b3).
libterm-readkey-perl set to manually installed.
gfortran-riscv64-linux-gnu is already the newest version (4:15.2.0-5).
gfortran-riscv64-linux-gnu set to manually installed.
libk5crypto3 is already the newest version (1.22.1-2).
libk5crypto3 set to manually installed.
libctf-nobfd0 is already the newest version (2.46-2).
libctf-nobfd0 set to manually installed.
libgav1-2 is already the newest version (0.20.0-2).
libgav1-2 set to manually installed.
gpg is already the newest version (2.4.8-5).
gpg set to manually installed.
libcurl3t64-gnutls is already the newest version (8.19.0~rc2-2).
libcurl3t64-gnutls set to manually installed.
liblerc4 is already the newest version (4.0.0+ds-5+b1).
liblerc4 set to manually installed.
libpod-constants-perl is already the newest version (0.19-2).
libpod-constants-perl set to manually installed.
libevdev2 is already the newest version (1.13.6+dfsg-1).
libevdev2 set to manually installed.
libdevel-size-perl is already the newest version (0.86-1).
libdevel-size-perl set to manually installed.
libavahi-client3 is already the newest version (0.8-18).
libavahi-client3 set to manually installed.
libpath-iterator-rule-perl is already the newest version (1.015-2).
libpath-iterator-rule-perl set to manually installed.
libkrb5-3 is already the newest version (1.22.1-2).
libkrb5-3 set to manually installed.
libpod-pom-perl is already the newest version (2.01-4).
libpod-pom-perl set to manually installed.
libsz2 is already the newest version (1.1.5-1).
libsz2 set to manually installed.
bash is already the newest version (5.3-2).
octave-dev is already the newest version (10.3.0-3).
octave-dev set to manually installed.
libgdbm-compat4t64 is already the newest version (1.26-1+b1).
libgdbm-compat4t64 set to manually installed.
libsereal-decoder-perl is already the newest version (5.004+ds-1+b4).
libsereal-decoder-perl set to manually installed.
libssl3t64 is already the newest version (3.5.5-1).
libmousex-nativetraits-perl is already the newest version (1.09-3).
libmousex-nativetraits-perl set to manually installed.
libxcb-xfixes0 is already the newest version (1.17.0-2+b2).
libxcb-xfixes0 set to manually installed.
libumfpack6 is already the newest version (1:7.12.2+dfsg-1).
libumfpack6 set to manually installed.
libsensors-config is already the newest version (1:3.6.2-2).
libsensors-config set to manually installed.
libdebconfclient0 is already the newest version (0.282+b2).
0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
I: running --customize-hook in shell: sh -c 'chroot "$1" dpkg -r debootsnap-dummy' exec /srv/rebuilderd/tmp/mmdebstrap.xnwdmcGr71
(Reading database ... 33900 files and directories currently installed.)
Removing debootsnap-dummy (1.0) ...
I: running --customize-hook in shell: sh -c 'chroot "$1" dpkg-query --showformat '${binary:Package}=${Version}\n' --show > "$1/pkglist"' exec /srv/rebuilderd/tmp/mmdebstrap.xnwdmcGr71
I: running special hook: download /pkglist ./pkglist
I: running --customize-hook in shell: sh -c 'rm "$1/pkglist"' exec /srv/rebuilderd/tmp/mmdebstrap.xnwdmcGr71
I: running special hook: upload sources.list /etc/apt/sources.list
I: waiting for background processes to finish...
I: cleaning package lists and apt cache...
I: skipping cleanup/reproducible as requested
I: creating tarball...
I: done
I: removing tempdir /srv/rebuilderd/tmp/mmdebstrap.xnwdmcGr71...
I: success in 147.0144 seconds

Downloading dependency 605 of 663: libxs-parse-keyword-perl:riscv64=0.49-1
Downloading dependency 606 of 663: readline-common:riscv64=8.3-4
Downloading dependency 607 of 663: libfribidi0:riscv64=1.0.16-5
Downloading dependency 608 of 663: g++-15:riscv64=15.2.0-13
Downloading dependency 609 of 663: libaliased-perl:riscv64=0.34-3
Downloading dependency 610 of 663: libssh2-1-dev:riscv64=1.11.1-1+b1
Downloading dependency 611 of 663: autotools-dev:riscv64=20240727.1
Downloading dependency 612 of 663: libcurl4-openssl-dev:riscv64=8.19.0~rc2-2
Downloading dependency 613 of 663: libxml2-16:riscv64=2.15.1+dfsg-2+b1
Downloading dependency 614 of 663: fontconfig-config:riscv64=2.17.1-5
Downloading dependency 615 of 663: libdata-validate-ip-perl:riscv64=0.31-1
Downloading dependency 616 of 663: libts0t64:riscv64=1.22-1.1+b2
Downloading dependency 617 of 663: libxcb-sync1:riscv64=1.17.0-2+b2
Downloading dependency 618 of 663: libmro-compat-perl:riscv64=0.15-2
Downloading dependency 619 of 663: libgnutls28-dev:riscv64=3.8.12-3
Downloading dependency 620 of 663: libb2-1:riscv64=0.98.1-1.1+b3
Downloading dependency 621 of 663: libevent-2.1-7t64:riscv64=2.1.12-stable-10+b2
Downloading dependency 622 of 663: libnghttp2-14:riscv64=1.68.0-1
Downloading dependency 623 of 663: libstring-format-perl:riscv64=1.18-1
Downloading dependency 624 of 663: libvorbis0a:riscv64=1.3.7-3+b1
Downloading dependency 625 of 663: libopengl0:riscv64=1.7.0-3
Downloading dependency 626 of 663: libsub-exporter-perl:riscv64=0.990-1
Downloading dependency 627 of 663: binutils-common:riscv64=2.46-2
Downloading dependency 628 of 663: libgomp1:riscv64=15.2.0-13
Downloading dependency 629 of 663: libabsl20240722:riscv64=20240722.0-4
Downloading dependency 630 of 663: libfile-homedir-perl:riscv64=1.006-2
Downloading dependency 631 of 663: libudev1:riscv64=259.1-1
Downloading dependency 632 of 663: libcurl4t64:riscv64=8.19.0~rc2-2
Downloading dependency 633 of 663: libgfortran-15-dev:riscv64=15.2.0-13
Downloading dependency 634 of 663: linux-libc-dev:riscv64=6.18.12-1
Downloading dependency 635 of 663: libsub-uplevel-perl:riscv64=0.2800-3
Downloading dependency 636 of 663: libopus0:riscv64=1.6.1-1
Downloading dependency 637 of 663: libqt6dbus6:riscv64=6.9.2+dfsg-4
Downloading dependency 638 of 663: libterm-readkey-perl:riscv64=2.38-2+b3
Downloading dependency 639 of 663: gfortran-riscv64-linux-gnu:riscv64=4:15.2.0-5
Downloading dependency 640 of 663: libk5crypto3:riscv64=1.22.1-2
Downloading dependency 641 of 663: libctf-nobfd0:riscv64=2.46-2
Downloading dependency 642 of 663: libgav1-2:riscv64=0.20.0-2
Downloading dependency 643 of 663: gpg:riscv64=2.4.8-5
Downloading dependency 644 of 663: libcurl3t64-gnutls:riscv64=8.19.0~rc2-2
Downloading dependency 645 of 663: liblerc4:riscv64=4.0.0+ds-5+b1
Downloading dependency 646 of 663: libpod-constants-perl:riscv64=0.19-2
Downloading dependency 647 of 663: libevdev2:riscv64=1.13.6+dfsg-1
Downloading dependency 648 of 663: libdevel-size-perl:riscv64=0.86-1
Downloading dependency 649 of 663: libavahi-client3:riscv64=0.8-18
Downloading dependency 650 of 663: libpath-iterator-rule-perl:riscv64=1.015-2
Downloading dependency 651 of 663: libkrb5-3:riscv64=1.22.1-2
Downloading dependency 652 of 663: libpod-pom-perl:riscv64=2.01-4
Downloading dependency 653 of 663: libsz2:riscv64=1.1.5-1
Downloading dependency 654 of 663: bash:riscv64=5.3-2
Downloading dependency 655 of 663: octave-dev:riscv64=10.3.0-3
Downloading dependency 656 of 663: libgdbm-compat4t64:riscv64=1.26-1+b1
Downloading dependency 657 of 663: libsereal-decoder-perl:riscv64=5.004+ds-1+b4
Downloading dependency 658 of 663: libssl3t64:riscv64=3.5.5-1
Downloading dependency 659 of 663: libmousex-nativetraits-perl:riscv64=1.09-3
Downloading dependency 660 of 663: libxcb-xfixes0:riscv64=1.17.0-2+b2
Downloading dependency 661 of 663: libumfpack6:riscv64=1:7.12.2+dfsg-1
Downloading dependency 662 of 663: libsensors-config:riscv64=1:3.6.2-2
Downloading dependency 663 of 663: libdebconfclient0:riscv64=0.282+b2
env --chdir=/srv/rebuilderd/tmp/rebuilderdQSPWid/out DEB_BUILD_OPTIONS=parallel=4 LANG=C.UTF-8 LC_COLLATE=C.UTF-8 LC_CTYPE=C.UTF-8 SOURCE_DATE_EPOCH=1771932468 SBUILD_CONFIG=/srv/rebuilderd/tmp/debrebuildx3q0LH/debrebuild.sbuildrc.9yv1zpBkoNgz sbuild --build=riscv64 --host=riscv64 --no-source --arch-any --no-arch-all --chroot=/srv/rebuilderd/tmp/debrebuildx3q0LH/debrebuild.tar.HZXqh3fJkUmA --chroot-mode=unshare --dist=unstable --no-run-lintian --no-run-piuparts --no-run-autopkgtest --no-apt-update --no-apt-upgrade --no-apt-distupgrade --verbose --nolog --bd-uninstallable-explainer= --build-path=/build/reproducible-path --dsc-dir=octave-statistics-1.8.1 /srv/rebuilderd/tmp/rebuilderdQSPWid/inputs/octave-statistics_1.8.1-3.dsc
I: consider moving your ~/.sbuildrc to /srv/rebuilderd/.config/sbuild/config.pl
The Debian buildds switched to the "unshare" backend and sbuild will default to it in the future.
To start using "unshare" add this to your `~/.config/sbuild/config.pl`:
	$chroot_mode = "unshare";
If you want to keep the old "schroot" mode even in the future, add the following to your `~/.config/sbuild/config.pl`:
	$chroot_mode = "schroot";
	$schroot = "schroot";
sbuild (Debian sbuild) 0.89.3+deb13u4 (28 December 2025) on localhost

+==============================================================================+
| octave-statistics 1.8.1-3 (riscv64)          Thu, 26 Feb 2026 23:25:34 +0000 |
+==============================================================================+

Package: octave-statistics
Version: 1.8.1-3
Source Version: 1.8.1-3
Distribution: unstable
Machine Architecture: riscv64
Host Architecture: riscv64
Build Architecture: riscv64
Build Type: any

I: No tarballs found in /srv/rebuilderd/.cache/sbuild
I: Unpacking /srv/rebuilderd/tmp/debrebuildx3q0LH/debrebuild.tar.HZXqh3fJkUmA to /srv/rebuilderd/tmp/tmp.sbuild.Vlx80RMP_w...
I: Setting up the chroot...
I: Creating chroot session...
I: Setting up log color...
I: Setting up apt archive...

+------------------------------------------------------------------------------+
| Fetch source files                           Thu, 26 Feb 2026 23:25:52 +0000 |
+------------------------------------------------------------------------------+


Local sources
-------------

/srv/rebuilderd/tmp/rebuilderdQSPWid/inputs/octave-statistics_1.8.1-3.dsc exists in /srv/rebuilderd/tmp/rebuilderdQSPWid/inputs; copying to chroot

+------------------------------------------------------------------------------+
| Install package build dependencies           Thu, 26 Feb 2026 23:25:55 +0000 |
+------------------------------------------------------------------------------+


Setup apt archive
-----------------

Merged Build-Depends: debhelper-compat (= 13), dh-octave (>= 1.11.1), dh-sequence-octave, octave, octave-datatypes (>= 1.1.8), octave-io, build-essential
Merged Build-Conflicts: octave-nan
Filtered Build-Depends: debhelper-compat (= 13), dh-octave (>= 1.11.1), dh-sequence-octave, octave, octave-datatypes (>= 1.1.8), octave-io, build-essential
Filtered Build-Conflicts: octave-nan
dpkg-deb: building package 'sbuild-build-depends-main-dummy' in '/build/reproducible-path/resolver-76Yrtr/apt_archive/sbuild-build-depends-main-dummy.deb'.

Install main build dependencies (apt-based resolver)
----------------------------------------------------

Installing build dependencies

+------------------------------------------------------------------------------+
| Check architectures                          Thu, 26 Feb 2026 23:26:03 +0000 |
+------------------------------------------------------------------------------+

Arch check ok (riscv64 included in any all)

+------------------------------------------------------------------------------+
| Build environment                            Thu, 26 Feb 2026 23:26:04 +0000 |
+------------------------------------------------------------------------------+

Kernel: Linux 6.6.87-win2030 #2025.04.20.18.43+bb0c69aea SMP Sun Apr 20 18:58:14 UTC 2025 riscv64 (riscv64)
Toolchain package versions: binutils_2.46-2 dpkg-dev_1.23.5 g++-15_15.2.0-13 gcc-15_15.2.0-13 libc6-dev_2.42-13 libstdc++-15-dev_15.2.0-13 libstdc++6_15.2.0-13 linux-libc-dev_6.18.12-1
Package versions: aglfn_1.7+git20191031.4036a9c-2 appstream_1.1.2-1 autoconf_2.72-3.1 automake_1:1.18.1-3 autopoint_0.23.2-1 autotools-dev_20240727.1 base-files_14 base-passwd_3.6.8 bash_5.3-2 binutils_2.46-2 binutils-common_2.46-2 binutils-riscv64-linux-gnu_2.46-2 bsdextrautils_2.41.3-4 build-essential_12.12 bzip2_1.0.8-6+b1 ca-certificates_20250419 cme_1.044-2 comerr-dev_2.1-1.47.2-3+b8 coreutils_9.7-3 cpp_4:15.2.0-5 cpp-15_15.2.0-13 cpp-15-riscv64-linux-gnu_15.2.0-13 cpp-riscv64-linux-gnu_4:15.2.0-5 dash_0.5.12-12 debconf_1.5.92 debhelper_13.30 debianutils_5.23.2 dh-autoreconf_21+nmu1 dh-octave_1.14.1 dh-octave-autopkgtest_1.14.1 dh-strip-nondeterminism_1.15.0-1 diffstat_1.68-1 diffutils_1:3.12-1 dpkg_1.23.5 dpkg-dev_1.23.5 dwz_0.16-2 file_1:5.46-5+b1 findutils_4.10.0-3 fontconfig_2.17.1-5 fontconfig-config_2.17.1-5 fonts-freefont-otf_20211204+svn4273-4 g++_4:15.2.0-5 g++-15_15.2.0-13 g++-15-riscv64-linux-gnu_15.2.0-13 g++-riscv64-linux-gnu_4:15.2.0-5 gcc_4:15.2.0-5 gcc-15_15.2.0-13 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libumfpack6_1:7.12.2+dfsg-1 libunbound8_1.24.2-1 libunicode-utf8-perl_0.64-1 libunistring5_1.3-2+b1 liburi-perl_5.34-2 libuuid1_2.41.3-4 libvariable-magic-perl_0.64-1+b1 libvorbis0a_1.3.7-3+b1 libvorbisenc2_1.3.7-3+b1 libvulkan1_1.4.341.0-1 libwacom-common_2.18.0-1 libwacom9_2.18.0-1 libwayland-client0_1.24.0-2+b2 libwebp7_1.5.0-0.1+b1 libwebpmux3_1.5.0-0.1+b1 libwmflite-0.2-7_0.2.13-2 libwww-mechanize-perl_2.20-1 libwww-perl_6.81-1 libwww-robotrules-perl_6.02-1 libx11-6_2:1.8.13-1 libx11-data_2:1.8.13-1 libx11-dev_2:1.8.13-1 libx11-xcb1_2:1.8.13-1 libxau-dev_1:1.0.11-1+b1 libxau6_1:1.0.11-1+b1 libxcb-cursor0_0.1.6-1 libxcb-dri3-0_1.17.0-2+b2 libxcb-glx0_1.17.0-2+b2 libxcb-icccm4_0.4.2-1+b1 libxcb-image0_0.4.0-2+b3 libxcb-keysyms1_0.4.1-1+b1 libxcb-present0_1.17.0-2+b2 libxcb-randr0_1.17.0-2+b2 libxcb-render-util0_0.3.10-1+b1 libxcb-render0_1.17.0-2+b2 libxcb-shape0_1.17.0-2+b2 libxcb-shm0_1.17.0-2+b2 libxcb-sync1_1.17.0-2+b2 libxcb-util1_0.4.1-1+b1 libxcb-xfixes0_1.17.0-2+b2 libxcb-xinput0_1.17.0-2+b2 libxcb-xkb1_1.17.0-2+b2 libxcb1_1.17.0-2+b2 libxcb1-dev_1.17.0-2+b2 libxcursor1_1:1.2.3-1+b1 libxdmcp-dev_1:1.1.5-2 libxdmcp6_1:1.1.5-2 libxext6_2:1.3.4-1+b4 libxfixes3_1:6.0.0-2+b5 libxft2_2.3.6-1+b5 libxinerama1_2:1.1.4-3+b5 libxkbcommon-x11-0_1.13.1-1 libxkbcommon0_1.13.1-1 libxml-libxml-perl_2.0207+dfsg+really+2.0134-7 libxml-namespacesupport-perl_1.12-2 libxml-sax-base-perl_1.09-3 libxml-sax-perl_1.02+dfsg-4 libxml2-16_2.15.1+dfsg-2+b1 libxmlb2_0.3.24-2 libxpm4_1:3.5.17-1+b4 libxrender1_1:0.9.12-1+b1 libxs-parse-keyword-perl_0.49-1 libxs-parse-sublike-perl_0.41-1 libxshmfence1_1.3.3-1+b1 libxxf86vm1_1:1.1.4-2 libxxhash0_0.8.3-2+b1 libyaml-0-2_0.2.5-2+b1 libyaml-libyaml-perl_0.904.0+ds-1 libyaml-pp-perl_0.39.0-1 libyaml-tiny-perl_1.76-1 libyuv0_0.0.1922.20260106-1 libz3-4_4.13.3-1+b1 libzstd-dev_1.5.7+dfsg-3+b1 libzstd1_1.5.7+dfsg-3+b1 licensecheck_3.3.9-1 lintian_2.130.0 linux-libc-dev_6.18.12-1 lzop_1.04-2+b1 m4_1.4.21-1 make_4.4.1-3 man-db_2.13.1-1 mawk_1.3.4.20260129-1 mesa-libgallium_26.0.0-1 ncurses-base_6.6+20251231-1 ncurses-bin_6.6+20251231-1 netbase_6.5 nettle-dev_3.10.2-1 octave_10.3.0-3 octave-common_10.3.0-3 octave-datatypes_1.1.8-2 octave-dev_10.3.0-3 octave-io_2.7.1-1 openssl_3.5.5-1 openssl-provider-legacy_3.5.5-1 patch_2.8-2 patchutils_0.4.3-1 perl_5.40.1-7 perl-base_5.40.1-7 perl-modules-5.40_5.40.1-7 perl-openssl-defaults_7+b2 perltidy_20250105-1 pkgconf_2.5.1-4 pkgconf-bin_2.5.1-4 plzip_1.13~rc1-3 po-debconf_1.0.22 procps_2:4.0.4-9+b1 readline-common_8.3-4 rpcsvc-proto_1.4.3-1+b2 sed_4.9-2 sensible-utils_0.0.26 shared-mime-info_2.4-5+b3 sysvinit-utils_3.15-6 t1utils_1.41-4+b1 tar_1.35+dfsg-4 tex-common_6.20 texinfo_7.2-5 texinfo-lib_7.2-5 ucf_3.0052 unzip_6.0-29 util-linux_2.41.3-4 x11-common_1:7.7+26 x11proto-dev_2025.1-1 xkb-data_2.46-2 xorg-sgml-doctools_1:1.11-1.1 xtrans-dev_1.6.0-1 xz-utils_5.8.2-2 zlib1g_1:1.3.dfsg+really1.3.1-3 zlib1g-dev_1:1.3.dfsg+really1.3.1-3

+------------------------------------------------------------------------------+
| Build                                        Thu, 26 Feb 2026 23:26:04 +0000 |
+------------------------------------------------------------------------------+


Unpack source
-------------

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA512

Format: 3.0 (quilt)
Source: octave-statistics
Binary: octave-statistics, octave-statistics-common
Architecture: any all
Version: 1.8.1-3
Maintainer: Debian Octave Group <team+pkg-octave-team@tracker.debian.org>
Uploaders: Sébastien Villemot <sebastien@debian.org>, Rafael Laboissière <rafael@debian.org>
Homepage: https://gnu-octave.github.io/packages/statistics/
Standards-Version: 4.7.3
Vcs-Browser: https://salsa.debian.org/pkg-octave-team/octave-statistics
Vcs-Git: https://salsa.debian.org/pkg-octave-team/octave-statistics.git
Testsuite: autopkgtest-pkg-octave
Build-Depends: debhelper-compat (= 13), dh-octave (>= 1.11.1), dh-sequence-octave, octave, octave-datatypes (>= 1.1.8), octave-io
Build-Conflicts: octave-nan
Package-List:
 octave-statistics deb math optional arch=any
 octave-statistics-common deb math optional arch=all
Checksums-Sha1:
 b4003dc5a80b0516221c89fdc0ea3c032974607f 1465996 octave-statistics_1.8.1.orig.tar.gz
 0775d429d8e2150c4098ac1c69967c8667e793f3 10752 octave-statistics_1.8.1-3.debian.tar.xz
Checksums-Sha256:
 5174f80ddf182674ca06d871d9fbed439f3eaed3e3970892a0716ed480f5545d 1465996 octave-statistics_1.8.1.orig.tar.gz
 ebde791e6ebde725ec4e565ebf18b2c20878a521e7c084a553d7b8c9d2350ae5 10752 octave-statistics_1.8.1-3.debian.tar.xz
Files:
 efe66a1d5028fc71d416b72b08de9742 1465996 octave-statistics_1.8.1.orig.tar.gz
 a56e8e407a47278ed7f1a14230c4132e 10752 octave-statistics_1.8.1-3.debian.tar.xz
Dgit: 005aba858d23dc3174eb23a1cfbed7a940e6e700 debian archive/debian/1.8.1-3 https://git.dgit.debian.org/octave-statistics

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dpkg-source: warning: cannot verify inline signature for ./octave-statistics_1.8.1-3.dsc: missing OpenPGP keyrings
dpkg-source: info: verifying ./octave-statistics_1.8.1-3.dsc
dpkg-source: info: skipping absent keyring /usr/share/keyrings/debian-keyring.pgp
dpkg-source: info: skipping absent keyring /usr/share/keyrings/debian-tag2upload.pgp
dpkg-source: info: skipping absent keyring /usr/share/keyrings/debian-nonupload.pgp
dpkg-source: info: skipping absent keyring /usr/share/keyrings/debian-maintainers.pgp
dpkg-source: info: extracting octave-statistics in /build/reproducible-path/octave-statistics-1.8.1
dpkg-source: info: unpacking octave-statistics_1.8.1.orig.tar.gz
dpkg-source: info: unpacking octave-statistics_1.8.1-3.debian.tar.xz
dpkg-source: info: using patch list from debian/patches/series
dpkg-source: info: applying relax-tolerance-unit-test-grpstats.patch

Check disk space
----------------

Sufficient free space for build

User Environment
----------------

APT_CONFIG=/var/lib/sbuild/apt.conf
DEB_BUILD_OPTIONS=parallel=4
HOME=/sbuild-nonexistent
LANG=C.UTF-8
LC_ALL=C.UTF-8
LC_COLLATE=C.UTF-8
LC_CTYPE=C.UTF-8
LOGNAME=sbuild
PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games
SHELL=/bin/sh
SOURCE_DATE_EPOCH=1771932468
USER=sbuild

dpkg-buildpackage
-----------------

Command: dpkg-buildpackage --sanitize-env -us -uc -B
dpkg-buildpackage: info: source package octave-statistics
dpkg-buildpackage: info: source version 1.8.1-3
dpkg-buildpackage: info: source distribution unstable
dpkg-buildpackage: info: source changed by Rafael Laboissière <rafael@debian.org>
 dpkg-source --before-build .
dpkg-buildpackage: info: host architecture riscv64
 debian/rules clean
dh clean --buildsystem=octave
   dh_auto_clean -O--buildsystem=octave
	dh_octave_clean
make[1]: Entering directory '/build/reproducible-path/octave-statistics-1.8.1'
make[1]: *** No rule to make target 'clean'.
make[1]: *** No rule to make target 'distclean'.
make[1]: Leaving directory '/build/reproducible-path/octave-statistics-1.8.1'
make[1]: Entering directory '/build/reproducible-path/octave-statistics-1.8.1/src'
rm -f editDistance.oct libsvmread.oct libsvmwrite.oct svmpredict.oct svmtrain.oct fcnntrain.oct fcnnpredict.oct
make[1]: *** No rule to make target 'distclean'.
make[1]: Leaving directory '/build/reproducible-path/octave-statistics-1.8.1/src'
   dh_autoreconf_clean -O--buildsystem=octave
   dh_clean -O--buildsystem=octave
 debian/rules binary-arch
dh binary-arch --buildsystem=octave
   dh_update_autotools_config -a -O--buildsystem=octave
   dh_autoreconf -a -O--buildsystem=octave
   dh_octave_version -a -O--buildsystem=octave
Checking the Octave version... ok
   dh_auto_configure -a -O--buildsystem=octave
   dh_auto_build -a -O--buildsystem=octave
   dh_auto_test -a -O--buildsystem=octave
   create-stamp debian/debhelper-build-stamp
   dh_testroot -a -O--buildsystem=octave
   dh_prep -a -O--buildsystem=octave
   dh_auto_install -a -O--buildsystem=octave
	octave --no-gui --no-history --silent --no-init-file --no-window-system /usr/share/dh-octave/install-pkg.m /build/reproducible-path/octave-statistics-1.8.1/debian/tmp/usr/share/octave/packages /build/reproducible-path/octave-statistics-1.8.1/debian/tmp/usr/lib/riscv64-linux-gnu/octave/packages
mkdir (/tmp/oct-4IxjNC)
untar (/tmp//octave-statistics-1.8.1.tar.gz, /tmp/oct-4IxjNC)
make[1]: Entering directory '/tmp/oct-4IxjNC/octave-statistics-1.8.1/src'
/usr/bin/mkoctfile --verbose editDistance.cc
/usr/bin/mkoctfile --verbose libsvmread.cc
/usr/bin/mkoctfile --verbose libsvmwrite.cc
/usr/bin/mkoctfile --verbose svmpredict.cc svm.cpp svm_model_octave.cc
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security    editDistance.cc -o /tmp/oct-wmfeUI.o
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security    libsvmread.cc -o /tmp/oct-7edqEp.o
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security    libsvmwrite.cc -o /tmp/oct-hImqSD.o
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security    svmpredict.cc -o /tmp/oct-34phDZ.o
In file included from /usr/include/octave-10.3.0/octave/../octave/ov.h:71,
                 from /usr/include/octave-10.3.0/octave/../octave/ovl.h:38,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-fcn.h:37,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-builtin.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-int.h:33,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-dld.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/oct.h:35,
                 from svmpredict.cc:23:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:236:24: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  236 |   std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>> m_converter;
      |                        ^~~~~~~~~~~~~~~
In file included from /usr/include/c++/15/locale:47,
                 from /usr/include/octave-10.3.0/octave/../octave/oct-string.h:31,
                 from /usr/include/octave-10.3.0/octave/../octave/oct-stream.h:38:
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
In file included from /usr/include/octave-10.3.0/octave/../octave/ov.h:71,
                 from /usr/include/octave-10.3.0/octave/../octave/ovl.h:38,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-fcn.h:37,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-builtin.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-int.h:33,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-dld.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/oct.h:35,
                 from editDistance.cc:26:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:236:24: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  236 |   std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>> m_converter;
      |                        ^~~~~~~~~~~~~~~
In file included from /usr/include/c++/15/locale:47,
                 from /usr/include/octave-10.3.0/octave/../octave/oct-string.h:31,
                 from /usr/include/octave-10.3.0/octave/../octave/oct-stream.h:38:
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h: In member function ‘std::ostream* octave::base_stream::preferred_output_stream()’:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:148:34: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  148 |           = std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>>
      |                                  ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:149:23: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  149 |             (new std::wbuffer_convert<convfacet_u8, char>
      |                       ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
In file included from /usr/include/octave-10.3.0/octave/../octave/ov.h:71,
                 from /usr/include/octave-10.3.0/octave/../octave/ovl.h:38,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-fcn.h:37,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-builtin.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-int.h:33,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-dld.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/oct.h:35,
                 from libsvmread.cc:22:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:236:24: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  236 |   std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>> m_converter;
      |                        ^~~~~~~~~~~~~~~
In file included from /usr/include/c++/15/locale:47,
                 from /usr/include/octave-10.3.0/octave/../octave/oct-string.h:31,
                 from /usr/include/octave-10.3.0/octave/../octave/oct-stream.h:38:
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h: In member function ‘std::ostream* octave::base_stream::preferred_output_stream()’:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:148:34: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  148 |           = std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>>
      |                                  ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:149:23: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  149 |             (new std::wbuffer_convert<convfacet_u8, char>
      |                       ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h: In member function ‘std::ostream* octave::base_stream::preferred_output_stream()’:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:148:34: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  148 |           = std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>>
      |                                  ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:149:23: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  149 |             (new std::wbuffer_convert<convfacet_u8, char>
      |                       ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
In file included from /usr/include/octave-10.3.0/octave/../octave/ov.h:71,
                 from /usr/include/octave-10.3.0/octave/../octave/ovl.h:38,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-fcn.h:37,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-builtin.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-int.h:33,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-dld.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/oct.h:35,
                 from libsvmwrite.cc:22:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:236:24: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  236 |   std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>> m_converter;
      |                        ^~~~~~~~~~~~~~~
In file included from /usr/include/c++/15/locale:47,
                 from /usr/include/octave-10.3.0/octave/../octave/oct-string.h:31,
                 from /usr/include/octave-10.3.0/octave/../octave/oct-stream.h:38:
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h: In member function ‘std::ostream* octave::base_stream::preferred_output_stream()’:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:148:34: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  148 |           = std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>>
      |                                  ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:149:23: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  149 |             (new std::wbuffer_convert<convfacet_u8, char>
      |                       ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
g++ -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security  -o libsvmwrite.oct  /tmp/oct-hImqSD.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose svmtrain.cc svm.cpp svm_model_octave.cc
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security    svmtrain.cc -o /tmp/oct-e0gdPK.o
g++ -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security  -o libsvmread.oct  /tmp/oct-7edqEp.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose fcnntrain.cc
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security    fcnntrain.cc -o /tmp/oct-ZGQWji.o
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security    svm.cpp -o /tmp/oct-9uzGDL.o
In file included from /usr/include/octave-10.3.0/octave/../octave/ov.h:71,
                 from /usr/include/octave-10.3.0/octave/../octave/ovl.h:38,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-fcn.h:37,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-builtin.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-int.h:33,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-dld.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/oct.h:35,
                 from svmtrain.cc:23:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:236:24: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  236 |   std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>> m_converter;
      |                        ^~~~~~~~~~~~~~~
In file included from /usr/include/c++/15/locale:47,
                 from /usr/include/octave-10.3.0/octave/../octave/oct-string.h:31,
                 from /usr/include/octave-10.3.0/octave/../octave/oct-stream.h:38:
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h: In member function ‘std::ostream* octave::base_stream::preferred_output_stream()’:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:148:34: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  148 |           = std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>>
      |                                  ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:149:23: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  149 |             (new std::wbuffer_convert<convfacet_u8, char>
      |                       ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
In file included from /usr/include/octave-10.3.0/octave/../octave/ov.h:71,
                 from /usr/include/octave-10.3.0/octave/../octave/ovl.h:38,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-fcn.h:37,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-builtin.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-int.h:33,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-dld.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/oct.h:35,
                 from fcnntrain.cc:26:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:236:24: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  236 |   std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>> m_converter;
      |                        ^~~~~~~~~~~~~~~
In file included from /usr/include/c++/15/locale:47,
                 from /usr/include/c++/15/iomanip:53,
                 from fcnntrain.cc:22:
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h: In member function ‘std::ostream* octave::base_stream::preferred_output_stream()’:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:148:34: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  148 |           = std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>>
      |                                  ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:149:23: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  149 |             (new std::wbuffer_convert<convfacet_u8, char>
      |                       ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security    svm_model_octave.cc -o /tmp/oct-v1T350.o
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security    svm.cpp -o /tmp/oct-cryY97.o
In file included from /usr/include/octave-10.3.0/octave/../octave/ov.h:71,
                 from /usr/include/octave-10.3.0/octave/../octave/ovl.h:38,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-fcn.h:37,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-builtin.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-int.h:33,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-dld.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/oct.h:35,
                 from svm_model_octave.cc:25:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:236:24: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  236 |   std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>> m_converter;
      |                        ^~~~~~~~~~~~~~~
In file included from /usr/include/c++/15/locale:47,
                 from /usr/include/octave-10.3.0/octave/../octave/oct-string.h:31,
                 from /usr/include/octave-10.3.0/octave/../octave/oct-stream.h:38:
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h: In member function ‘std::ostream* octave::base_stream::preferred_output_stream()’:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:148:34: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  148 |           = std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>>
      |                                  ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:149:23: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  149 |             (new std::wbuffer_convert<convfacet_u8, char>
      |                       ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
g++ -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security  -o editDistance.oct  /tmp/oct-wmfeUI.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
/usr/bin/mkoctfile --verbose fcnnpredict.cc
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security    fcnnpredict.cc -o /tmp/oct-loRk6c.o
g++ -c -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security    svm_model_octave.cc -o /tmp/oct-Tv59dc.o
In file included from /usr/include/octave-10.3.0/octave/../octave/ov.h:71,
                 from /usr/include/octave-10.3.0/octave/../octave/ovl.h:38,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-fcn.h:37,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-builtin.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-int.h:33,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-dld.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/oct.h:35,
                 from fcnnpredict.cc:26:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:236:24: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  236 |   std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>> m_converter;
      |                        ^~~~~~~~~~~~~~~
In file included from /usr/include/c++/15/locale:47,
                 from /usr/include/c++/15/iomanip:53,
                 from fcnnpredict.cc:22:
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h: In member function ‘std::ostream* octave::base_stream::preferred_output_stream()’:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:148:34: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  148 |           = std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>>
      |                                  ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:149:23: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  149 |             (new std::wbuffer_convert<convfacet_u8, char>
      |                       ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
In file included from /usr/include/octave-10.3.0/octave/../octave/ov.h:71,
                 from /usr/include/octave-10.3.0/octave/../octave/ovl.h:38,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-fcn.h:37,
                 from /usr/include/octave-10.3.0/octave/../octave/ov-builtin.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-int.h:33,
                 from /usr/include/octave-10.3.0/octave/../octave/defun-dld.h:35,
                 from /usr/include/octave-10.3.0/octave/../octave/oct.h:35,
                 from svm_model_octave.cc:25:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:236:24: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  236 |   std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>> m_converter;
      |                        ^~~~~~~~~~~~~~~
In file included from /usr/include/c++/15/locale:47,
                 from /usr/include/octave-10.3.0/octave/../octave/oct-string.h:31,
                 from /usr/include/octave-10.3.0/octave/../octave/oct-stream.h:38:
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h: In member function ‘std::ostream* octave::base_stream::preferred_output_stream()’:
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:148:34: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  148 |           = std::unique_ptr<std::wbuffer_convert<convfacet_u8, char>>
      |                                  ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
/usr/include/octave-10.3.0/octave/../octave/oct-stream.h:149:23: warning: ‘template<class _Codecvt, class _Elem, class _Tr> class std::wbuffer_convert’ is deprecated [-Wdeprecated-declarations]
  149 |             (new std::wbuffer_convert<convfacet_u8, char>
      |                       ^~~~~~~~~~~~~~~
/usr/include/c++/15/bits/locale_conv.h:409:33: note: declared here
  409 |     class _GLIBCXX17_DEPRECATED wbuffer_convert
      |                                 ^~~~~~~~~~~~~~~
g++ -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security  -o svmpredict.oct  /tmp/oct-34phDZ.o /tmp/oct-9uzGDL.o /tmp/oct-v1T350.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
g++ -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security  -o fcnntrain.oct  /tmp/oct-ZGQWji.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
g++ -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security  -o svmtrain.oct  /tmp/oct-e0gdPK.o /tmp/oct-cryY97.o /tmp/oct-Tv59dc.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
g++ -I/usr/include/octave-10.3.0/octave/.. -I/usr/include/octave-10.3.0/octave  -pthread -fopenmp -g -O2 -ffile-prefix-map=/build/reproducible-path/octave-statistics-1.8.1=. -fstack-protector-strong -Wformat -Werror=format-security  -o fcnnpredict.oct  /tmp/oct-loRk6c.o   -shared -Wl,-Bsymbolic -Wl,-z,relro  -flto=auto -ffat-lto-objects -Wl,-z,relro 
make[1]: Leaving directory '/tmp/oct-4IxjNC/octave-statistics-1.8.1/src'
copyfile /tmp/oct-4IxjNC/octave-statistics-1.8.1/src/editDistance.oct /tmp/oct-4IxjNC/octave-statistics-1.8.1/src/fcnnpredict.oct /tmp/oct-4IxjNC/octave-statistics-1.8.1/src/fcnntrain.oct /tmp/oct-4IxjNC/octave-statistics-1.8.1/src/libsvmread.oct /tmp/oct-4IxjNC/octave-statistics-1.8.1/src/libsvmwrite.oct /tmp/oct-4IxjNC/octave-statistics-1.8.1/src/svmpredict.oct /tmp/oct-4IxjNC/octave-statistics-1.8.1/src/svmtrain.oct /tmp/oct-4IxjNC/octave-statistics-1.8.1/src/editDistance.cc-tst /tmp/oct-4IxjNC/octave-statistics-1.8.1/src/fcnnpredict.cc-tst /tmp/oct-4IxjNC/octave-statistics-1.8.1/src/fcnntrain.cc-tst /tmp/oct-4IxjNC/octave-statistics-1.8.1/src/libsvmread.cc-tst /tmp/oct-4IxjNC/octave-statistics-1.8.1/src/libsvmwrite.cc-tst /tmp/oct-4IxjNC/octave-statistics-1.8.1/src/svmpredict.cc-tst /tmp/oct-4IxjNC/octave-statistics-1.8.1/src/svmtrain.cc-tst /tmp/oct-4IxjNC/octave-statistics-1.8.1/inst/riscv64-unknown-linux-gnu-api-v60
For information about changes from previous versions of the statistics package, run 'news statistics'.
Please report any issues with the statistics package at "https://github.com/gnu-octave/statistics/issues"
rm: cannot remove '/build/reproducible-path/octave-statistics-1.8.1/debian/tmp/usr/share/octave/packages/statistics-1.8.1/doc': Is a directory
   dh_octave_check -a -O--buildsystem=octave
Checking package...
Run the unit tests...
Checking m files ...
[inst/vartestn.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/vartestn.m
***** demo
 ## Test the null hypothesis that the variances are equal across the five
 ## columns of data in the students’ exam grades matrix, grades.

 load examgrades
 vartestn (grades)
***** demo
 ## Test the null hypothesis that the variances in miles per gallon (MPG) are
 ## equal across different model years.

 load carsmall
 vartestn (MPG, Model_Year)
***** demo
 ## Use Levene’s test to test the null hypothesis that the variances in miles
 ## per gallon (MPG) are equal across different model years.

 load carsmall
 p = vartestn (MPG, Model_Year, "TestType", "LeveneAbsolute")
***** demo
 ## Test the null hypothesis that the variances are equal across the five
 ## columns of data in the students’ exam grades matrix, grades, using the
 ## Brown-Forsythe test.  Suppress the display of the summary table of
 ## statistics and the box plot.

 load examgrades
 [p, stats] = vartestn (grades, "TestType", "BrownForsythe", "Display", "off")
***** error<vartestn: too few input arguments.> vartestn ();
***** error<vartestn: X must be a vector or a matrix.> vartestn (1);
***** error<vartestn: if X is a vector then a group vector is required.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7]);
***** error<vartestn: if X is a vector then a group vector is required.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7], []);
***** error<vartestn: if X is a vector then a group vector is required.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7], "TestType", "LeveneAbsolute");
***** error<vartestn: if X is a vector then a group vector is required.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7], [], "TestType", "LeveneAbsolute");
***** error<vartestn: invalid value for display.> ...
 vartestn ([1, 2, 3, 4, 5, 6, 7], [1, 1, 1, 2, 2, 2, 2], "Display", "some");
***** error<vartestn: invalid value for display.> ...
 vartestn (ones (50,3), "Display", "some");
***** error<vartestn: invalid value for testtype.> ...
 vartestn (ones (50,3), "Display", "off", "testtype", "some");
***** error<vartestn: optional arguments must be in name/value pairs.> ...
 vartestn (ones (50,3), [], "som");
***** error<vartestn: invalid name for optional arguments.> ...
 vartestn (ones (50,3), [], "some", "some");
***** error<vartestn: columns in X and GROUP length do not match.> ...
 vartestn (ones (50,3), [1, 2], "Display", "off");
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off");
 assert (p, 7.908647337018238e-08, 1e-14);
 assert (stat.chisqstat, 38.7332, 1e-4);
 assert (stat.df, 4);
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneAbsolute");
 assert (p, 9.523239714592791e-07, 1e-14);
 assert (stat.fstat, 8.5953, 1e-4);
 assert (stat.df, [4, 595]);
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off", "TestType", "LeveneQuadratic");
 assert (p, 7.219514351897161e-07, 1e-14);
 assert (stat.fstat, 8.7503, 1e-4);
 assert (stat.df, [4, 595]);
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off", "TestType", "BrownForsythe");
 assert (p, 1.312093241723211e-06, 1e-14);
 assert (stat.fstat, 8.4160, 1e-4);
 assert (stat.df, [4, 595]);
***** test
 load examgrades
 [p, stat] = vartestn (grades, "Display", "off", "TestType", "OBrien");
 assert (p, 8.235660885480556e-07, 1e-14);
 assert (stat.fstat, 8.6766, 1e-4);
 assert (stat.df, [4, 595]);
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/fitgmdist.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/fitgmdist.m
***** demo
 ## Generate a two-cluster problem
 C1 = randn (100, 2) + 2;
 C2 = randn (100, 2) - 2;
 data = [C1; C2];

 ## Perform clustering
 GMModel = fitgmdist (data, 2);

 ## Plot the result
 figure
 [heights, bins] = hist3([C1; C2]);
 [xx, yy] = meshgrid(bins{1}, bins{2});
 bbins = [xx(:), yy(:)];
 contour (reshape (GMModel.pdf (bbins), size (heights)));
***** demo
 Angle_Theta = [ 30 + 10 * randn(1, 10),  60 + 10 * randn(1, 10) ]';
 nbOrientations = 2;
 initial_orientations = [38.0; 18.0];
 initial_weights = ones (1, nbOrientations) / nbOrientations;
 initial_Sigma = 10 * ones (1, 1, nbOrientations);
 start = struct ("mu", initial_orientations, "Sigma", initial_Sigma, ...
                 "ComponentProportion", initial_weights);
 GMModel_Theta = fitgmdist (Angle_Theta, nbOrientations, "Start", start , ...
                            "RegularizationValue", 0.0001)
***** test
 load fisheriris
 classes = unique (species);
 [~, score] = pca (meas, "NumComponents", 2);
 options.MaxIter = 1000;
 options.TolFun = 1e-6;
 options.Display = "off";
 GMModel = fitgmdist (score, 2, "Options", options);
 assert (isa (GMModel, "gmdistribution"), true);
 assert (GMModel.mu, [1.3212, -0.0954; -2.6424, 0.1909], 1e-4);
1 test, 1 passed, 0 known failure, 0 skipped
[inst/crosstab.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/crosstab.m
***** error crosstab ()
***** error crosstab (1)
***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab (ones (2), [1 1])
***** error <crosstab: x1, x2 ... xn must be vectors.> crosstab ([1 1], ones (2))
***** error <x1, x2 .* must be vectors of the same length> crosstab ([1], [1 2])
***** error <x1, x2 .* must be vectors of the same length> crosstab ([1 2], [1])
***** error <crosstab: unsupported type for data vector.> crosstab ([1 2], {1, 2})
***** test
 load carbig
 [t, chisq, p, labels] = crosstab (cyl4, when, org);
 assert (t(2,3,1), 38);
 assert (labels{3,3}, "Japan");
***** test
 load carbig
 [t, chisq, p, labels] = crosstab (cyl4, when, org);
 assert (t(2,3,2), 17);
 assert (labels{1,3}, "USA");
***** test
 x = [1, 1, 2, 3, 1];
 y = [1, 2, 5, 3, 1];
 t = crosstab (x, y);
 assert (t, [2, 1, 0, 0; 0, 0, 0, 1; 0, 0, 1, 0]);
***** test
 x = [1, 1, 2, 3, 1];
 y = [1, 2, 3, 5, 1];
 t = crosstab (x, y);
 assert (t, [2, 1, 0, 0; 0, 0, 1, 0; 0, 0, 0, 1]);
***** test
 x1 = [1, 3, 7, 7, 8];
 x2 = [4, 2, 1, 1, 1];
 x3 = [6, 2, 6, 2, NaN];
 T1 = [0, 0, 0; 0, 1, 0; 1, 0, 0; 0, 0, 0];
 T2 = [0, 0, 1; 0, 0, 0; 1, 0, 0; 0, 0, 0];
 T = zeros (4, 3, 2);
 T(:,:,1) = T1;
 T(:,:,2) = T2;
 t = crosstab (x1, x2, x3);
 assert (t, T);
***** test
 x = [1, 2, NaN, 1];
 y = [1, 2, 3, NaN];
 t = crosstab (x, y);
 assert (t, [1, 0, 0; 0, 1, 0]);
***** test
 x = categorical ({'A', 'B', 'A', 'C', 'B'});
 y = [1, 2, 1, 3, 2];
 t = crosstab (x, y);
 assert (size (t), [3, 3]);
 assert (t(1, 1), 2);  # A with 1
 assert (t(2, 2), 2);  # B with 2
***** test
 x = categorical ({'low', 'med', 'high', 'low', 'med'});
 y = categorical ({'X', 'Y', 'X', 'Y', 'X'});
 t = crosstab (x, y);
 assert (size (t), [3, 2]);
***** test
 ## Test categorical with numeric
 x = categorical ([10, 20, 10, 30, 20]);
 y = [1, 2, 1, 3, 2];
 t = crosstab (x, y);
 assert (t, [2, 0, 0; 0, 2, 0; 0, 0, 1]);
***** test
 smoker = [1 1 0 0 1 0 1 1 0 0 1 0]';
 gender = [1 0 1 0 1 1 0 0 1 0 0 1]';
 w = warning ();
 warning ('off');
 [t, chisq, p, labels] = crosstab (smoker, gender);
 warning (w);
 assert (t, [2 4; 4 2]);
 assert (chisq, 1.33333333, 1e-8);
 assert (p, 0.24821308, 1e-8);
 assert (labels{1,1}, '0');
 assert (labels{1,2}, '0');
 assert (labels{2,1}, '1');
 assert (labels{2,2}, '1');
***** test
 ## Test for categorical
 smk_cat = categorical ([0 0 1 1 0 1 0 0 1 1 0 1]');
 gen_cat = categorical ([0 1 0 1 0 0 1 1 0 1 1 0]');
 w = warning ();
 warning ('off');
 [t, chisq, p] = crosstab (smk_cat, gen_cat);
 warning (w);
 assert (t, [2 4; 4 2]);
 assert (chisq, 1.33333333, 1e-6);
 assert (p, 0.24821308, 1e-6);
***** test
 x = [1 1 1 2 2 2 3 3 3]';
 y = [1 1 1 2 2 2 3 3 3]';
 w = warning ();
 warning ('off');
 [t, chisq, p, labels] = crosstab (x, y);
 warning (w);
 assert (t, diag ([3 3 3]));
 assert (chisq, 18.00000000);
 assert (p, 0.00123410, 1e-8);
***** test
 ## Test for Partial NaN giving NaN for chisq/p
 x7 = [1 2 3 4 NaN NaN]';
 y7 = [10 20 30 40 50 60]';
 w = warning ();
 warning ('off');
 [t, chisq, p, labels] = crosstab (x7, y7);
 warning (w);
 assert (t, [eye(4), zeros(4, 2)]);
 assert (isnan (chisq));
 assert (isnan (p));
 assert (labels{1,1}, '1');
 assert (labels{1,2}, '10');
 assert (labels{2,1}, '2');
 assert (labels{2,2}, '20');
 assert (labels{3,1}, '3');
 assert (labels{3,2}, '30');
 assert (labels{4,1}, '4');
 assert (labels{4,2}, '40');
 assert (isempty (labels{5,1}));
 assert (labels{5,2}, '50');
 assert (isempty (labels{6,1}));
 assert (labels{6,2}, '60');
***** test
 a = ones (15,1);
 b = ones (15,1);
 w = warning ();
 warning ('off');
 [t, chisq, p] = crosstab (a, b);
 warning (w);
 assert (t, 15);
 assert (isnan (chisq));
 assert (isnan (p));
***** test
 ## all NaN → empty table + NaN stats
 na = NaN (6,1);
 nb = (1:6)';
 w = warning ();
 warning ('off');
 [t, chisq, p] = crosstab (na, nb);
 warning (w);
 assert (all (t(:) == 0));
 assert (isnan (chisq));
 assert (isnan (p));
***** test
 ## single observation → 1×1 table, NaN statistic
 w = warning ();
 warning ('off');
 [t, chisq, p, labels] = crosstab (5, 'Z');
 warning (w);
 assert (t, 1);
 assert (isnan (chisq));
 assert (isnan (p));
 assert (labels{1,1}, '5');
 assert (labels{1,2}, 'Z');
***** test
 xx = [1 1 1 1 2 2 2 2]';
 yy = [10 10 10 10 20 20 20 20]';
 w = warning ();
 warning ('off');
 [t, chisq, p] = crosstab (xx, yy);
 warning (w);
 assert (t, [4 0; 0 4]);
 assert (chisq, 8.00000000);
 assert (p, 0.00467773, 1e-8);
***** test
 set1 = repmat ((1:5)', 20, 1);
 set2 = repmat ([1; 2], 50, 1);
 w = warning ();
 warning ('off');
 [t, chisq, p] = crosstab (set1, set2);
 warning (w);
 assert (t, 10 * ones (5,2));
 assert (chisq, 0);
 assert (p, 1);
***** test
 ## 3-way table with NaN
 a = [1 1 2 2 3 3 1]';
 b = [1 2 1 2 1 2 1]';
 c = [1 1 NaN 2 2 1 2]';
 w = warning ();
 warning ('off');
 [t, chisq, p] = crosstab (a, b, c);
 warning (w);
 expected(:,:,1) = [1 1; 0 0; 0 1];
 expected(:,:,2) = [1 0; 0 1; 1 0];
 assert (t, expected);
 assert (chisq, 6.00000000);
 assert (p, 0.53974935, 1e-9);
***** test
 ## sparse cellstr
 g = [1 5 7 12 1 5 19]';
 h = {'A', 'B', 'A', 'C', 'D', 'E', 'A'}';
 w = warning ();
 warning ('off');
 [t, chisq, p] = crosstab (g, h);
 warning (w);
 assert (sum (t(:)), 7);
 assert (chisq, 16.33333333, 1e-8);
 assert (p, 0.42994852, 1e-8);
***** test
 ## string array
 str1 = ['low'; 'high'; 'med'; 'low'; 'high'];
 str2 = ['X'; 'Y'; 'X'; 'Y'; 'X'];
 w = warning ();
 warning ('off');
 [t, chisq, p] = crosstab (str1, str2);
 warning (w);
 assert (t, [1 1; 1 1; 1 0]);
 assert (chisq, 0.83333333, 1e-8);
 assert (p, 0.659240631, 1e-9);
***** test
 ## cellstr
 c1 = {'A','B','A','C','B','A'}';
 c2 = {'1','2','1','3','2','1'}';
 w = warning ();
 warning ('off');
 [t, chisq, p] = crosstab (c1, c2);
 warning (w);
 assert (t, [3 0 0; 0 2 0; 0 0 1]);
 assert (chisq, 12.00000000, 1e-14);
 assert (p, 0.01735127, 1e-8);
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/correlation_test.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/correlation_test.m
***** error<Invalid call to correlation_test.  Correct usage> correlation_test ();
***** error<Invalid call to correlation_test.  Correct usage> correlation_test (1);
***** error<correlation_test: X must contain finite real numbers.> ...
 correlation_test ([1 2 NaN]', [2 3 4]');
***** error<correlation_test: X must contain finite real numbers.> ...
 correlation_test ([1 2 Inf]', [2 3 4]');
***** error<correlation_test: X must contain finite real numbers.> ...
 correlation_test ([1 2 3+i]', [2 3 4]');
***** error<correlation_test: Y must contain finite real numbers.> ...
 correlation_test ([1 2 3]', [2 3 NaN]');
***** error<correlation_test: Y must contain finite real numbers.> ...
 correlation_test ([1 2 3]', [2 3 Inf]');
***** error<correlation_test: Y must contain finite real numbers.> ...
 correlation_test ([1 2 3]', [3 4 3+i]');
***** error<correlation_test: X and Y must be vectors of equal length.> ...
 correlation_test ([1 2 3]', [3 4 4 5]');
***** error<correlation_test: invalid value for alpha.> ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", 0);
***** error<correlation_test: invalid value for alpha.> ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", 1.2);
***** error<correlation_test: invalid value for alpha.> ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
***** error<correlation_test: invalid value for alpha.> ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", "a");
***** error<correlation_test: invalid Name argument.> ...
 correlation_test ([1 2 3]', [2 3 4]', "some", 0.05);
***** error<correlation_test: invalid value for tail.>  ...
 correlation_test ([1 2 3]', [2 3 4]', "tail", "val");
***** error<correlation_test: invalid value for tail.>  ...
 correlation_test ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
***** error<correlation_test: invalid value for method.>  ...
 correlation_test ([1 2 3]', [2 3 4]', "method", 0.01);
***** error<correlation_test: invalid value for method.>  ...
 correlation_test ([1 2 3]', [2 3 4]', "method", "some");
***** test
 x = [6 7 7 9 10 12 13 14 15 17];
 y = [19 22 27 25 30 28 30 29 25 32];
 [h, pval, stats] = correlation_test (x, y);
 assert (stats.corrcoef, corr (x', y'), 1e-14);
 assert (pval, 0.0223, 1e-4);
***** test
 x = [6 7 7 9 10 12 13 14 15 17]';
 y = [19 22 27 25 30 28 30 29 25 32]';
 [h, pval, stats] = correlation_test (x, y);
 assert (stats.corrcoef, corr (x, y), 1e-14);
 assert (pval, 0.0223, 1e-4);
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/pdist.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/pdist.m
***** shared xy, t, eucl, x
 xy = [0 1; 0 2; 7 6; 5 6];
 t = 1e-3;
 eucl = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 x = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
***** assert (pdist (xy),                 [1.000 8.602 7.071 8.062 6.403 2.000], t);
***** assert (pdist (xy, eucl),           [1.000 8.602 7.071 8.062 6.403 2.000], t);
***** assert (pdist (xy, "euclidean"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
***** assert (pdist (xy, "seuclidean"),   [0.380 2.735 2.363 2.486 2.070 0.561], t);
***** assert (pdist (xy, "mahalanobis"),  [1.384 1.967 2.446 2.384 1.535 2.045], t);
***** assert (pdist (xy, "cityblock"),    [1.000 12.00 10.00 11.00 9.000 2.000], t);
***** assert (pdist (xy, "minkowski"),    [1.000 8.602 7.071 8.062 6.403 2.000], t);
***** assert (pdist (xy, "minkowski", 3), [1.000 7.763 6.299 7.410 5.738 2.000], t);
***** assert (pdist (xy, "cosine"),       [0.000 0.349 0.231 0.349 0.231 0.013], t);
***** assert (pdist (xy, "correlation"),  [0.000 2.000 0.000 2.000 0.000 2.000], t);
***** assert (pdist (xy, "spearman"),     [0.000 2.000 0.000 2.000 0.000 2.000], t);
***** assert (pdist (xy, "hamming"),      [0.500 1.000 1.000 1.000 1.000 0.500], t);
***** assert (pdist (xy, "jaccard"),      [1.000 1.000 1.000 1.000 1.000 0.500], t);
***** assert (pdist (xy, "chebychev"),    [1.000 7.000 5.000 7.000 5.000 2.000], t);
***** assert (pdist (x), [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
***** assert (pdist (x, "euclidean"), ...
        [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
***** assert (pdist (x, eucl), ...
        [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
***** assert (pdist (x, "squaredeuclidean"), [27, 108, 8, 27, 35, 116]);
***** assert (pdist (x, "seuclidean"), ...
        [1.8071, 3.6142, 0.9831, 1.8071, 1.8143, 3.4854], 1e-4);
***** warning<pdist: matrix is close to singular> ...
 pdist (x, "mahalanobis");
***** assert (pdist (x, "cityblock"), [9, 18, 4, 9, 9, 18]);
***** assert (pdist (x, "minkowski"), ...
        [5.1962, 10.3923, 2.8284, 5.1962, 5.9161, 10.7703], 1e-4);
***** assert (pdist (x, "minkowski", 3), ...
        [4.3267, 8.6535, 2.5198, 4.3267, 5.3485, 9.2521], 1e-4);
***** assert (pdist (x, "cosine"), ...
        [0.0254, 0.0406, 0.2857, 0.0018, 0.1472, 0.1173], 1e-4);
***** assert (pdist (x, "correlation"), [0, 0, 2, 0, 2, 2], 1e-14);
***** assert (pdist (x, "spearman"), [0, 0, 2, 0, 2, 2], 1e-14);
***** assert (pdist (x, "hamming"), [1, 1, 2/3, 1, 1, 1]);
***** assert (pdist (x, "jaccard"), [1, 1, 2/3, 1, 1, 1]);
***** assert (pdist (x, "chebychev"), [3, 6, 2, 3, 5, 8]);
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/harmmean.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/harmmean.m
***** test
 x = [0:10];
 y = [x;x+5;x+10];
 assert (harmmean (x), 0);
 m = [0 8.907635160795225 14.30854471766802];
 assert (harmmean (y, 2), m', 4e-14);
 assert (harmmean (y, "all"), 0);
 y(2,4) = NaN;
 m(2) = 9.009855936313949;
 assert (harmmean (y, 2), [0 NaN m(3)]', 4e-14);
 assert (harmmean (y', "omitnan"), m, 4e-14);
 z = y + 20;
 assert (harmmean (z, "all"), NaN);
 assert (harmmean (z, "all", "includenan"), NaN);
 assert (harmmean (z, "all", "omitnan"), 29.1108719858295, 4e-14);
 m = [24.59488458841874 NaN 34.71244385944397];
 assert (harmmean (z'), m, 4e-14);
 assert (harmmean (z', "includenan"), m, 4e-14);
 m(2) = 29.84104075528277;
 assert (harmmean (z', "omitnan"), m, 4e-14);
 assert (harmmean (z, 2, "omitnan"), m', 4e-14);
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (harmmean (x, [3 2])), [10 1 1 3]);
 assert (size (harmmean (x, [1 2])), [1 1 6 3]);
 assert (size (harmmean (x, [1 2 4])), [1 1 6]);
 assert (size (harmmean (x, [1 4 3])), [1 40]);
 assert (size (harmmean (x, [1 2 3 4])), [1 1]);
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 m = repmat ([5.559045930488016;13.04950789021461], [5 1 1 3]);
 assert (harmmean (x, [3 2]), m, 4e-14);
 x(2,5,6,3) = NaN;
 m(2,3) = NaN;
 assert (harmmean (x, [3 2]), m, 4e-14);
 m(2,3) = 13.06617961315406;
 assert (harmmean (x, [3 2], "omitnan"), m, 4e-14);
***** error <harmmean: X must contain real nonnegative values.> harmmean ("char")
***** error <harmmean: X must contain real nonnegative values.> harmmean ([1 -1 3])
***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
 harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
***** error <harmmean: DIM must be a positive integer scalar or vector.> ...
 harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
***** error <harmmean: VECDIM must contain non-repeating positive integers.> ...
 harmmean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/parseWilkinsonFormula.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/parseWilkinsonFormula.m
***** demo
 ## Demo : Tokenizer Mode
 ## Inspects the raw tokens generated from a formula string.
 formula = "y ~ A * (B + c)";
 tokens = parseWilkinsonFormula (formula, "tokenize");
 display (tokens);
***** demo
 ## Demo : Parser Mode (AST generation)
 ## Returns the Abstract Syntax Tree (AST) structure.
 formula = "A / B";
 tree = parseWilkinsonFormula (formula, "parse");
 display (tree);
***** demo
 ## Demo : Expansion Mode (Crossings)
 ## Demonstrates standard Wilkinson expansion for interactions.
 formula = "A * B * C";
 terms = parseWilkinsonFormula (formula, "expand");
 disp (terms);
***** demo
 ## Demo : Expansion Mode (Nesting)
 ## Demonstrates hierarchical nesting logic.
 formula = "Block / Plot / Subplot";
 terms = parseWilkinsonFormula (formula, "expand");
 disp (terms);
***** demo
 ## Demo : Matrix Schema Mode
 ## Generates the binary terms matrix (Row = Term, Col = Variable).
 formula = "y ~ Age + Height + Age:Height";
 schema = parseWilkinsonFormula (formula, "matrix");
 disp (schema.VariableNames);
 disp (schema.Terms);
***** demo
 ## Demo : Model Matrix (Regression / Continuous)
 ## Builds the Design Matrix (X) and Response (y) for numeric data.
 d_reg.BP = [120; 122; 128; 130; 125];
 d_reg.Age = [25; 30; 35; 40; 32];
 d_reg.Weight = [70; 75; 80; 85; 78];
 [X, y, names] = parseWilkinsonFormula ("BP ~ Age * Weight", "model_matrix", d_reg);
 disp (names);
 disp (X);
***** demo
 ## Demo : Model Matrix (ANOVA / Categorical)
 ## Automatically handles categorical variables (dummy coding).
 d_cat.Yield = [10; 12; 15; 14; 11; 13];
 d_cat.Variety = {"A"; "A"; "B"; "B"; "C"; "C"};
 [X, y, names] = parseWilkinsonFormula ("Yield ~ Variety", "model_matrix", d_cat);
 disp (names);
 disp (X);
***** demo
 ## Demo : Model Matrix (Mixed Numeric & Categorical)
 ## Demonstrates Analysis of Covariance (ANCOVA) structures.
 d_mix.Growth = [1.2; 1.4; 1.1; 1.8];
 d_mix.Fertilizer = {"Old"; "Old"; "New"; "New"};
 d_mix.Dose = [10; 20; 10; 20];
 [X, ~, names] = parseWilkinsonFormula ("Growth ~ Fertilizer * Dose", "model_matrix", d_mix);
 disp (names);
 disp (X);
***** demo
 ## Demo : Multi-Response
 ## Selects specific response variables using comma.
 d_list = struct ();
 d_list.Yield_A = [10; 12; 11; 14];
 d_list.Yield_B = [20; 22; 21; 24];
 d_list.Rain    = [100; 110; 105; 120];
 formula = "Yield_A, Yield_B ~ Rain";
 [X, y, names] = parseWilkinsonFormula (formula, "model_matrix", d_list);
 disp (names);
 disp (y);
 disp (X);
***** demo
 ## Demo : Multi-Response
 ## Selects a contiguous range of variables using the hyphen.
 d_rng.Y_Jan = rand (4, 1);
 d_rng.Y_Feb = rand (4, 1);
 d_rng.Y_Mar = rand (4, 1);
 d_rng.Trt   = {"A"; "B"; "A"; "B"};
 formula = "Y_Jan - Y_Mar ~ Trt";
 [X, y, names] = parseWilkinsonFormula (formula, "model_matrix", d_rng);
 disp (names);
 disp (y);
 disp (X);
***** test
 ## Test : Identifiers with numbers and underscores
 tokens = parseWilkinsonFormula ("Yield ~ Var_1 + A2_B", "tokenize");
 vals = {tokens.value};
 assert (vals, {"Yield", "~", "Var_1", "+", "A2_B", "EOF"});
***** test
 ## Test : Floating point numbers
 tokens = parseWilkinsonFormula ("y ~ 0.5 * A", "tokenize");
 vals = {tokens.value};
 assert (vals, {"y", "~", "0.5", "*", "A", "EOF"});
***** test
 ## Test : Whitespace insensitivity
 t1 = parseWilkinsonFormula ("A*B", "tokenize");
 t2 = parseWilkinsonFormula ("A   * B", "tokenize");
 assert ({t1.value}, {t2.value});
***** test
 ## Test : Precedence
 t = parseWilkinsonFormula ("A + B * C . D", "expand");
 terms = cellfun (@(x) strjoin(sort(x), ":"), t, "UniformOutput", false);
 assert (sort (terms), sort ({"A", "B", "C:D", "B:C:D"}));
***** test
 ## Test : Parentheses Override
 t = parseWilkinsonFormula ("(A + B) . C", "expand");
 terms = cellfun (@(x) strjoin(sort(x), ":"), t, "UniformOutput", false);
 assert (sort (terms), sort ({"A:C", "B:C"}));
***** test
 ## Test : Crossing Operator (*)
 t = parseWilkinsonFormula ("A * B", "expand");
 assert (length (t), 3);
 t3 = parseWilkinsonFormula ("A * B * C", "expand");
 assert (length (t3), 7);
***** test
 ## Test : Nesting Operator (/)
 t = parseWilkinsonFormula ("Field / Plot", "expand");
 terms = cellfun (@(x) strjoin(sort(x), ":"), t, "UniformOutput", false);
 assert (sort (terms), sort ({"Field", "Field:Plot"}));
***** test
 ## Test : Multi-level Nesting
 t = parseWilkinsonFormula ("Block / Plot / Subplot", "expand");
 terms = cellfun (@(x) strjoin(sort(x), ":"), t, "UniformOutput", false);
 assert (sort (terms), sort ({"Block", "Block:Plot", "Block:Plot:Subplot"}));
***** test
 ## Test : Interaction Operator (.)
 t = parseWilkinsonFormula ("A . B", "expand");
 assert (length (t), 1);
 assert (t{1}, {"A", "B"});
***** test
 ## Test : Power operator on cube.
 t = parseWilkinsonFormula ("(A + B + C)^3", "expand");
 terms = cellfun (@(x) strjoin(sort(x), ":"), t, "UniformOutput", false);
 expected = sort ({"A", "B", "C", "A:B", "A:C", "B:C", "A:B:C"});
 assert (sort (terms), expected);
***** test
 ## Test : Power Operator.
 t = parseWilkinsonFormula ("(A + B + C)^2", "expand");
 terms = cellfun (@(x) strjoin(sort(x), ":"), t, "UniformOutput", false);
 assert (! ismember ("A:B:C", terms));
 assert (ismember ("A:B", terms));
***** test
 ## Test : Redundancy Check
 t1 = parseWilkinsonFormula ("A + A", "expand");
 assert (length (t1), 1);
 t2 = parseWilkinsonFormula ("A * A", "expand");
 assert (length (t2), 1);
***** test
 ## Test : Deletion - Exact (-)
 t = parseWilkinsonFormula ("A * B - A", "expand");
 terms = cellfun (@(x) strjoin(sort(x), ":"), t, "UniformOutput", false);
 assert (sort (terms), sort ({"B", "A:B"}));
***** test
 ## Test : Deletion - Clean (-*)
 t = parseWilkinsonFormula ("A * B -* A", "expand");
 terms = cellfun (@(x) strjoin(sort(x), ":"), t, "UniformOutput", false);
 assert (sort (terms), {"B"});
***** test
 ## Test : Deletion - Marginal (-/)
 t = parseWilkinsonFormula ("A * B -/ A", "expand");
 terms = cellfun (@(x) strjoin(sort(x), ":"), t, "UniformOutput", false);
 assert (sort (terms), sort ({"A", "B"}));
***** test
 ## Test : Deletion - Complex Sequence
 t = parseWilkinsonFormula ("A*B*C - A:B:C", "expand");
 assert (length (t), 6);
 terms = cellfun (@(x) strjoin(sort(x), ":"), t, "UniformOutput", false);
 assert (! ismember ("A:B:C", terms));
 assert (ismember ("A:B", terms));
***** test
 ## Test : LHS and RHS Identification
 s = parseWilkinsonFormula ("logY ~ A + B", "matrix");
 assert (s.VariableNames{s.ResponseIdx}, "logY");
 assert (any (strcmp ("A", s.VariableNames)));
***** test
 ## Test : No Response Variable
 s = parseWilkinsonFormula ("~ A + B", "matrix");
 assert (isempty (s.ResponseIdx));
***** test
 ## Test : Intercept Handling
 s1 = parseWilkinsonFormula ("~ A", "matrix");
 assert (any (all (s1.Terms == 0, 2)));
 s2 = parseWilkinsonFormula ("~ A - 1", "matrix");
 assert (! any (all (s2.Terms == 0, 2)));
***** test
 ## Test : Numeric Interaction
 d.y = [1;2;3;4;5];
 d.X1 = [1;2;1;2;1];
 d.X2 = [10;10;20;20;10];
 [M, ~, ~] = parseWilkinsonFormula ("y ~ X1:X2", "model_matrix", d);
 assert (size (M), [5, 2]);
 assert (M(:, 2), d.X1 .* d.X2);
***** test
 ## Test : Categorical Expansion
 d.y = [1;1;1];
 d.G = {"A"; "B"; "C"};
 [M, ~, names] = parseWilkinsonFormula ("~ G", "model_matrix", d);
 assert (size (M, 2), 3);
 assert (names, {"(Intercept)"; "G_B"; "G_C"});
***** test
 ## Test : Categorical * Categorical Rank
 d.y = [1;2;3;4];
 d.F1 = {"a";"b";"a";"b"};
 d.F2 = {"x";"x";"y";"y"};
 [M, ~, ~] = parseWilkinsonFormula ("~ F1 * F2", "model_matrix", d);
 assert (size (M, 2), 4);
 assert (rank (M), 4);
***** test
 ## Test : Numeric * Categorical Naming
 d.y = [1;2];
 d.N = [10; 20];
 d.C = {"lo"; "hi"};
 [M, ~, names] = parseWilkinsonFormula ("~ N * C", "model_matrix", d);
 assert (any (strcmp (names, "C_lo:N")));
***** test
 ## Test : Intercept Only Model
 d.y = [1; 2; 3];
 [X, ~, names] = parseWilkinsonFormula ("y ~ 1", "model_matrix", d);
 assert (size (X, 2), 1);
 assert (names, {"(Intercept)"});
 assert (all (X == 1));
***** test
 ## Test : NaNs and Missing Data
 d.y = [1; 2; 3; 4];
 d.A = [1; 1; NaN; 1];
 d.B = [10; 20; 30; NaN];
 [X, y_out, ~] = parseWilkinsonFormula ("y ~ A", "model_matrix", d);
 assert (length (y_out), 3);
 assert (y_out(3), 4);
 assert (size (X, 1), 3);
***** test
 ## Test : Nesting with Groups
 t = parseWilkinsonFormula ("A / (B + C)", "expand");
 terms = cellfun (@(x) strjoin(sort(x), ":"), t, "UniformOutput", false);
 expected = sort ({"A", "A:B", "A:C"});
 assert (sort (terms), expected);
***** test
 ## Test : Variable Name Collision
 d.Var = [1; 1];
 d.Var_1 = [2; 2];
 [~, ~, names] = parseWilkinsonFormula ("~ Var + Var_1", "model_matrix", d);
 assert (any (strcmp (names, "Var")));
 assert (any (strcmp (names, "Var_1")));
***** test
 ## Test : One-argument call
 result = parseWilkinsonFormula ("A * B");
 expected = sort ({"A", "B", "A:B"});
 actual = cellfun (@(x) strjoin(sort(x), ":"), result, "UniformOutput", false);
 assert (sort (actual), expected);
***** test
 ## Test : Compatibility with Table Data
 Age = [25; 30; 35; 40; 45];
 Weight = [70; 75; 80; 85; 90];
 BP = [120; 122; 128; 130; 135];
 T = table (Age, Weight, BP);
 formula = "BP ~ Age * Weight";
 [X, y, names] = parseWilkinsonFormula (formula, "model_matrix", T);
 assert (size (X), [5, 4]);
 assert (y, BP);
 assert (any (strcmp ("Age", names)));
 assert (any (strcmp ("Weight", names)));
 assert (names{1}, "(Intercept)");
***** test
 ## Test : Multi-variable List
 d.y1 = [1; 2; 3]; d.y2 = [4; 5; 6]; d.x = [1; 0; 1];
 [X, y, ~] = parseWilkinsonFormula ("y1, y2 ~ x", "model_matrix", d);
 assert (size (y), [3, 2]);
 assert (y(:,1), d.y1);
 assert (y(:,2), d.y2);
***** test
 ## Test : multivariable range.
 d.A = [10;20]; d.B = [30;40]; d.C = [50;60]; d.x = [1;2];
 [X, y, ~] = parseWilkinsonFormula ("A - C ~ x", "model_matrix", d);
 assert (size (y), [2, 3]);
 assert (y(:,1), d.A);
 assert (y(:,2), d.B);
 assert (y(:,3), d.C);
***** test
 ## Test : multivariable list + range.
 d.y1 = [1]; d.y2 = [2]; d.y3 = [3]; d.y4 = [4]; d.y5 = [5];
 d.x1 = [10]; d.x2 = [2];
 [X, y, names] = parseWilkinsonFormula ("y1, y3 - y5 ~ x1:x2", "model_matrix", d);
 expected_y = [d.y1, d.y3, d.y4, d.y5];
 assert (isequal (y, expected_y));
 assert (size (X, 2), 2);
 assert (any (strcmp (names, "x1:x2")));
***** test
 ## Test : reverse range.
 d.A = [1]; d.B = [2]; d.C = [3]; d.x = [10];
 [X, y, names] = parseWilkinsonFormula ("C - A ~ x - 1", "model_matrix", d);
 assert (size (y), [1, 3]);
 assert (y(:,1), d.A);
 assert (y(:,3), d.C);
 assert (size (X, 2), 1);
 assert (! any (strcmp (names, "(Intercept)")));
***** test
 ## Test : nans in multi-y.
 d.yA = {1; 2; 3; 4};
 d.yB = [10; 20; NaN; 40];
 d.x  = [1; 1; 1; 1];
 [X, y, ~] = parseWilkinsonFormula ("yA, yB ~ x", "model_matrix", d);
 assert (size (y), [3, 2]);
 assert (y(3, 1), 4);
 assert (y(3, 2), 40);
 assert (size (X, 1), 3);
***** error <Input formula string is required> parseWilkinsonFormula ()
***** error <Unknown mode> parseWilkinsonFormula ("y ~ x", "invalid_mode")
***** error <Unexpected End Of Formula> parseWilkinsonFormula ("", "parse")
***** error <Unexpected End Of Formula> parseWilkinsonFormula ("A +", "parse")
***** error <Unexpected End Of Formula> parseWilkinsonFormula ("A *", "parse")
***** error <Unexpected End Of Formula> parseWilkinsonFormula ("A .", "parse")
***** error <Unexpected End Of Formula> parseWilkinsonFormula ("A /", "parse")
***** error <Exponent must be a number> parseWilkinsonFormula ("(A+B)^C", "expand")
***** error <Mismatched Parentheses> parseWilkinsonFormula ("(A + B", "parse")
***** error <parseWilkinsonFormula: Unexpected token> parseWilkinsonFormula ("A + B)", "parse")
***** error <Unexpected token> parseWilkinsonFormula ("( )", "parse")
***** error <Unexpected token> parseWilkinsonFormula ("A + * B", "parse")
***** error <Unexpected token> parseWilkinsonFormula ("y ~ x ~ z", "parse")
***** error <'model_matrix' mode requires a Data Table> parseWilkinsonFormula ("~ A", "model_matrix")
***** error <Unknown variable> d.x=1; parseWilkinsonFormula ("~ Z", "model_matrix", d)
***** error <Response variable 'Z' not found in Data> d.x=1; d.y=1; parseWilkinsonFormula ("Z ~ x", "model_matrix", d)
***** error <Unknown variable 'A' in range> d.x=1; d.y=1; parseWilkinsonFormula ("A - y ~ x", "model_matrix", d)
***** error <Unknown variable 'B' in range> d.x=1; d.y=1; parseWilkinsonFormula ("y - B ~ x", "model_matrix", d)
***** error <Invalid syntax in response term> d.y=1; parseWilkinsonFormula ("y - y - y ~ x", "model_matrix", d)
***** error <Response variable 'S' must be numeric> d.S={"a";"b"}; d.x=[1;2]; parseWilkinsonFormula ("S ~ x", "model_matrix", d)
***** error <Data must be a table or struct> parseWilkinsonFormula ("y ~ x", "model_matrix", [1,2,3])
55 tests, 55 passed, 0 known failure, 0 skipped
[inst/ttest2.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/ttest2.m
***** test
 a = 1:5;
 b = 6:10;
 b(5) = NaN;
 [h,p,ci,stats] = ttest2 (a,b);
 assert (h, 1);
 assert (p, 0.002535996080258229, 1e-14);
 assert (ci, [-6.822014919225481, -2.17798508077452], 1e-14);
 assert (stats.tstat, -4.582575694955839, 1e-14);
 assert (stats.df, 7);
 assert (stats.sd, 1.4638501094228, 1e-13);
***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", "invalid");
***** error ttest2 ([8:0.1:12], [8:0.1:12], "tail", 25);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/histfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/histfit.m
***** demo
 histfit (randn (100, 1))
***** demo
 histfit (poissrnd (2, 1000, 1), 10, "Poisson")
***** demo
 histfit (betarnd (3, 10, 1000, 1), 10, "beta")
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
   histfit (x);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
warning: using the gnuplot graphics toolkit is discouraged

The gnuplot graphics toolkit is not actively maintained and has a number
of limitations that are unlikely to be fixed.  Communication with gnuplot
uses a one-directional pipe and limited information is passed back to the
Octave interpreter so most changes made interactively in the plot window
will not be reflected in the graphics properties managed by Octave.  For
example, if the plot window is closed with a mouse click, Octave will not
be notified and will not update its internal list of open figure windows.
The qt toolkit is recommended instead.
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
   histfit (x);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 4, 3, 2, NaN, 3, 2, 5, 6, 4, 7, 5, 9, 8, 10, 4, 11];
   histfit (x, 3);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   histfit (randn (100, 1));
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   histfit (poissrnd (2, 1000, 1), 10, "Poisson");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   histfit (betarnd (3, 10, 1000, 1), 10, "beta");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = gca ();
   histfit (ax, randn (100, 1));
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = gca ();
   histfit (ax, poissrnd (2, 1000, 1), 10, "Poisson");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = gca ();
   histfit (ax, betarnd (3, 10, 1000, 1), 10, "beta");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = axes ("parent", hf);
   fail ("histfit (ax)", "histfit: too few input arguments.");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error<histfit: X must be a numeric vector of real numbers.> ...
 histfit ('wer')
***** error<histfit: no data in X.> histfit ([NaN, NaN, NaN]);
***** error<histfit: NBINS must be a real scalar integer value.> ...
 histfit (randn (100, 1), 5.6)
***** error<histfit: DISTNAME must be a character vector.> ...
 histfit (randn (100, 1), 8, 5)
***** error<histfit: DISTNAME must be a character vector.> ...
 histfit (randn (100, 1), 8, {'normal'})
***** error<histfit: 'Kernel' distribution is not supported yet.> ...
 histfit (randn (100, 1), 8, 'Kernel')
***** error<histfit: unrecognized distribution name.> ...
 histfit (randn (100, 1), 8, 'ASDASDASD')
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/hist3.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/hist3.m
***** demo
 X = [
    1    1
    1    1
    1   10
    1   10
    5    5
    5    5
    5    5
    5    5
    5    5
    7    3
    7    3
    7    3
   10   10
   10   10];
 hist3 (X)
***** test
 N_exp = [ 0  0  0  5 20
           0  0 10 15  0
           0 15 10  0  0
          20  5  0  0  0];

 n = 100;
 x = [1:n]';
 y = [n:-1:1]';
 D = [x y];
 N = hist3 (D, [4 5]);
 assert (N, N_exp);
***** test
 N_exp = [0  0  0  0  1
          0  0  0  0  1
          0  0  0  0  1
          1  1  1  1 93];

 n = 100;
 x = [1:n]';
 y = [n:-1:1]';
 D = [x y];
 C{1} = [1 1.7 3 4];
 C{2} = [1:5];
 N = hist3 (D, C);
 assert (N, N_exp);
***** test
 D = [1 1; 3 1; 3 3; 3 1];
 [c, nn] = hist3 (D, {0:4, 0:4});
 exp_c = zeros (5);
 exp_c([7 9 19]) = [1 2 1];
 assert (c, exp_c);
 assert (nn, {0:4, 0:4});
***** test
 for i = 10
   assert (size (hist3 (rand (9, 2), "Edges", {[0:.2:1]; [0:.2:1]})), [6 6])
 endfor
***** test
 edge_1 = linspace (0, 10, 10);
 edge_2 = linspace (0, 50, 10);
 [c, nn] = hist3 ([1:10; 1:5:50]', "Edges", {edge_1, edge_2});
 exp_c = zeros (10, 10);
 exp_c([1 12 13 24 35 46 57 68 79 90]) = 1;
 assert (c, exp_c);

 assert (nn{1}, edge_1 + edge_1(2)/2, eps*10^4)
 assert (nn{2}, edge_2 + edge_2(2)/2, eps*10^4)
***** shared X
 X = [
  5  2
  5  3
  1  4
  5  3
  4  4
  1  2
  2  3
  3  3
  5  4
  5  3];
***** test
 N = zeros (10);
 N([1 10 53 56 60 91 98 100]) = [1 1 1 1 3 1 1 1];
 C = {(1.2:0.4:4.8), (2.1:0.2:3.9)};
 assert (nthargout ([1 2], @hist3, X), {N C}, eps*10^3)
***** test
 N = zeros (5, 7);
 N([1 5 17 18 20 31 34 35]) = [1 1 1 1 3 1 1 1];
 C = {(1.4:0.8:4.6), ((2+(1/7)):(2/7):(4-(1/7)))};
 assert (nthargout ([1 2], @hist3, X, [5 7]), {N C}, eps*10^3)
 assert (nthargout ([1 2], @hist3, X, "Nbins", [5 7]), {N C}, eps*10^3)
***** test
 N = [0 1 0; 0 1 0; 0 0 1; 0 0 0];
 C = {(2:5), (2.5:1:4.5)};
 assert (nthargout ([1 2], @hist3, X, "Edges", {(1.5:4.5), (2:4)}), {N C})
***** test
 N = [0 0 1 0 1 0; 0 0 0 1 0 0; 0 0 1 4 2 0];
 C = {(1.2:3.2), (0:5)};
 assert (nthargout ([1 2], @hist3, X, "Ctrs", C), {N C})
 assert (nthargout ([1 2], @hist3, X, C), {N C})
***** test
 [~, C] = hist3 (rand (10, 2), "Edges", {[0 .05 .15 .35 .55 .95],
                                         [-1 .05 .07 .2 .3 .5 .89 1.2]});
 C_exp = {[ 0.025  0.1   0.25   0.45  0.75  1.15], ...
          [-0.475  0.06  0.135  0.25  0.4   0.695  1.045  1.355]};
 assert (C, C_exp, eps*10^2)
***** test
 Xv = repmat ([1:10]', [1 2]);

 ## Test Centers
 assert (hist3 (Xv, "Ctrs", {1:10, 1:10}), eye (10))

 N_exp = eye (6);
 N_exp([1 end]) = 3;
 assert (hist3 (Xv, "Ctrs", {3:8, 3:8}), N_exp)

 N_exp = zeros (8, 6);
 N_exp([1 2 11 20 29 38 47 48]) = [2 1 1 1 1 1 1 2];
 assert (hist3 (Xv, "Ctrs", {2:9, 3:8}), N_exp)

 ## Test Edges
 assert (hist3 (Xv, "Edges", {1:10, 1:10}), eye (10))
 assert (hist3 (Xv, "Edges", {3:8, 3:8}), eye (6))
 assert (hist3 (Xv, "Edges", {2:9, 3:8}), [zeros(1, 6); eye(6); zeros(1, 6)])

 N_exp = zeros (14);
 N_exp(3:12, 3:12) = eye (10);
 assert (hist3 (Xv, "Edges", {-1:12, -1:12}), N_exp)

 ## Test for Nbins
 assert (hist3 (Xv), eye (10))
 assert (hist3 (Xv, [10 10]), eye (10))
 assert (hist3 (Xv, "nbins", [10 10]), eye (10))
 assert (hist3 (Xv, [5 5]), eye (5) * 2)

 N_exp = zeros (7, 5);
 N_exp([1 9 10 18 26 27 35]) = [2 1 1 2 1 1 2];
 assert (hist3 (Xv, [7 5]), N_exp)
***** test # bug #51059
 D = [1 1; NaN 2; 3 1; 3 3; 1 NaN; 3 1];
 [c, nn] = hist3 (D, {0:4, 0:4});
 exp_c = zeros (5);
 exp_c([7 9 19]) = [1 2 1];
 assert (c, exp_c)
 assert (nn, {0:4, 0:4})
***** test
 [c, nn] = hist3 ([1 8]);
 exp_c = zeros (10, 10);
 exp_c(6, 6) = 1;
 exp_nn = {-4:5, 3:12};
 assert (c, exp_c)
 assert (nn, exp_nn, eps)

 [c, nn] = hist3 ([1 8], [10 11]);
 exp_c = zeros (10, 11);
 exp_c(6, 6) = 1;
 exp_nn = {-4:5, 3:13};
 assert (c, exp_c)
 assert (nn, exp_nn, eps)
***** test
 [c, nn] = hist3 ([1 NaN; 2 3; 6 9; 8 NaN]);
 exp_c = zeros (10, 10);
 exp_c(2, 1) = 1;
 exp_c(8, 10) = 1;
 exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
 assert (c, exp_c)
 assert (nn, exp_nn, eps*100)
***** test
 [c, nn] = hist3 ([1 NaN; 2 NaN; 6 NaN; 8 NaN]);
 exp_c = zeros (10, 10);
 exp_nn = {linspace(1.35, 7.65, 10) NaN(1, 10)};
 assert (c, exp_c)
 assert (nn, exp_nn, eps*100)
***** test
 [c, nn] = hist3 ([1 NaN; NaN 3; NaN 9; 8 NaN]);
 exp_c = zeros (10, 10);
 exp_nn = {linspace(1.35, 7.65, 10) linspace(3.3, 8.7, 10)};
 assert (c, exp_c)
 assert (nn, exp_nn, eps*100)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/rangesearch.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/rangesearch.m
***** demo
 ## Generate 1000 random 2D points from each of five distinct multivariate
 ## normal distributions that form five separate classes
 N = 1000;
 d = 10;
 randn ("seed", 5);
 X1 = mvnrnd (d * [0, 0], eye (2), 1000);
 randn ("seed", 6);
 X2 = mvnrnd (d * [1, 1], eye (2), 1000);
 randn ("seed", 7);
 X3 = mvnrnd (d * [-1, -1], eye (2), 1000);
 randn ("seed", 8);
 X4 = mvnrnd (d * [1, -1], eye (2), 1000);
 randn ("seed", 8);
 X5 = mvnrnd (d * [-1, 1], eye (2), 1000);
 X = [X1; X2; X3; X4; X5];

 ## For each point in X, find the points in X that are within a radius d
 ## away from the points in X.
 Idx = rangesearch (X, X, d, "NSMethod", "exhaustive");

 ## Select the first point in X (corresponding to the first class) and find
 ## its nearest neighbors within the radius d.  Display these points in
 ## one color and the remaining points in a different color.
 x = X(1,:);
 nearestPoints = X (Idx{1},:);
 nonNearestIdx = true (size (X, 1), 1);
 nonNearestIdx(Idx{1}) = false;

 scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
 hold on
 scatter (nearestPoints(:,1),nearestPoints(:,2))
 scatter (x(1), x(2), "black", "filled")
 hold off

 ## Select the last point in X (corresponding to the fifth class) and find
 ## its nearest neighbors within the radius d.  Display these points in
 ## one color and the remaining points in a different color.
 x = X(end,:);
 nearestPoints = X (Idx{1},:);
 nonNearestIdx = true (size (X, 1), 1);
 nonNearestIdx(Idx{1}) = false;

 figure
 scatter (X(nonNearestIdx,1), X(nonNearestIdx,2))
 hold on
 scatter (nearestPoints(:,1),nearestPoints(:,2))
 scatter (x(1), x(2), "black", "filled")
 hold off
***** shared x, y, X, Y
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [2, 3, 4; 1, 4, 3];
 X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
 Y = [1, 2, 2, 3; 2, 3, 3, 4];
***** test
 [idx, D] = rangesearch (x, y, 4);
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive");
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "NSMethod", "kdtree");
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "SortIndices", true);
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "SortIndices", false);
 assert (idx, {[1, 2, 4]; [1, 4]});
 assert (D, {[1.7321, 3.4641, 3.3166]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "NSMethod", "exhaustive", ...
                         "SortIndices", false);
 assert (idx, {[1, 2, 4]; [1, 4]});
 assert (D, {[1.7321, 3.4641, 3.3166]; [2, 3.4641]}, 1e-4);
***** test
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [idx, D] = rangesearch (x, y, 4, "Distance", eucldist);
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [idx, D] = rangesearch (x, y, 4, "Distance", eucldist, ...
                         "NSMethod", "exhaustive");
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
                         "NSMethod", "exhaustive");
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[0.6024, 1.0079, 1.2047]; [0.6963, 1.2047]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 1.5, "Distance", "seuclidean", ...
                         "NSMethod", "exhaustive", "SortIndices", false);
 assert (idx, {[1, 2, 4]; [1, 4]});
 assert (D, {[0.6024, 1.2047, 1.0079]; [0.6963, 1.2047]}, 1e-4);
***** test
 [idx, D] = rangesearch (X, Y, 4);
 assert (idx, {[1, 2]; [1, 2, 3]});
 assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
***** test
 [idx, D] = rangesearch (X, Y, 2);
 assert (idx, {[1]; [1, 2]});
 assert (D, {[1.4142]; [1.4142, 1.4142]}, 1e-4);
***** test
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [idx, D] = rangesearch (X, Y, 4, "Distance", eucldist);
 assert (idx, {[1, 2]; [1, 2, 3]});
 assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
***** test
 [idx, D] = rangesearch (X, Y, 4, "SortIndices", false);
 assert (idx, {[1, 2]; [1, 2, 3]});
 assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
***** test
 [idx, D] = rangesearch (X, Y, 4, "Distance", "seuclidean", ...
                         "NSMethod", "exhaustive");
 assert (idx, {[1, 2]; [1, 2, 3]});
 assert (D, {[1.4142, 3.1623]; [1.4142, 1.4142, 3.1623]}, 1e-4);
***** test
 X = ones (10, 2);
 [idx, D] = rangesearch (X, X, 0.1, "NSMethod", "kdtree");
 assert (numel (idx), 10);
***** test
 X = ones (3, 2);
 [idx, D] = rangesearch (X, X, 0.1, "NSMethod", "kdtree", "BucketSize", 1);
 assert (numel (idx), 3);
 assert (cellfun (@numel, idx) == 3, [true; true; true]);
 assert (idx{1}, [1, 2, 3]);
 assert (idx{2}, [1, 2, 3]);
 assert (idx{3}, [1, 2, 3]);
 assert (D{1}, [0, 0, 0]);
 assert (D{2}, [0, 0, 0]);
 assert (D{3}, [0, 0, 0]);
***** test
 [idx, D] = rangesearch (x, y, 4, "NSMethod", "kdtree", "SortIndices", true);
 assert (idx, {[1, 4, 2]; [1, 4]});
 assert (D, {[1.7321, 3.3166, 3.4641]; [2, 3.4641]}, 1e-4);
***** test
 [idx, D] = rangesearch (x, y, 4, "NSMethod", "kdtree", "SortIndices", false);
 assert (idx, {[1, 2, 4]; [1, 4]});
 assert (D, {[1.7321, 3.4641, 3.3166]; [2, 3.4641]}, 1e-4);
***** error<rangesearch: too few input arguments.> rangesearch (1)
***** error<rangesearch: too few input arguments.> rangesearch (ones (4, 5))
***** error<rangesearch: too few input arguments.> ...
 rangesearch (ones (4, 5), ones (4))
***** error<rangesearch: number of columns in X and Y must match.> ...
 rangesearch (ones (4, 5), ones (4), 1)
***** error<rangesearch: invalid NAME in optional pairs of arguments.> ...
 rangesearch (ones (4, 2), ones (3, 2), 1, "Distance", "euclidean", "some", "some")
***** error<rangesearch: only a single distance parameter can be defined.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones (1, 5), "P", 3)
***** error<rangesearch: invalid value of Minkowski Exponent.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "P", -2)
***** error<rangesearch: invalid value in Scale or the size of Scale.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "scale", ones(4,5), "distance", "euclidean")
***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ["some" "some"])
***** error<rangesearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "cov", ones(4,5), "distance", "euclidean")
***** error<rangesearch: invalid value of bucketsize.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "bucketsize", -1)
***** error<rangesearch: invalid value of bucketsize.> ...
 rangesearch (ones (4,2), ones (1,2), 1, "BucketSize", 2.5)
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "cosine")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "mahalanobis")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "correlation")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "seuclidean")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "spearman")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "hamming")
***** error<rangesearch: 'kdtree' cannot be used with the given distance metric.> ...
 rangesearch (ones (4, 5), ones (1, 5), 1, "NSmethod", "kdtree", "distance", "jaccard")
***** error<rangesearch: 'kdtree' cannot be used with custom distance functions.> ...
 rangesearch (ones (4,2), ones (1,2), 1, "Distance", @(x,y) sqrt(sum((x-y).^2)), "NSMethod", "kdtree")
39 tests, 39 passed, 0 known failure, 0 skipped
[inst/dist_fun/gamcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gamcdf.m
***** demo
 ## Plot various CDFs from the Gamma distribution
 x = 0:0.01:20;
 p1 = gamcdf (x, 1, 2);
 p2 = gamcdf (x, 2, 2);
 p3 = gamcdf (x, 3, 2);
 p4 = gamcdf (x, 5, 1);
 p5 = gamcdf (x, 9, 0.5);
 p6 = gamcdf (x, 7.5, 1);
 p7 = gamcdf (x, 0.5, 1);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
       x, p5, "-k", x, p6, "-b", x, p7, "-c")
 grid on
 legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
          "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
          "α = 0.5, β = 1"}, "location", "southeast")
 title ("Gamma CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y, u
 x = [-1, 0, 0.5, 1, 2, Inf];
 y = [0, gammainc(x(2:end), 1)];
 u = [0, NaN, NaN, 1, 0.1353352832366127, 0];
***** assert (gamcdf (x, ones (1,6), ones (1,6)), y, eps)
***** assert (gamcdf (x, ones (1,6), ones (1,6), []), y, eps)
***** assert (gamcdf (x, 1, ones (1,6)), y, eps)
***** assert (gamcdf (x, ones (1,6), 1), y, eps)
***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1), [1, NaN, NaN, 0, y(5:6)], eps)
***** assert (gamcdf (x, [0, -Inf, NaN, Inf, 1, 1], 1, "upper"), u, eps)
***** assert (gamcdf (x, 1, [0, -Inf, NaN, Inf, 1, 1]), [NaN, NaN, NaN, 0, y(5:6)], eps)
***** assert (gamcdf ([x(1:2), NaN, x(4:6)], 1, 1), [y(1:2), NaN, y(4:6)], eps)
***** assert (gamcdf ([x, NaN], 1, 1), [y, NaN])
***** assert (gamcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (gamcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** assert (gamcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** error<gamcdf: invalid number of input arguments.> gamcdf ()
***** error<gamcdf: invalid number of input arguments.> gamcdf (1)
***** error<gamcdf: invalid number of input arguments.> gamcdf (1, 2, 3, 4, 5, 6, 7)
***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, "uper")
***** error<gamcdf: invalid argument for upper tail.> gamcdf (1, 2, 3, 4, 5, "uper")
***** error<gamcdf: invalid size of covariance matrix.> gamcdf (2, 3, 4, [1, 2])
***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = gamcdf (1, 2, 3)
***** error<gamcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = gamcdf (1, 2, 3, "upper")
***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
 gamcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
 gamcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<gamcdf: invalid value for alpha.> [p, plo, pup] = ...
 gamcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
 gamcdf (ones (3), ones (2), ones (2))
***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
 gamcdf (ones (2), ones (3), ones (2))
***** error<gamcdf: X, A, and B must be of common size or scalars.> ...
 gamcdf (ones (2), ones (2), ones (3))
***** error<gamcdf: X, A, and B must not be complex.> gamcdf (i, 2, 2)
***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, i, 2)
***** error<gamcdf: X, A, and B must not be complex.> gamcdf (2, 2, i)
***** error<gamcdf: bad covariance matrix.> ...
 [p, plo, pup] = gamcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
30 tests, 30 passed, 0 known failure, 0 skipped
[inst/dist_fun/plinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/plinv.m
***** demo
 ## Plot various iCDFs from the Piecewise linear distribution
 p = 0.001:0.001:0.999;
 x1 = [0, 1, 3, 4, 7, 10];
 Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
 x2 = [0, 2, 5, 6, 7, 8];
 Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
 data1 = plinv (p, x1, Fx1);
 data2 = plinv (p, x2, Fx2);
 plot (p, data1, "-b", p, data2, "-g")
 grid on
 legend ({"x1, Fx1", "x2, Fx2"}, "location", "northwest")
 title ("Piecewise linear iCDF")
 xlabel ("probability")
 ylabel ("values in data")
***** test
 p = 0:0.2:1;
 data = plinv (p, [0, 1], [0, 1]);
 assert (data, p);
***** test
 p = 0:0.2:1;
 data = plinv (p, [0, 2], [0, 1]);
 assert (data, 2 * p);
***** test
 p = 0:0.2:1;
 data_out = 1:6;
 data = plinv (p, [0, 1], [0, 0.5]);
 assert (data, [0, 0.4, 0.8, NA, NA, NA]);
***** test
 p = 0:0.2:1;
 data_out = 1:6;
 data = plinv (p, [0, 0.5], [0, 1]);
 assert (data, [0:0.1:0.5]);
***** error<plinv: function called with too few input arguments.> plinv ()
***** error<plinv: function called with too few input arguments.> plinv (1)
***** error<plinv: function called with too few input arguments.> plinv (1, 2)
***** error<plinv: X and FX must be vectors of equal size.> ...
 plinv (1, [0, 1, 2], [0, 1])
***** error<plinv: X and FX must be at least two-elements long.> ...
 plinv (1, [0], [1])
***** error<plinv: FX must be bounded in the range> ...
 plinv (1, [0, 1, 2], [0, 1, 1.5])
***** error<plinv: FX must be bounded in the range> ...
 plinv (1, [0, 1, 2], [0, i, 1])
***** error<plinv: P, X, and FX must not be complex.> ...
 plinv (i, [0, 1, 2], [0, 0.5, 1])
***** error<plinv: P, X, and FX must not be complex.> ...
 plinv (1, [0, i, 2], [0, 0.5, 1])
***** error<plinv: P, X, and FX must not be complex.> ...
 plinv (1, [0, 1, 2], [0, 0.5i, 1])
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvtrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/mvtrnd.m
***** test
 rho = [1, 0.5; 0.5, 1];
 df = 3;
 n = 10;
 r = mvtrnd (rho, df, n);
 assert (size (r), [10, 2]);
***** test
 rho = [1, 0.5; 0.5, 1];
 df = [2; 3];
 n = 2;
 r = mvtrnd (rho, df, 2);
 assert (size (r), [2, 2]);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/dist_fun/binopdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/binopdf.m
***** demo
 ## Plot various PDFs from the binomial distribution
 x = 0:40;
 y1 = binopdf (x, 20, 0.5);
 y2 = binopdf (x, 20, 0.7);
 y3 = binopdf (x, 40, 0.5);
 plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
 grid on
 ylim ([0, 0.25])
 legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
          "n = 40, ps = 0.5"}, "location", "northeast")
 title ("Binomial PDF")
 xlabel ("values in x (number of successes)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 3];
 y = [0 1/4 1/2 1/4 0];
***** assert (binopdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), y, eps)
***** assert (binopdf (x, 2, 0.5 * ones (1, 5)), y, eps)
***** assert (binopdf (x, 2 * ones (1, 5), 0.5), y, eps)
***** assert (binopdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 0])
***** assert (binopdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 0])
***** assert (binopdf ([x, NaN], 2, 0.5), [y, NaN], eps)
***** assert (binopdf (cat (3, x, x), 2, 0.5), cat (3, y, y), eps)
***** assert (binopdf (1, 1, 1), 1)
***** assert (binopdf (0, 3, 0), 1)
***** assert (binopdf (2, 2, 1), 1)
***** assert (binopdf (1, 2, 1), 0)
***** assert (binopdf (0, 1.1, 0), NaN)
***** assert (binopdf (1, 2, -1), NaN)
***** assert (binopdf (1, 2, 1.5), NaN)
***** assert (binopdf ([], 1, 1), [])
***** assert (binopdf (1, [], 1), [])
***** assert (binopdf (1, 1, []), [])
***** assert (binopdf (ones (1, 0), 2, .5), ones(1, 0))
***** assert (binopdf (ones (0, 1), 2, .5), ones(0, 1))
***** assert (binopdf (ones (0, 1, 2), 2, .5), ones(0, 1, 2))
***** assert (binopdf (1, ones (0, 1, 2), .5), ones(0, 1, 2))
***** assert (binopdf (1, 2, ones (0, 1, 2)), ones(0, 1, 2))
***** assert (binopdf (ones (1, 0, 2), 2, .5), ones(1, 0, 2))
***** assert (binopdf (ones (1, 2, 0), 2, .5), ones(1, 2, 0))
***** assert (binopdf (ones (0, 1, 2), NaN, .5), ones(0, 1, 2))
***** assert (binopdf (ones (0, 1, 2), 2, NaN), ones(0, 1, 2))
***** assert (binopdf (single ([x, NaN]), 2, 0.5), single ([y, NaN]))
***** assert (binopdf ([x, NaN], single (2), 0.5), single ([y, NaN]))
***** assert (binopdf ([x, NaN], 2, single (0.5)), single ([y, NaN]))
***** error<binopdf: function called with too few input arguments.> binopdf ()
***** error<binopdf: function called with too few input arguments.> binopdf (1)
***** error<binopdf: function called with too few input arguments.> binopdf (1, 2)
***** error<binopdf: function called with too many inputs> binopdf (1, 2, 3, 4)
***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
 binopdf (ones (3), ones (2), ones (2))
***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
 binopdf (ones (2), ones (3), ones (2))
***** error<binopdf: X, N, and PS must be of common size or scalars.> ...
 binopdf (ones (2), ones (2), ones (3))
***** error<binopdf: X, N, and PS must not be complex.> binopdf (i, 2, 2)
***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, i, 2)
***** error<binopdf: X, N, and PS must not be complex.> binopdf (2, 2, i)
39 tests, 39 passed, 0 known failure, 0 skipped
[inst/dist_fun/hygeinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/hygeinv.m
***** demo
 ## Plot various iCDFs from the hypergeometric distribution
 p = 0.001:0.001:0.999;
 x1 = hygeinv (p, 500, 50, 100);
 x2 = hygeinv (p, 500, 60, 200);
 x3 = hygeinv (p, 500, 70, 300);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 60])
 legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
          "m = 500, k = 70, n = 300"}, "location", "northwest")
 title ("Hypergeometric iCDF")
 xlabel ("probability")
 ylabel ("values in p (number of successes)")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (hygeinv (p, 4*ones (1,5), 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 2 NaN])
***** assert (hygeinv (p, 4*ones (1,5), 2, 2), [NaN 0 1 2 NaN])
***** assert (hygeinv (p, 4, 2*ones (1,5), 2), [NaN 0 1 2 NaN])
***** assert (hygeinv (p, 4, 2, 2*ones (1,5)), [NaN 0 1 2 NaN])
***** assert (hygeinv (p, 4*[1 -1 NaN 1.1 1], 2, 2), [NaN NaN NaN NaN NaN])
***** assert (hygeinv (p, 4, 2*[1 -1 NaN 1.1 1], 2), [NaN NaN NaN NaN NaN])
***** assert (hygeinv (p, 4, 5, 2), [NaN NaN NaN NaN NaN])
***** assert (hygeinv (p, 4, 2, 2*[1 -1 NaN 1.1 1]), [NaN NaN NaN NaN NaN])
***** assert (hygeinv (p, 4, 2, 5), [NaN NaN NaN NaN NaN])
***** assert (hygeinv ([p(1:2) NaN p(4:5)], 4, 2, 2), [NaN 0 NaN 2 NaN])
***** assert (hygeinv ([p, NaN], 4, 2, 2), [NaN 0 1 2 NaN NaN])
***** assert (hygeinv (single ([p, NaN]), 4, 2, 2), single ([NaN 0 1 2 NaN NaN]))
***** assert (hygeinv ([p, NaN], single (4), 2, 2), single ([NaN 0 1 2 NaN NaN]))
***** assert (hygeinv ([p, NaN], 4, single (2), 2), single ([NaN 0 1 2 NaN NaN]))
***** assert (hygeinv ([p, NaN], 4, 2, single (2)), single ([NaN 0 1 2 NaN NaN]))
***** error<hygeinv: function called with too few input arguments.> hygeinv ()
***** error<hygeinv: function called with too few input arguments.> hygeinv (1)
***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2)
***** error<hygeinv: function called with too few input arguments.> hygeinv (1,2,3)
***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
 hygeinv (ones (2), ones (3), 1, 1)
***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
 hygeinv (1, ones (2), ones (3), 1)
***** error<hygeinv: P, T, M, and N must be of common size or scalars.> ...
 hygeinv (1, 1, ones (2), ones (3))
***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (i, 2, 2, 2)
***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, i, 2, 2)
***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, i, 2)
***** error<hygeinv: P, T, M, and N must not be complex.> hygeinv (2, 2, 2, i)
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/dist_fun/nbinrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/nbinrnd.m
***** assert (size (nbinrnd (1, 0.5)), [1, 1])
***** assert (size (nbinrnd (1, 0.5 * ones (2, 1))), [2, 1])
***** assert (size (nbinrnd (1, 0.5 * ones (2, 2))), [2, 2])
***** assert (size (nbinrnd (ones (2, 1), 0.5)), [2, 1])
***** assert (size (nbinrnd (ones (2, 2), 0.5)), [2, 2])
***** assert (size (nbinrnd (1, 0.5, 3)), [3, 3])
***** assert (size (nbinrnd (1, 0.5, [4, 1])), [4, 1])
***** assert (size (nbinrnd (1, 0.5, 4, 1)), [4, 1])
***** assert (size (nbinrnd (1, 0.5, 4, 1, 5)), [4, 1, 5])
***** assert (size (nbinrnd (1, 0.5, 0, 1)), [0, 1])
***** assert (size (nbinrnd (1, 0.5, 1, 0)), [1, 0])
***** assert (size (nbinrnd (1, 0.5, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (nbinrnd (1, 0.5, [])), [0, 0])
***** assert (size (nbinrnd (1, 0.5, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (nbinrnd (1, 0.5)), "double")
***** assert (class (nbinrnd (1, single (0.5))), "single")
***** assert (class (nbinrnd (1, single ([0.5, 0.5]))), "single")
***** assert (class (nbinrnd (single (1), 0.5)), "single")
***** assert (class (nbinrnd (single ([1, 1]), 0.5)), "single")
***** error<nbinrnd: function called with too few input arguments.> nbinrnd ()
***** error<nbinrnd: function called with too few input arguments.> nbinrnd (1)
***** error<nbinrnd: R and PS must be of common size or scalars.> ...
 nbinrnd (ones (3), ones (2))
***** error<nbinrnd: R and PS must be of common size or scalars.> ...
 nbinrnd (ones (2), ones (3))
***** error<nbinrnd: R and PS must not be complex.> nbinrnd (i, 2, 3)
***** error<nbinrnd: R and PS must not be complex.> nbinrnd (1, i, 3)
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, -1)
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, 1.2)
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, ones (2))
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, [2 -1 2])
***** error<nbinrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nbinrnd (1, 2, [2 0 2.5])
***** error<nbinrnd: dimensions must be non-negative integers.> ...
 nbinrnd (1, 2, 2, -1, 5)
***** error<nbinrnd: dimensions must be non-negative integers.> ...
 nbinrnd (1, 2, 2, 1.5, 5)
***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
 nbinrnd (2, ones (2), 3)
***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
 nbinrnd (2, ones (2), [3, 2])
***** error<nbinrnd: R and PS must be scalars or of size SZ.> ...
 nbinrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/mnrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/mnrnd.m
***** test
 n = 10;
 pk = [0.2, 0.5, 0.3];
 r = mnrnd (n, pk);
 assert (size (r), size (pk));
 assert (all (r >= 0));
 assert (all (round (r) == r));
 assert (sum (r) == n);
***** test
 n = 10 * ones (3, 1);
 pk = [0.2, 0.5, 0.3];
 r = mnrnd (n, pk);
 assert (size (r), [length(n), length(pk)]);
 assert (all (r >= 0));
 assert (all (round (r) == r));
 assert (all (sum (r, 2) == n));
***** test
 n = (1:2)';
 pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
 r = mnrnd (n, pk);
 assert (size (r), size (pk));
 assert (all (r >= 0));
 assert (all (round (r) == r));
 assert (all (sum (r, 2) == n));
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/dist_fun/gppdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gppdf.m
***** demo
 ## Plot various PDFs from the generalized Pareto distribution
 x = 0:0.001:5;
 y1 = gppdf (x, 1, 1, 0);
 y2 = gppdf (x, 5, 1, 0);
 y3 = gppdf (x, 20, 1, 0);
 y4 = gppdf (x, 1, 2, 0);
 y5 = gppdf (x, 5, 2, 0);
 y6 = gppdf (x, 20, 2, 0);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
       x, y4, "-c", x, y5, "-m", x, y6, "-k")
 grid on
 xlim ([0, 5])
 ylim ([0, 1])
 legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
          "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
          "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
         "location", "northeast")
 title ("Generalized Pareto PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y1, y2, y3
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y1 = [0, 0, 1, 0.6065306597126334, 0.36787944117144233, 0];
 y2 = [0, 0, 1, 4/9, 1/4, 0];
 y3 = [0, 0, 1, 1, 1, 0];
***** assert (gppdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
***** assert (gppdf (x, 0, 1, zeros (1,6)), y1, eps)
***** assert (gppdf (x, 0, ones (1,6), 0), y1, eps)
***** assert (gppdf (x, zeros (1,6), 1, 0), y1, eps)
***** assert (gppdf (x, 0, 1, 0), y1, eps)
***** assert (gppdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)])
***** assert (gppdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)])
***** assert (gppdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)])
***** assert (gppdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)])
***** assert (gppdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
***** assert (gppdf (x, 1, 1, zeros (1,6)), y2, eps)
***** assert (gppdf (x, 1, ones (1,6), 0), y2, eps)
***** assert (gppdf (x, ones (1,6), 1, 0), y2, eps)
***** assert (gppdf (x, 1, 1, 0), y2, eps)
***** assert (gppdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)])
***** assert (gppdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)])
***** assert (gppdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)])
***** assert (gppdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)])
***** assert (gppdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
***** assert (gppdf (x, -1, 1, zeros (1,6)), y3, eps)
***** assert (gppdf (x, -1, ones (1,6), 0), y3, eps)
***** assert (gppdf (x, -ones (1,6), 1, 0), y3, eps)
***** assert (gppdf (x, -1, 1, 0), y3, eps)
***** assert (gppdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)])
***** assert (gppdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)])
***** assert (gppdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)])
***** assert (gppdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)])
***** assert (gppdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]))
***** assert (gppdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]))
***** assert (gppdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]))
***** assert (gppdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]))
***** assert (gppdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]))
***** assert (gppdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]))
***** assert (gppdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]))
***** assert (gppdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]))
***** assert (gppdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]))
***** assert (gppdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]))
***** assert (gppdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]))
***** assert (gppdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]))
***** error<gpcdf: function called with too few input arguments.> gpcdf ()
***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (3), ones (2), ones(2), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (3), ones(2), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (2), ones(3), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (2), ones(2), ones(3))
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
51 tests, 51 passed, 0 known failure, 0 skipped
[inst/dist_fun/nbincdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/nbincdf.m
***** demo
 ## Plot various CDFs from the negative binomial distribution
 x = 0:50;
 p1 = nbincdf (x, 2, 0.15);
 p2 = nbincdf (x, 5, 0.2);
 p3 = nbincdf (x, 4, 0.4);
 p4 = nbincdf (x, 10, 0.3);
 plot (x, p1, "*r", x, p2, "*g", x, p3, "*k", x, p4, "*m")
 grid on
 xlim ([0, 40])
 legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
          "r = 10, ps = 0.3"}, "location", "southeast")
 title ("Negative binomial CDF")
 xlabel ("values in x (number of failures)")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 1 2 Inf];
 y = [0 1/2 3/4 7/8 1];
***** assert (nbincdf (x, ones (1,5), 0.5*ones (1,5)), y)
***** assert (nbincdf (x, 1, 0.5*ones (1,5)), y)
***** assert (nbincdf (x, ones (1,5), 0.5), y)
***** assert (nbincdf (x, ones (1,5), 0.5, "upper"), 1 - y, eps)
***** assert (nbincdf ([x(1:3) 0 x(5)], [0 1 NaN 1.5 Inf], 0.5), ...
 [NaN 1/2 NaN nbinpdf(0,1.5,0.5) NaN], eps)
***** assert (nbincdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
***** assert (nbincdf ([x(1:2) NaN x(4:5)], 1, 0.5), [y(1:2) NaN y(4:5)])
***** assert (nbincdf ([x, NaN], 1, 0.5), [y, NaN])
***** assert (nbincdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
***** assert (nbincdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
***** assert (nbincdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
***** error<nbincdf: function called with too few input arguments.> nbincdf ()
***** error<nbincdf: function called with too few input arguments.> nbincdf (1)
***** error<nbincdf: function called with too few input arguments.> nbincdf (1, 2)
***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, 4)
***** error<nbincdf: invalid argument for upper tail.> nbincdf (1, 2, 3, "some")
***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
 nbincdf (ones (3), ones (2), ones (2))
***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
 nbincdf (ones (2), ones (3), ones (2))
***** error<nbincdf: X, R, and PS must be of common size or scalars.> ...
 nbincdf (ones (2), ones (2), ones (3))
***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (i, 2, 2)
***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, i, 2)
***** error<nbincdf: X, R, and PS must not be complex.> nbincdf (2, 2, i)
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/dist_fun/hygernd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/hygernd.m
***** assert (size (hygernd (4, 2, 2)), [1, 1])
***** assert (size (hygernd (4 * ones (2, 1), 2,2)), [2, 1])
***** assert (size (hygernd (4 * ones (2, 2), 2,2)), [2, 2])
***** assert (size (hygernd (4, 2 * ones (2, 1), 2)), [2, 1])
***** assert (size (hygernd (4, 2 * ones (2, 2), 2)), [2, 2])
***** assert (size (hygernd (4, 2, 2 * ones (2, 1))), [2, 1])
***** assert (size (hygernd (4, 2, 2 * ones (2, 2))), [2, 2])
***** assert (size (hygernd (4, 2, 2, 3)), [3, 3])
***** assert (size (hygernd (4, 2, 2, [4, 1])), [4, 1])
***** assert (size (hygernd (4, 2, 2, 4, 1)), [4, 1])
***** assert (size (hygernd (4, 2, 2, [])), [0, 0])
***** assert (size (hygernd (4, 2, 2, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (hygernd (4, 2, 2)), "double")
***** assert (class (hygernd (single (4), 2, 2)), "single")
***** assert (class (hygernd (single ([4, 4]), 2, 2)), "single")
***** assert (class (hygernd (4, single (2), 2)), "single")
***** assert (class (hygernd (4, single ([2, 2]),2)), "single")
***** assert (class (hygernd (4, 2, single (2))), "single")
***** assert (class (hygernd (4, 2, single ([2, 2]))), "single")
***** error<hygernd: function called with too few input arguments.> hygernd ()
***** error<hygernd: function called with too few input arguments.> hygernd (1)
***** error<hygernd: function called with too few input arguments.> hygernd (1, 2)
***** error<hygernd: T, M, and N must be of common size or scalars.> ...
 hygernd (ones (3), ones (2), ones (2))
***** error<hygernd: T, M, and N must be of common size or scalars.> ...
 hygernd (ones (2), ones (3), ones (2))
***** error<hygernd: T, M, and N must be of common size or scalars.> ...
 hygernd (ones (2), ones (2), ones (3))
***** error<hygernd: T, M, and N must not be complex.> hygernd (i, 2, 3)
***** error<hygernd: T, M, and N must not be complex.> hygernd (1, i, 3)
***** error<hygernd: T, M, and N must not be complex.> hygernd (1, 2, i)
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, -1)
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, 1.2)
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, ones (2))
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, [2 -1 2])
***** error<hygernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hygernd (1, 2, 3, [2 0 2.5])
***** error<hygernd: dimensions must be non-negative integers.> ...
 hygernd (1, 2, 3, 2, -1, 5)
***** error<hygernd: dimensions must be non-negative integers.> ...
 hygernd (1, 2, 3, 2, 1.5, 5)
***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
 hygernd (2, ones (2), 2, 3)
***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
 hygernd (2, ones (2), 2, [3, 2])
***** error<hygernd: T, M, and N must be scalars or of size SZ.> ...
 hygernd (2, ones (2), 2, 3, 2)
38 tests, 38 passed, 0 known failure, 0 skipped
[inst/dist_fun/gevinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gevinv.m
***** demo
 ## Plot various iCDFs from the generalized extreme value distribution
 p = 0.001:0.001:0.999;
 x1 = gevinv (p, 1, 1, 1);
 x2 = gevinv (p, 0.5, 1, 1);
 x3 = gevinv (p, 1, 1, 5);
 x4 = gevinv (p, 1, 2, 5);
 x5 = gevinv (p, 1, 5, 5);
 x6 = gevinv (p, 1, 0.5, 5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
       p, x4, "-c", p, x5, "-m", p, x6, "-k")
 grid on
 ylim ([-1, 10])
 legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
          "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
          "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
         "location", "northwest")
 title ("Generalized extreme value iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 p = 0.1:0.1:0.9;
 k = 0;
 sigma = 1;
 mu = 0;
 x = gevinv (p, k, sigma, mu);
 c = gevcdf(x, k, sigma, mu);
 assert (c, p, 0.001);
***** test
 p = 0.1:0.1:0.9;
 k = 1;
 sigma = 1;
 mu = 0;
 x = gevinv (p, k, sigma, mu);
 c = gevcdf(x, k, sigma, mu);
 assert (c, p, 0.001);
***** test
 p = 0.1:0.1:0.9;
 k = 0.3;
 sigma = 1;
 mu = 0;
 x = gevinv (p, k, sigma, mu);
 c = gevcdf(x, k, sigma, mu);
 assert (c, p, 0.001);
***** error<gevinv: function called with too few input arguments.> gevinv ()
***** error<gevinv: function called with too few input arguments.> gevinv (1)
***** error<gevinv: function called with too few input arguments.> gevinv (1, 2)
***** error<gevinv: function called with too few input arguments.> gevinv (1, 2, 3)
***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
 gevinv (ones (3), ones (2), ones(2), ones(2))
***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
 gevinv (ones (2), ones (3), ones(2), ones(2))
***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
 gevinv (ones (2), ones (2), ones(3), ones(2))
***** error<gevinv: P, K, SIGMA, and MU must be of common size or scalars.> ...
 gevinv (ones (2), ones (2), ones(2), ones(3))
***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (i, 2, 3, 4)
***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, i, 3, 4)
***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, i, 4)
***** error<gevinv: P, K, SIGMA, and MU must not be complex.> gevinv (1, 2, 3, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/gumbelrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gumbelrnd.m
***** assert (size (gumbelrnd (1, 1)), [1 1])
***** assert (size (gumbelrnd (1, ones (2,1))), [2, 1])
***** assert (size (gumbelrnd (1, ones (2,2))), [2, 2])
***** assert (size (gumbelrnd (ones (2,1), 1)), [2, 1])
***** assert (size (gumbelrnd (ones (2,2), 1)), [2, 2])
***** assert (size (gumbelrnd (1, 1, 3)), [3, 3])
***** assert (size (gumbelrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (gumbelrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (gumbelrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (gumbelrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (gumbelrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (gumbelrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (gumbelrnd (1, 1, [])), [0, 0])
***** assert (size (gumbelrnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (gumbelrnd (1, 1)), "double")
***** assert (class (gumbelrnd (1, single (1))), "single")
***** assert (class (gumbelrnd (1, single ([1, 1]))), "single")
***** assert (class (gumbelrnd (single (1), 1)), "single")
***** assert (class (gumbelrnd (single ([1, 1]), 1)), "single")
***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd ()
***** error<gumbelrnd: function called with too few input arguments.> gumbelrnd (1)
***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
 gumbelrnd (ones (3), ones (2))
***** error<gumbelrnd: MU and BETA must be of common size or scalars.> ...
 gumbelrnd (ones (2), ones (3))
***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (i, 2, 3)
***** error<gumbelrnd: MU and BETA must not be complex.> gumbelrnd (1, i, 3)
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, -1)
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, 1.2)
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, ones (2))
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, [2 -1 2])
***** error<gumbelrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gumbelrnd (1, 2, [2 0 2.5])
***** error<gumbelrnd: dimensions must be non-negative integers.> ...
 gumbelrnd (1, 2, 2, -1, 5)
***** error<gumbelrnd: dimensions must be non-negative integers.> ...
 gumbelrnd (1, 2, 2, 1.5, 5)
***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
 gumbelrnd (2, ones (2), 3)
***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
 gumbelrnd (2, ones (2), [3, 2])
***** error<gumbelrnd: MU and BETA must be scalars or of size SZ.> ...
 gumbelrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/frnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/frnd.m
***** assert (size (frnd (1, 1)), [1 1])
***** assert (size (frnd (1, ones (2,1))), [2, 1])
***** assert (size (frnd (1, ones (2,2))), [2, 2])
***** assert (size (frnd (ones (2,1), 1)), [2, 1])
***** assert (size (frnd (ones (2,2), 1)), [2, 2])
***** assert (size (frnd (1, 1, 3)), [3, 3])
***** assert (size (frnd (1, 1, [4, 1])), [4, 1])
***** assert (size (frnd (1, 1, 4, 1)), [4, 1])
***** assert (size (frnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (frnd (1, 1, 0, 1)), [0, 1])
***** assert (size (frnd (1, 1, 1, 0)), [1, 0])
***** assert (size (frnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (frnd (1, 1, [])), [0, 0])
***** assert (size (frnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (frnd (1, 1)), "double")
***** assert (class (frnd (1, single (1))), "single")
***** assert (class (frnd (1, single ([1, 1]))), "single")
***** assert (class (frnd (single (1), 1)), "single")
***** assert (class (frnd (single ([1, 1]), 1)), "single")
***** error<frnd: function called with too few input arguments.> frnd ()
***** error<frnd: function called with too few input arguments.> frnd (1)
***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
 frnd (ones (3), ones (2))
***** error<frnd: DF1 and DF2 must be of common size or scalars.> ...
 frnd (ones (2), ones (3))
***** error<frnd: DF1 and DF2 must not be complex.> frnd (i, 2, 3)
***** error<frnd: DF1 and DF2 must not be complex.> frnd (1, i, 3)
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, -1)
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, 1.2)
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, ones (2))
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, [2 -1 2])
***** error<frnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 frnd (1, 2, [2 0 2.5])
***** error<frnd: dimensions must be non-negative integers.> ...
 frnd (1, 2, 2, -1, 5)
***** error<frnd: dimensions must be non-negative integers.> ...
 frnd (1, 2, 2, 1.5, 5)
***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
 frnd (2, ones (2), 3)
***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
 frnd (2, ones (2), [3, 2])
***** error<frnd: DF1 and DF2 must be scalars or of size SZ.> ...
 frnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/invgcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/invgcdf.m
***** demo
 ## Plot various CDFs from the inverse Gaussian distribution
 x = 0:0.001:3;
 p1 = invgcdf (x, 1, 0.2);
 p2 = invgcdf (x, 1, 1);
 p3 = invgcdf (x, 1, 3);
 p4 = invgcdf (x, 3, 0.2);
 p5 = invgcdf (x, 3, 1);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-y")
 grid on
 xlim ([0, 3])
 legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
          "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "southeast")
 title ("Inverse Gaussian CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p1, p1u, y2, y2u, y3, y3u
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 p1 = [0, 0, 0, 0.3650, 0.6681, 1];
 p1u = [1, 1, 1, 0.6350, 0.3319, 0];
***** assert (invgcdf (x, ones (1,6), ones (1,6)), p1, 1e-4)
***** assert (invgcdf (x, 1, 1), p1, 1e-4)
***** assert (invgcdf (x, 1, ones (1,6)), p1, 1e-4)
***** assert (invgcdf (x, ones (1,6), 1), p1, 1e-4)
***** assert (invgcdf (x, 1, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (invgcdf (x, [1, 1, 1, NaN, 1, 1], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (invgcdf ([x(1:3), NaN, x(5:6)], 1, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (invgcdf (x, ones (1,6), ones (1,6), "upper"), p1u, 1e-4)
***** assert (invgcdf (x, 1, 1, "upper"), p1u, 1e-4)
***** assert (invgcdf (x, 1, ones (1,6), "upper"), p1u, 1e-4)
***** assert (invgcdf (x, ones (1,6), 1, "upper"), p1u, 1e-4)
***** assert (class (invgcdf (single ([x, NaN]), 1, 1)), "single")
***** assert (class (invgcdf ([x, NaN], 1, single (1))), "single")
***** assert (class (invgcdf ([x, NaN], single (1), 1)), "single")
***** error<invgcdf: function called with too few input arguments.> invgcdf ()
***** error<invgcdf: function called with too few input arguments.> invgcdf (1)
***** error<invgcdf: function called with too few input arguments.> invgcdf (1, 2)
***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, "tail")
***** error<invgcdf: invalid argument for upper tail.> invgcdf (1, 2, 3, 5)
***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgcdf (ones (3), ones (2), ones(2))
***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgcdf (ones (2), ones (3), ones(2))
***** error<invgcdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgcdf (ones (2), ones (2), ones(3))
***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (i, 2, 3)
***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, i, 3)
***** error<invgcdf: X, MU, and LAMBDA must not be complex.> invgcdf (1, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/invgrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/invgrnd.m
***** assert (size (invgrnd (1, 1, 1)), [1, 1])
***** assert (size (invgrnd (1, 1, 2)), [2, 2])
***** assert (size (invgrnd (1, 1, [2, 1])), [2, 1])
***** assert (size (invgrnd (1, zeros (2, 2))), [2, 2])
***** assert (size (invgrnd (1, ones (2, 1))), [2, 1])
***** assert (size (invgrnd (1, ones (2, 2))), [2, 2])
***** assert (size (invgrnd (ones (2, 1), 1)), [2, 1])
***** assert (size (invgrnd (ones (2, 2), 1)), [2, 2])
***** assert (size (invgrnd (1, 1, 3)), [3, 3])
***** assert (size (invgrnd (1, 1, [4 1])), [4, 1])
***** assert (size (invgrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (invgrnd (1, 1, [])), [0, 0])
***** assert (size (invgrnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** test
 r =  invgrnd (1, [1, 0, -1]);
 assert (r([2:3]), [NaN, NaN])
***** assert (class (invgrnd (1, 0)), "double")
***** assert (class (invgrnd (1, single (0))), "single")
***** assert (class (invgrnd (1, single ([0, 0]))), "single")
***** assert (class (invgrnd (1, single (1))), "single")
***** assert (class (invgrnd (1, single ([1, 1]))), "single")
***** assert (class (invgrnd (single (1), 1)), "single")
***** assert (class (invgrnd (single ([1, 1]), 1)), "single")
***** error<invgrnd: function called with too few input arguments.> invgrnd ()
***** error<invgrnd: function called with too few input arguments.> invgrnd (1)
***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
 invgrnd (ones (3), ones (2))
***** error<invgrnd: MU and LAMBDA must be of common size or scalars.> ...
 invgrnd (ones (2), ones (3))
***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (i, 2, 3)
***** error<invgrnd: MU and LAMBDA must not be complex.> invgrnd (1, i, 3)
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, -1)
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, 1.2)
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, ones (2))
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, [2 -1 2])
***** error<invgrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 invgrnd (1, 2, [2 0 2.5])
***** error<invgrnd: dimensions must be non-negative integers.> ...
 invgrnd (1, 2, 2, -1, 5)
***** error<invgrnd: dimensions must be non-negative integers.> ...
 invgrnd (1, 2, 2, 1.5, 5)
***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
 invgrnd (2, ones (2), 3)
***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
 invgrnd (2, ones (2), [3, 2])
***** error<invgrnd: MU and LAMBDA must be scalars or of size SZ.> ...
 invgrnd (2, ones (2), 3, 2)
37 tests, 37 passed, 0 known failure, 0 skipped
[inst/dist_fun/iwishrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/iwishrnd.m
***** assert(size (iwishrnd (1,2,1)), [1, 1]);
***** assert(size (iwishrnd ([],2,1)), [1, 1]);
***** assert(size (iwishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
***** assert(size (iwishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
***** error iwishrnd ()
***** error iwishrnd (1)
***** error iwishrnd ([-3 1; 1 3],1)
***** error iwishrnd ([1; 1],1)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/bisapdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/bisapdf.m
***** demo
 ## Plot various PDFs from the Birnbaum-Saunders distribution
 x = 0.01:0.01:4;
 y1 = bisapdf (x, 1, 0.5);
 y2 = bisapdf (x, 1, 1);
 y3 = bisapdf (x, 1, 2);
 y4 = bisapdf (x, 1, 5);
 y5 = bisapdf (x, 1, 10);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 1.5])
 legend ({"β = 1 ,γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
          "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northeast")
 title ("Birnbaum-Saunders PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Plot various PDFs from the Birnbaum-Saunders distribution
 x = 0.01:0.01:6;
 y1 = bisapdf (x, 1, 0.3);
 y2 = bisapdf (x, 2, 0.3);
 y3 = bisapdf (x, 1, 0.5);
 y4 = bisapdf (x, 3, 0.5);
 y5 = bisapdf (x, 5, 0.5);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 1.5])
 legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
          "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northeast")
 title ("Birnbaum-Saunders CDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 0.3989422804014327, 0.1647717335503959, 0];
***** assert (bisapdf (x, ones (1,5), ones (1,5)), y, eps)
***** assert (bisapdf (x, 1, 1), y, eps)
***** assert (bisapdf (x, 1, ones (1,5)), y, eps)
***** assert (bisapdf (x, ones (1,5), 1), y, eps)
***** assert (bisapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
***** assert (bisapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (bisapdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (bisapdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (bisapdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** assert (bisapdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** error<bisapdf: function called with too few input arguments.> bisapdf ()
***** error<bisapdf: function called with too few input arguments.> bisapdf (1)
***** error<bisapdf: function called with too few input arguments.> bisapdf (1, 2)
***** error<bisapdf: function called with too many inputs> bisapdf (1, 2, 3, 4)
***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisapdf (ones (3), ones (2), ones(2))
***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisapdf (ones (2), ones (3), ones(2))
***** error<bisapdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisapdf (ones (2), ones (2), ones(3))
***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (i, 4, 3)
***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, i, 3)
***** error<bisapdf: X, BETA, and GAMMA must not be complex.> bisapdf (1, 4, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/poissinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/poissinv.m
***** demo
 ## Plot various iCDFs from the Poisson distribution
 p = 0.001:0.001:0.999;
 x1 = poissinv (p, 13);
 x2 = poissinv (p, 4);
 x3 = poissinv (p, 10);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 20])
 legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northwest")
 title ("Poisson iCDF")
 xlabel ("probability")
 ylabel ("values in x (number of occurences)")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (poissinv (p, ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (poissinv (p, 1), [NaN 0 1 Inf NaN])
***** assert (poissinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
***** assert (poissinv ([p(1:2) NaN p(4:5)], 1), [NaN 0 NaN Inf NaN])
***** assert (poissinv ([p, NaN], 1), [NaN 0 1 Inf NaN NaN])
***** assert (poissinv (single ([p, NaN]), 1), single ([NaN 0 1 Inf NaN NaN]))
***** assert (poissinv ([p, NaN], single (1)), single ([NaN 0 1 Inf NaN NaN]))
***** error<poissinv: function called with too few input arguments.> poissinv ()
***** error<poissinv: function called with too few input arguments.> poissinv (1)
***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
 poissinv (ones (3), ones (2))
***** error<poissinv: P and LAMBDA must be of common size or scalars.> ...
 poissinv (ones (2), ones (3))
***** error<poissinv: P and LAMBDA must not be complex.> poissinv (i, 2)
***** error<poissinv: P and LAMBDA must not be complex.> poissinv (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/loglrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/loglrnd.m
***** assert (size (loglrnd (1, 1)), [1, 1])
***** assert (size (loglrnd (1, ones (2, 1))), [2, 1])
***** assert (size (loglrnd (1, ones (2, 2))), [2, 2])
***** assert (size (loglrnd (ones (2, 1), 1)), [2, 1])
***** assert (size (loglrnd (ones (2, 2), 1)), [2, 2])
***** assert (size (loglrnd (1, 1, 3)), [3, 3])
***** assert (size (loglrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (loglrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (loglrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (loglrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (loglrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (loglrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (loglrnd (1, 1, [])), [0, 0])
***** assert (size (loglrnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (loglrnd (1, 1)), "double")
***** assert (class (loglrnd (1, single (1))), "single")
***** assert (class (loglrnd (1, single ([1, 1]))), "single")
***** assert (class (loglrnd (single (1), 1)), "single")
***** assert (class (loglrnd (single ([1, 1]), 1)), "single")
***** error<loglrnd: function called with too few input arguments.> loglrnd ()
***** error<loglrnd: function called with too few input arguments.> loglrnd (1)
***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
 loglrnd (ones (3), ones (2))
***** error<loglrnd: MU and SIGMA must be of common size or scalars.> ...
 loglrnd (ones (2), ones (3))
***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (i, 2, 3)
***** error<loglrnd: MU and SIGMA must not be complex.> loglrnd (1, i, 3)
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, -1)
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, 1.2)
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, ones (2))
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, [2 -1 2])
***** error<loglrnd: SZ must be mu scalar or mu row vector of non-negative integers.> ...
 loglrnd (1, 2, [2 0 2.5])
***** error<loglrnd: dimensions must be non-negative integers.> ...
 loglrnd (1, 2, 2, -1, 5)
***** error<loglrnd: dimensions must be non-negative integers.> ...
 loglrnd (1, 2, 2, 1.5, 5)
***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
 loglrnd (2, ones (2), 3)
***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
 loglrnd (2, ones (2), [3, 2])
***** error<loglrnd: MU and SIGMA must be scalars or of size SZ.> ...
 loglrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/betapdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/betapdf.m
***** demo
 ## Plot various PDFs from the Beta distribution
 x = 0.001:0.001:0.999;
 y1 = betapdf (x, 0.5, 0.5);
 y2 = betapdf (x, 5, 1);
 y3 = betapdf (x, 1, 3);
 y4 = betapdf (x, 2, 2);
 y5 = betapdf (x, 2, 5);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 2.5])
 legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
          "α = 2, β = 2", "α = 2, β = 5"}, "location", "north")
 title ("Beta PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 2];
 y = [0 2 1 0 0];
***** assert (betapdf (x, ones (1, 5), 2 * ones (1, 5)), y)
***** assert (betapdf (x, 1, 2 * ones (1, 5)), y)
***** assert (betapdf (x, ones (1, 5), 2), y)
***** assert (betapdf (x, [0 NaN 1 1 1], 2), [NaN NaN y(3:5)])
***** assert (betapdf (x, 1, 2 * [0 NaN 1 1 1]), [NaN NaN y(3:5)])
***** assert (betapdf ([x, NaN], 1, 2), [y, NaN])
***** assert (betapdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
***** assert (betapdf ([x, NaN], single (1), 2), single ([y, NaN]))
***** assert (betapdf ([x, NaN], 1, single (2)), single ([y, NaN]))
***** test
 x = rand (10,1);
 y = 1 ./ (pi * sqrt (x .* (1 - x)));
 assert (betapdf (x, 1/2, 1/2), y, 1e-12);
***** assert (betapdf (0.5, 1000, 1000), 35.678, 1e-3)
***** error<betapdf: function called with too few input arguments.> betapdf ()
***** error<betapdf: function called with too few input arguments.> betapdf (1)
***** error<betapdf: function called with too few input arguments.> betapdf (1,2)
***** error<betapdf: function called with too many inputs> betapdf (1,2,3,4)
***** error<betapdf: X, A, and B must be of common size or scalars.> ...
 betapdf (ones (3), ones (2), ones (2))
***** error<betapdf: X, A, and B must be of common size or scalars.> ...
 betapdf (ones (2), ones (3), ones (2))
***** error<betapdf: X, A, and B must be of common size or scalars.> ...
 betapdf (ones (2), ones (2), ones (3))
***** error<betapdf: X, A, and B must not be complex.> betapdf (i, 2, 2)
***** error<betapdf: X, A, and B must not be complex.> betapdf (2, i, 2)
***** error<betapdf: X, A, and B must not be complex.> betapdf (2, 2, i)
21 tests, 21 passed, 0 known failure, 0 skipped
[inst/dist_fun/geocdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/geocdf.m
***** demo
 ## Plot various CDFs from the geometric distribution
 x = 0:10;
 p1 = geocdf (x, 0.2);
 p2 = geocdf (x, 0.5);
 p3 = geocdf (x, 0.7);
 plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
 grid on
 xlim ([0, 10])
 legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "southeast")
 title ("Geometric CDF")
 xlabel ("values in x (number of failures)")
 ylabel ("probability")
***** test
 p = geocdf ([1, 2, 3, 4], 0.25);
 assert (p(1), 0.4375000000, 1e-14);
 assert (p(2), 0.5781250000, 1e-14);
 assert (p(3), 0.6835937500, 1e-14);
 assert (p(4), 0.7626953125, 1e-14);
***** test
 p = geocdf ([1, 2, 3, 4], 0.25, "upper");
 assert (p(1), 0.5625000000, 1e-14);
 assert (p(2), 0.4218750000, 1e-14);
 assert (p(3), 0.3164062500, 1e-14);
 assert (p(4), 0.2373046875, 1e-14);
***** shared x, p
 x = [-1 0 1 Inf];
 p = [0 0.5 0.75 1];
***** assert (geocdf (x, 0.5*ones (1,4)), p)
***** assert (geocdf (x, 0.5), p)
***** assert (geocdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN p(4)])
***** assert (geocdf ([x(1:2) NaN x(4)], 0.5), [p(1:2) NaN p(4)])
***** assert (geocdf ([x, NaN], 0.5), [p, NaN])
***** assert (geocdf (single ([x, NaN]), 0.5), single ([p, NaN]))
***** assert (geocdf ([x, NaN], single (0.5)), single ([p, NaN]))
***** error<geocdf: function called with too few input arguments.> geocdf ()
***** error<geocdf: function called with too few input arguments.> geocdf (1)
***** error<geocdf: X and PS must be of common size or scalars.> ...
 geocdf (ones (3), ones (2))
***** error<geocdf: X and PS must be of common size or scalars.> ...
 geocdf (ones (2), ones (3))
***** error<geocdf: X and PS must not be complex.> geocdf (i, 2)
***** error<geocdf: X and PS must not be complex.> geocdf (2, i)
***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, "tail")
***** error<geocdf: invalid argument for upper tail.> geocdf (2, 3, 5)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncfinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/ncfinv.m
***** demo
 ## Plot various iCDFs from the noncentral F distribution
 p = 0.001:0.001:0.999;
 x1 = ncfinv (p, 2, 5, 1);
 x2 = ncfinv (p, 2, 5, 2);
 x3 = ncfinv (p, 5, 10, 1);
 x4 = ncfinv (p, 10, 20, 10);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
 grid on
 ylim ([0, 5])
 legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
          "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
         "location", "northwest")
 title ("Noncentral F iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** demo
 ## Compare the noncentral F iCDF with LAMBDA = 10 to the F iCDF with the
 ## same number of numerator and denominator degrees of freedom (5, 20)

 p = 0.001:0.001:0.999;
 x1 = ncfinv (p, 5, 20, 10);
 x2 = finv (p, 5, 20);
 plot (p, x1, "-", p, x2, "-");
 grid on
 ylim ([0, 10])
 legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northwest")
 title ("Noncentral F vs F quantile functions")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 x = [0,0.1775,0.3864,0.6395,0.9564,1.3712,1.9471,2.8215,4.3679,8.1865,Inf];
 assert (ncfinv ([0:0.1:1], 2, 3, 1), x, 1e-4);
***** test
 x = [0,0.7492,1.3539,2.0025,2.7658,3.7278,5.0324,6.9826,10.3955,18.7665,Inf];
 assert (ncfinv ([0:0.1:1], 2, 3, 5), x, 1e-4);
***** test
 x = [0,0.2890,0.8632,1.5653,2.4088,3.4594,4.8442,6.8286,10.0983,17.3736,Inf];
 assert (ncfinv ([0:0.1:1], 1, 4, 3), x, 1e-4);
***** test
 x = [0.078410, 0.212716, 0.288618, 0.335752, 0.367963, 0.391460];
 assert (ncfinv (0.05, [1, 2, 3, 4, 5, 6], 10, 3), x, 1e-6);
***** test
 x = [0.2574, 0.2966, 0.3188, 0.3331, 0.3432, 0.3507];
 assert (ncfinv (0.05, 5, [1, 2, 3, 4, 5, 6], 3), x, 1e-4);
***** test
 x = [1.6090, 1.8113, 1.9215, 1.9911, NaN, 2.0742];
 assert (ncfinv (0.05, 1, [1, 2, 3, 4, -1, 6], 10), x, 1e-4);
***** test
 assert (ncfinv (0.996, 3, 5, 8), 58.0912074080671, 4e-12);
***** error<ncfinv: function called with too few input arguments.> ncfinv ()
***** error<ncfinv: function called with too few input arguments.> ncfinv (1)
***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2)
***** error<ncfinv: function called with too few input arguments.> ncfinv (1, 2, 3)
***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfinv (ones (3), ones (2), ones (2), ones (2))
***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfinv (ones (2), ones (3), ones (2), ones (2))
***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfinv (ones (2), ones (2), ones (3), ones (2))
***** error<ncfinv: P, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfinv (ones (2), ones (2), ones (2), ones (3))
***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (i, 2, 2, 2)
***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, i, 2, 2)
***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, i, 2)
***** error<ncfinv: P, DF1, DF2, and LAMBDA must not be complex.> ncfinv (2, 2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvncdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/mvncdf.m
***** demo
 mu = [1, -1];
 Sigma = [0.9, 0.4; 0.4, 0.3];
 [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 X = [X1(:), X2(:)];
 p = mvncdf (X, mu, Sigma);
 Z = reshape (p, 25, 25);
 surf (X1, X2, Z);
 title ("Bivariate Normal Distribution");
 ylabel "X1"
 xlabel "X2"
***** demo
 mu = [0, 0];
 Sigma = [0.25, 0.3; 0.3, 1];
 p = mvncdf ([0 0], [1 1], mu, Sigma);
 x1 = -3:.2:3;
 x2 = -3:.2:3;
 [X1, X2] = meshgrid (x1, x2);
 X = [X1(:), X2(:)];
 p = mvnpdf (X, mu, Sigma);
 p = reshape (p, length (x2), length (x1));
 contour (x1, x2, p, [0.0001, 0.001, 0.01, 0.05, 0.15, 0.25, 0.35]);
 xlabel ("x");
 ylabel ("p");
 title ("Probability over Rectangular Region");
 line ([0, 0, 1, 1, 0], [1, 0, 0, 1, 1], "Linestyle", "--", "Color", "k");
***** test
 fD = (-2:2)';
 X = repmat (fD, 1, 4);
 p = mvncdf (X);
 assert (p, [0; 0.0006; 0.0625; 0.5011; 0.9121], ones (5, 1) * 1e-4);
***** test
 mu = [1, -1];
 Sigma = [0.9, 0.4; 0.4, 0.3];
 [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 X = [X1(:), X2(:)];
 p = mvncdf (X, mu, Sigma);
 p_out = [0.00011878988774500, 0.00034404112322371, ...
          0.00087682502191813, 0.00195221905058185, ...
          0.00378235566873474, 0.00638175749734415, ...
          0.00943764224329656, 0.01239164888125426, ...
          0.01472750274376648, 0.01623228313374828]';
 assert (p([1:10]), p_out, 1e-16);
***** test
 mu = [1, -1];
 Sigma = [0.9, 0.4; 0.4, 0.3];
 [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 X = [X1(:), X2(:)];
 p = mvncdf (X, mu, Sigma);
 p_out = [0.8180695783608276, 0.8854485749482751, ...
          0.9308108777385832, 0.9579855743025508, ...
          0.9722897881414742, 0.9788150170059926, ...
          0.9813597788804785, 0.9821977956568989, ...
          0.9824283794464095, 0.9824809345614861]';
 assert (p([616:625]), p_out, 3e-16);
***** test
 mu = [0, 0];
 Sigma = [0.25, 0.3; 0.3, 1];
 [p, err] = mvncdf ([0, 0], [1, 1], mu, Sigma);
 assert (p, 0.2097424404755626, 1e-16);
 assert (err, 1e-08);
***** test
 x = [1 2];
 mu = [0.5 1.5];
 sigma = [1.0, 0.5; 0.5, 1.0];
 p = mvncdf (x, mu, sigma);
 assert (p, 0.546244443857090, 1e-15);
***** test
 x = [1 2];
 mu = [0.5 1.5];
 sigma = [1.0, 0.5; 0.5, 1.0];
 a = [-inf 0];
 p = mvncdf (a, x, mu, sigma);
 assert (p, 0.482672935215631, 1e-15);
***** error p = mvncdf (randn (25,26), [], eye (26));
***** error p = mvncdf (randn (25,8), [], eye (9));
***** error p = mvncdf (randn (25,4), randn (25,5), [], eye (4));
***** error p = mvncdf (randn (25,4), randn (25,4), [2, 3; 2, 3], eye (4));
***** error p = mvncdf (randn (25,4), randn (25,4), ones (1, 5), eye (4));
***** error p = mvncdf ([-inf, 0], [1, 2], [0.5, 1.5], [1.0, 0.5; 0.5, 1.0], option)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncfpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/ncfpdf.m
***** demo
 ## Plot various PDFs from the noncentral F distribution
 x = 0:0.01:5;
 y1 = ncfpdf (x, 2, 5, 1);
 y2 = ncfpdf (x, 2, 5, 2);
 y3 = ncfpdf (x, 5, 10, 1);
 y4 = ncfpdf (x, 10, 20, 10);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
 grid on
 xlim ([0, 5])
 ylim ([0, 0.8])
 legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
          "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
         "location", "northeast")
 title ("Noncentral F PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Compare the noncentral F PDF with LAMBDA = 10 to the F PDF with the
 ## same number of numerator and denominator degrees of freedom (5, 20)

 x = 0.01:0.1:10.01;
 y1 = ncfpdf (x, 5, 20, 10);
 y2 = fpdf (x, 5, 20);
 plot (x, y1, "-", x, y2, "-");
 grid on
 xlim ([0, 10])
 ylim ([0, 0.8])
 legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "northeast")
 title ("Noncentral F vs F PDFs")
 xlabel ("values in x")
 ylabel ("density")
***** shared x1, df1, df2, lambda
 x1 = [-Inf, 2, NaN, 4, Inf];
 df1 = [2, 0, -1, 1, 4];
 df2 = [2, 4, 5, 6, 8];
 lambda = [1, NaN, 3, -1, 2];
***** assert (ncfpdf (x1, df1, df2, lambda), [0, NaN, NaN, NaN, NaN]);
***** assert (ncfpdf (x1, df1, df2, 1), [0, NaN, NaN, ...
                                   0.05607937264237208, NaN], 1e-14);
***** assert (ncfpdf (x1, df1, df2, 3), [0, NaN, NaN, ...
                                   0.080125760971946518, NaN], 1e-14);
***** assert (ncfpdf (x1, df1, df2, 2), [0, NaN, NaN, ...
                                   0.0715902008258656, NaN], 1e-14);
***** assert (ncfpdf (x1, 3, 5, lambda), [0, NaN, NaN, NaN, NaN]);
***** assert (ncfpdf (2, df1, df2, lambda), [0.1254046999837947, NaN, NaN, ...
                                      NaN, 0.2152571783045893], 1e-14);
***** assert (ncfpdf (4, df1, df2, lambda), [0.05067089541001374, NaN, NaN, ...
                                      NaN, 0.05560846335398539], 1e-14);
***** error<ncfpdf: function called with too few input arguments.> ncfpdf ()
***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1)
***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2)
***** error<ncfpdf: function called with too few input arguments.> ncfpdf (1, 2, 3)
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfpdf (ones (3), ones (2), ones (2), ones (2))
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfpdf (ones (2), ones (3), ones (2), ones (2))
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfpdf (ones (2), ones (2), ones (3), ones (2))
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfpdf (ones (2), ones (2), ones (2), ones (3))
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (i, 2, 2, 2)
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, i, 2, 2)
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, i, 2)
***** error<ncfpdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfpdf (2, 2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/riceinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/riceinv.m
***** demo
 ## Plot various iCDFs from the Rician distribution
 p = 0.001:0.001:0.999;
 x1 = riceinv (p, 0, 1);
 x2 = riceinv (p, 0.5, 1);
 x3 = riceinv (p, 1, 1);
 x4 = riceinv (p, 2, 1);
 x5 = riceinv (p, 4, 1);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
 grid on
 legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
          "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northwest")
 title ("Rician iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.75 1 2];
***** assert (riceinv (p, ones (1,5), 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
***** assert (riceinv (p, 1, 2*ones (1,5)), [NaN 0 3.5354 Inf NaN], 1e-4)
***** assert (riceinv (p, ones (1,5), 2), [NaN 0 3.5354 Inf NaN], 1e-4)
***** assert (riceinv (p, [1 0 NaN 1 1], 2), [NaN 0 NaN Inf NaN])
***** assert (riceinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN])
***** assert (riceinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
***** assert (riceinv ([p, NaN], 1, 2), [NaN 0 3.5354 Inf NaN NaN], 1e-4)
***** assert (riceinv (single ([p, NaN]), 1, 2), ...
        single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
***** assert (riceinv ([p, NaN], single (1), 2), ...
        single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
***** assert (riceinv ([p, NaN], 1, single (2)), ...
        single ([NaN 0 3.5354 Inf NaN NaN]), 1e-4)
***** error<riceinv: function called with too few input arguments.> riceinv ()
***** error<riceinv: function called with too few input arguments.> riceinv (1)
***** error<riceinv: function called with too few input arguments.> riceinv (1,2)
***** error<riceinv: function called with too many inputs> riceinv (1,2,3,4)
***** error<riceinv: P, S, and B must be of common size or scalars.> ...
 riceinv (ones (3), ones (2), ones (2))
***** error<riceinv: P, S, and B must be of common size or scalars.> ...
 riceinv (ones (2), ones (3), ones (2))
***** error<riceinv: P, S, and B must be of common size or scalars.> ...
 riceinv (ones (2), ones (2), ones (3))
***** error<riceinv: P, S, and B must not be complex.> riceinv (i, 2, 2)
***** error<riceinv: P, S, and B must not be complex.> riceinv (2, i, 2)
***** error<riceinv: P, S, and B must not be complex.> riceinv (2, 2, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/hnrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/hnrnd.m
***** assert (size (hnrnd (1, 1, 1)), [1, 1])
***** assert (size (hnrnd (1, 1, 2)), [2, 2])
***** assert (size (hnrnd (1, 1, [2, 1])), [2, 1])
***** assert (size (hnrnd (1, zeros (2, 2))), [2, 2])
***** assert (size (hnrnd (1, ones (2, 1))), [2, 1])
***** assert (size (hnrnd (1, ones (2, 2))), [2, 2])
***** assert (size (hnrnd (ones (2, 1), 1)), [2, 1])
***** assert (size (hnrnd (ones (2, 2), 1)), [2, 2])
***** assert (size (hnrnd (1, 1, 3)), [3, 3])
***** assert (size (hnrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (hnrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (hnrnd (1, 1, [])), [0, 0])
***** assert (size (hnrnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** test
 r =  hnrnd (1, [1, 0, -1]);
 assert (r([2:3]), [NaN, NaN])
***** assert (class (hnrnd (1, 0)), "double")
***** assert (class (hnrnd (1, single (0))), "single")
***** assert (class (hnrnd (1, single ([0, 0]))), "single")
***** assert (class (hnrnd (1, single (1))), "single")
***** assert (class (hnrnd (1, single ([1, 1]))), "single")
***** assert (class (hnrnd (single (1), 1)), "single")
***** assert (class (hnrnd (single ([1, 1]), 1)), "single")
***** error<hnrnd: function called with too few input arguments.> hnrnd ()
***** error<hnrnd: function called with too few input arguments.> hnrnd (1)
***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
 hnrnd (ones (3), ones (2))
***** error<hnrnd: MU and SIGMA must be of common size or scalars.> ...
 hnrnd (ones (2), ones (3))
***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (i, 2, 3)
***** error<hnrnd: MU and SIGMA must not be complex.> hnrnd (1, i, 3)
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, -1)
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, 1.2)
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, ones (2))
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, [2 -1 2])
***** error<hnrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 hnrnd (1, 2, [2 0 2.5])
***** error<hnrnd: dimensions must be non-negative integers.> ...
 hnrnd (1, 2, 2, -1, 5)
***** error<hnrnd: dimensions must be non-negative integers.> ...
 hnrnd (1, 2, 2, 1.5, 5)
***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
 hnrnd (2, ones (2), 3)
***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
 hnrnd (2, ones (2), [3, 2])
***** error<hnrnd: MU and SIGMA must be scalars or of size SZ.> ...
 hnrnd (2, ones (2), 3, 2)
37 tests, 37 passed, 0 known failure, 0 skipped
[inst/dist_fun/binornd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/binornd.m
***** assert (size (binornd (2, 1/2)), [1 1])
***** assert (size (binornd (2 * ones (2, 1), 1/2)), [2, 1])
***** assert (size (binornd (2 * ones (2, 2), 1/2)), [2, 2])
***** assert (size (binornd (2, 1/2 * ones (2, 1))), [2, 1])
***** assert (size (binornd (1, 1/2 * ones (2, 2))), [2, 2])
***** assert (size (binornd (ones (2, 1), 1)), [2, 1])
***** assert (size (binornd (ones (2, 2), 1)), [2, 2])
***** assert (size (binornd (2, 1/2, 3)), [3, 3])
***** assert (size (binornd (1, 1, [4, 1])), [4, 1])
***** assert (size (binornd (1, 1, 4, 1)), [4, 1])
***** assert (size (binornd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (binornd (1, 1, 0, 1)), [0, 1])
***** assert (size (binornd (1, 1, 1, 0)), [1, 0])
***** assert (size (binornd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (binornd (1, 1, [])), [0, 0])
***** assert (size (binornd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (binornd (1, 1)), "double")
***** assert (class (binornd (1, single (0))), "single")
***** assert (class (binornd (1, single ([0, 0]))), "single")
***** assert (class (binornd (1, single (1), 2)), "single")
***** assert (class (binornd (1, single ([1, 1]), 1, 2)), "single")
***** assert (class (binornd (single (1), 1, 2)), "single")
***** assert (class (binornd (single ([1, 1]), 1, 1, 2)), "single")
***** error<binornd: function called with too few input arguments.> binornd ()
***** error<binornd: function called with too few input arguments.> binornd (1)
***** error<binornd: N and PS must be of common size or scalars.> ...
 binornd (ones (3), ones (2))
***** error<binornd: N and PS must be of common size or scalars.> ...
 binornd (ones (2), ones (3))
***** error<binornd: N and PS must not be complex.> binornd (i, 2)
***** error<binornd: N and PS must not be complex.> binornd (1, i)
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, -1)
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, 1.2)
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, ones (2))
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, [2 -1 2])
***** error<binornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 binornd (1, 1/2, [2 0 2.5])
***** error<binornd: dimensions must be non-negative integers.> ...
 binornd (1, 1/2, 2, -1, 5)
***** error<binornd: dimensions must be non-negative integers.> ...
 binornd (1, 1/2, 2, 1.5, 5)
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2, 1/2 * ones (2), 3)
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2, 1/2 * ones (2), [3, 2])
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2, 1/2 * ones (2), 3, 2)
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2 * ones (2), 1/2, 3)
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2 * ones (2), 1/2, [3, 2])
***** error<binornd: N and PS must be scalars or of size SZ.> ...
 binornd (2 * ones (2), 1/2, 3, 2)
42 tests, 42 passed, 0 known failure, 0 skipped
[inst/dist_fun/wblpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/wblpdf.m
***** demo
 ## Plot various PDFs from the Weibul distribution
 x = 0:0.001:2.5;
 y1 = wblpdf (x, 1, 0.5);
 y2 = wblpdf (x, 1, 1);
 y3 = wblpdf (x, 1, 1.5);
 y4 = wblpdf (x, 1, 5);
 plot (x, y1, "-b", x, y2, "-r", x, y3, "-m", x, y4, "-g")
 grid on
 ylim ([0, 2.5])
 legend ({"λ = 5, k = 0.5", "λ = 9, k = 1", ...
          "λ = 6, k = 1.5", "λ = 2, k = 5"}, "location", "northeast")
 title ("Weibul PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x,y
 x = [-1 0 0.5 1 Inf];
 y = [0, exp(-x(2:4)), NaN];
***** assert (wblpdf (x, ones (1,5), ones (1,5)), y)
***** assert (wblpdf (x, 1, ones (1,5)), y)
***** assert (wblpdf (x, ones (1,5), 1), y)
***** assert (wblpdf (x, [0 NaN Inf 1 1], 1), [NaN NaN NaN y(4:5)])
***** assert (wblpdf (x, 1, [0 NaN Inf 1 1]), [NaN NaN NaN y(4:5)])
***** assert (wblpdf ([x, NaN], 1, 1), [y, NaN])
***** assert (wblpdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
***** assert (wblpdf ([x, NaN], single (1), 1), single ([y, NaN]))
***** assert (wblpdf ([x, NaN], 1, single (1)), single ([y, NaN]))
***** error wblpdf ()
***** error wblpdf (1,2,3,4)
***** error wblpdf (ones (3), ones (2), ones (2))
***** error wblpdf (ones (2), ones (3), ones (2))
***** error wblpdf (ones (2), ones (2), ones (3))
***** error wblpdf (i, 2, 2)
***** error wblpdf (2, i, 2)
***** error wblpdf (2, 2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/wblcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/wblcdf.m
***** demo
 ## Plot various CDFs from the Weibull distribution
 x = 0:0.001:2.5;
 p1 = wblcdf (x, 1, 0.5);
 p2 = wblcdf (x, 1, 1);
 p3 = wblcdf (x, 1, 1.5);
 p4 = wblcdf (x, 1, 5);
 plot (x, p1, "-b", x, p2, "-r", x, p3, "-m", x, p4, "-g")
 grid on
 legend ({"λ = 1, k = 0.5", "λ = 1, k = 1", ...
          "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "southeast")
 title ("Weibull CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 0.5 1 Inf];
 y = [0, 1-exp(-x(2:4)), 1];
***** assert (wblcdf (x, ones (1,5), ones (1,5)), y, 1e-16)
***** assert (wblcdf (x, ones (1,5), ones (1,5), "upper"), 1 - y)
***** assert (wblcdf (x, "upper"), 1 - y)
***** assert (wblcdf (x, 1, ones (1,5)), y, 1e-16)
***** assert (wblcdf (x, ones (1,5), 1), y, 1e-16)
***** assert (wblcdf (x, [0 1 NaN Inf 1], 1), [NaN 0 NaN 0 1])
***** assert (wblcdf (x, [0 1 NaN Inf 1], 1, "upper"), 1 - [NaN 0 NaN 0 1])
***** assert (wblcdf (x, 1, [0 1 NaN Inf 1]), [NaN 0 NaN y(4:5)])
***** assert (wblcdf (x, 1, [0 1 NaN Inf 1], "upper"), 1 - [NaN 0 NaN y(4:5)])
***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1), [y(1:2) NaN y(4:5)])
***** assert (wblcdf ([x(1:2) NaN x(4:5)], 1, 1, "upper"), 1 - [y(1:2) NaN y(4:5)])
***** assert (wblcdf ([x, NaN], 1, 1), [y, NaN], 1e-16)
***** assert (wblcdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
***** assert (wblcdf ([x, NaN], single (1), 1), single ([y, NaN]))
***** assert (wblcdf ([x, NaN], 1, single (1)), single ([y, NaN]))
***** error<wblcdf: invalid number of input arguments.> wblcdf ()
***** error<wblcdf: invalid number of input arguments.> wblcdf (1,2,3,4,5,6,7)
***** error<wblcdf: invalid argument for upper tail.> wblcdf (1, 2, 3, 4, "uper")
***** error<wblcdf: X, LAMBDA, and K must be of common size or scalars.> ...
 wblcdf (ones (3), ones (2), ones (2))
***** error<wblcdf: invalid size of covariance matrix.> wblcdf (2, 3, 4, [1, 2])
***** error<wblcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = wblcdf (1, 2, 3)
***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
 wblcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
 wblcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<wblcdf: invalid value for alpha.> [p, plo, pup] = ...
 wblcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (i, 2, 2)
***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, i, 2)
***** error<wblcdf: X, LAMBDA, and K must not be complex.> wblcdf (2, 2, i)
***** error<wblcdf: bad covariance matrix.> ...
 [p, plo, pup] =wblcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
28 tests, 28 passed, 0 known failure, 0 skipped
[inst/dist_fun/raylrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/raylrnd.m
***** assert (size (raylrnd (2)), [1, 1])
***** assert (size (raylrnd (ones (2, 1))), [2, 1])
***** assert (size (raylrnd (ones (2, 2))), [2, 2])
***** assert (size (raylrnd (1, 3)), [3, 3])
***** assert (size (raylrnd (1, [4, 1])), [4, 1])
***** assert (size (raylrnd (1, 4, 1)), [4, 1])
***** assert (size (raylrnd (1, 4, 1)), [4, 1])
***** assert (size (raylrnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (raylrnd (1, 0, 1)), [0, 1])
***** assert (size (raylrnd (1, 1, 0)), [1, 0])
***** assert (size (raylrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (raylrnd (1, [])), [0, 0])
***** assert (size (raylrnd (1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (raylrnd (0, 1, 1), NaN)
***** assert (raylrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
***** assert (class (raylrnd (2)), "double")
***** assert (class (raylrnd (single (2))), "single")
***** assert (class (raylrnd (single ([2, 2]))), "single")
***** error<raylrnd: function called with too few input arguments.> raylrnd ()
***** error<raylrnd: SIGMA must not be complex.> raylrnd (i)
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, -1)
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, 1.2)
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, ones (2))
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, [2 -1 2])
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (1, [2 0 2.5])
***** error<raylrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 raylrnd (ones (2), ones (2))
***** error<raylrnd: dimensions must be non-negative integers.> ...
 raylrnd (1, 2, -1, 5)
***** error<raylrnd: dimensions must be non-negative integers.> ...
 raylrnd (1, 2, 1.5, 5)
***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 3)
***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), [3, 2])
***** error<raylrnd: SIGMA must be scalar or of size SZ.> raylrnd (ones (2,2), 2, 3)
31 tests, 31 passed, 0 known failure, 0 skipped
[inst/dist_fun/hnpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/hnpdf.m
***** demo
 ## Plot various PDFs from the half-normal distribution
 x = 0:0.001:10;
 y1 = hnpdf (x, 0, 1);
 y2 = hnpdf (x, 0, 2);
 y3 = hnpdf (x, 0, 3);
 y4 = hnpdf (x, 0, 5);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([0, 10])
 ylim ([0, 0.9])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
          "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northeast")
 title ("Half-normal PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Plot half-normal against normal probability density function
 x = -5:0.001:5;
 y1 = hnpdf (x, 0, 1);
 y2 = normpdf (x);
 plot (x, y1, "-b", x, y2, "-g")
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.9])
 legend ({"half-normal with μ = 0, σ = 1", ...
          "standart normal (μ = 0, σ = 1)"}, "location", "northeast")
 title ("Half-normal against standard normal PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y = [0, 0, 0.7979, 0.7041, 0.4839, 0];
***** assert (hnpdf ([x, NaN], 0, 1), [y, NaN], 1e-4)
***** assert (hnpdf (x, 0, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
***** error<hnpdf: function called with too few input arguments.> hnpdf ()
***** error<hnpdf: function called with too few input arguments.> hnpdf (1)
***** error<hnpdf: function called with too few input arguments.> hnpdf (1, 2)
***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hnpdf (1, ones (2), ones (3))
***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hnpdf (ones (2), 1, ones (3))
***** error<hnpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hnpdf (ones (2), ones (3), 1)
***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (i, 2, 3)
***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, i, 3)
***** error<hnpdf: X, MU, and SIGMA must not be complex.> hnpdf (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/gevrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gevrnd.m
***** assert (size (gevrnd (1, 2, 1)), [1, 1]);
***** assert (size (gevrnd (ones (2, 1), 2, 1)), [2, 1]);
***** assert (size (gevrnd (ones (2, 2), 2, 1)), [2, 2]);
***** assert (size (gevrnd (1, 2 * ones (2, 1), 1)), [2, 1]);
***** assert (size (gevrnd (1, 2 * ones (2, 2), 1)), [2, 2]);
***** assert (size (gevrnd (1, 2, 1, 3)), [3, 3]);
***** assert (size (gevrnd (1, 2, 1, [4, 1])), [4, 1]);
***** assert (size (gevrnd (1, 2, 1, 4, 1)), [4, 1]);
***** assert (size (gevrnd (1, 2, 1, [])), [0, 0])
***** assert (size (gevrnd (1, 2, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (gevrnd (1,1,1)), "double")
***** assert (class (gevrnd (single (1),1,1)), "single")
***** assert (class (gevrnd (single ([1 1]),1,1)), "single")
***** assert (class (gevrnd (1,single (1),1)), "single")
***** assert (class (gevrnd (1,single ([1 1]),1)), "single")
***** assert (class (gevrnd (1,1,single (1))), "single")
***** assert (class (gevrnd (1,1,single ([1 1]))), "single")
***** error<gevrnd: function called with too few input arguments.> gevrnd ()
***** error<gevrnd: function called with too few input arguments.> gevrnd (1)
***** error<gevrnd: function called with too few input arguments.> gevrnd (1, 2)
***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
 gevrnd (ones (3), ones (2), ones (2))
***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
 gevrnd (ones (2), ones (3), ones (2))
***** error<gevrnd: K, SIGMA, and MU must be of common size or scalars.> ...
 gevrnd (ones (2), ones (2), ones (3))
***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (i, 2, 3)
***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, i, 3)
***** error<gevrnd: K, SIGMA, and MU must not be complex.> gevrnd (1, 2, i)
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, -1)
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, 1.2)
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, ones (2))
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, [2 -1 2])
***** error<gevrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gevrnd (1, 2, 3, [2 0 2.5])
***** error<gevrnd: dimensions must be non-negative integers.> ...
 gevrnd (1, 2, 3, 2, -1, 5)
***** error<gevrnd: dimensions must be non-negative integers.> ...
 gevrnd (1, 2, 3, 2, 1.5, 5)
***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
 gevrnd (2, ones (2), 2, 3)
***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
 gevrnd (2, ones (2), 2, [3, 2])
***** error<gevrnd: K, SIGMA, and MU must be scalars or of size SZ.> ...
 gevrnd (2, ones (2), 2, 3, 2)
36 tests, 36 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvtpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/mvtpdf.m
***** demo
 ## Compute the pdf of a multivariate t distribution with correlation
 ## parameters rho = [1 .4; .4 1] and 2 degrees of freedom.

 rho = [1, 0.4; 0.4, 1];
 df = 2;
 [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
 X = [X1(:), X2(:)];
 y = mvtpdf (X, rho, df);
 surf (X1, X2, reshape (y, 25, 25));
 title ("Bivariate Student's t probability density function");
***** assert (mvtpdf ([0 0], eye(2), 1), 0.1591549, 1E-7)
***** assert (mvtpdf ([1 0], [1 0.5; 0.5 1], 2), 0.06615947, 1E-7)
***** assert (mvtpdf ([1 0.4 0; 1.2 0.5 0.5; 1.4 0.6 1], ...
 [1 0.5 0.3; 0.5 1 0.6; 0.3 0.6 1], [5 6 7]), ...
 [0.04713313 0.03722421 0.02069011]', 1E-7)
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/dist_fun/hninv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/hninv.m
***** demo
 ## Plot various iCDFs from the half-normal distribution
 p = 0.001:0.001:0.999;
 x1 = hninv (p, 0, 1);
 x2 = hninv (p, 0, 2);
 x3 = hninv (p, 0, 3);
 x4 = hninv (p, 0, 5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([0, 10])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
          "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "northwest")
 title ("Half-normal iCDF")
 xlabel ("probability")
 ylabel ("x")
***** shared p, x
 p = [0, 0.3829, 0.6827, 1];
 x = [0, 1/2, 1, Inf];
***** assert (hninv (p, 0, 1), x, 1e-4);
***** assert (hninv (p, 5, 1), x + 5, 1e-4);
***** assert (hninv (p, 0, ones (1,4)), x, 1e-4);
***** assert (hninv (p, 0, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
***** assert (class (hninv (single ([p, NaN]), 0, 1)), "single")
***** assert (class (hninv ([p, NaN], single (0), 1)), "single")
***** assert (class (hninv ([p, NaN], 0, single (1))), "single")
***** error<hninv: function called with too few input arguments.> hninv (1)
***** error<hninv: function called with too few input arguments.> hninv (1, 2)
***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
 hninv (1, ones (2), ones (3))
***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
 hninv (ones (2), 1, ones (3))
***** error<hninv: P, MU, and SIGMA must be of common size or scalars.> ...
 hninv (ones (2), ones (3), 1)
***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (i, 2, 3)
***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, i, 3)
***** error<hninv: P, MU, and SIGMA must not be complex.> hninv (1, 2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/loglpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/loglpdf.m
***** demo
 ## Plot various PDFs from the log-logistic distribution
 x = 0.001:0.001:2;
 y1 = loglpdf (x, log (1), 1/0.5);
 y2 = loglpdf (x, log (1), 1);
 y3 = loglpdf (x, log (1), 1/2);
 y4 = loglpdf (x, log (1), 1/4);
 y5 = loglpdf (x, log (1), 1/8);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0,3])
 legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
          "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northeast")
 title ("Log-logistic PDF")
 xlabel ("values in x")
 ylabel ("density")
 text (0.1, 2.8, "μ = 0 (α = 1), values of σ (β) as shown in legend")
***** shared out1, out2
 out1 = [0, 0, 1, 0.2500, 0.1111, 0.0625, 0.0400, 0.0278, 0];
 out2 = [0, 0, 0.0811, 0.0416, 0.0278, 0.0207, 0.0165, 0];
***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
***** assert (loglpdf ([-1,0,realmin,1:5,Inf], 0, 1), out1, 1e-4)
***** assert (loglpdf ([-1:5,Inf], 1, 3), out2, 1e-4)
***** assert (class (loglpdf (single (1), 2, 3)), "single")
***** assert (class (loglpdf (1, single (2), 3)), "single")
***** assert (class (loglpdf (1, 2, single (3))), "single")
***** error<loglpdf: function called with too few input arguments.> loglpdf (1)
***** error<loglpdf: function called with too few input arguments.> loglpdf (1, 2)
***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglpdf (1, ones (2), ones (3))
***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglpdf (ones (2), 1, ones (3))
***** error<loglpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglpdf (ones (2), ones (3), 1)
***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (i, 2, 3)
***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, i, 3)
***** error<loglpdf: X, MU, and SIGMA must not be complex.> loglpdf (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/nctcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/nctcdf.m
***** demo
 ## Plot various CDFs from the noncentral Τ distribution
 x = -5:0.01:5;
 p1 = nctcdf (x, 1, 0);
 p2 = nctcdf (x, 4, 0);
 p3 = nctcdf (x, 1, 2);
 p4 = nctcdf (x, 4, 2);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
 grid on
 xlim ([-5, 5])
 legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
          "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "southeast")
 title ("Noncentral Τ CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Compare the noncentral T CDF with MU = 1 to the T CDF
 ## with the same number of degrees of freedom (10).

 x = -5:0.1:5;
 p1 = nctcdf (x, 10, 1);
 p2 = tcdf (x, 10);
 plot (x, p1, "-", x, p2, "-")
 grid on
 xlim ([-5, 5])
 legend ({"Noncentral T(10,1)", "T(10)"}, "location", "southeast")
 title ("Noncentral T vs T CDFs")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = -2:0.1:2;
 p = nctcdf (x, 10, 1);
 assert (p(1), 0.003302485766631558, 1e-14);
 assert (p(2), 0.004084668193532631, 1e-14);
 assert (p(3), 0.005052800319478737, 1e-14);
 assert (p(41), 0.8076115625303751, 1e-14);
***** test
 p = nctcdf (12, 10, 3);
 assert (p, 0.9997719343243797, 1e-14);
***** test
 p = nctcdf (2, 3, 2);
 assert (p, 0.4430757822176028, 1e-14);
***** test
 p = nctcdf (2, 3, 2, "upper");
 assert (p, 0.5569242177823971, 1e-14);
***** test
 p = nctcdf ([3, 6], 3, 2, "upper");
 assert (p, [0.3199728259444777, 0.07064855592441913], 1e-14);
***** error<nctcdf: function called with too few input arguments.> nctcdf ()
***** error<nctcdf: function called with too few input arguments.> nctcdf (1)
***** error<nctcdf: function called with too few input arguments.> nctcdf (1, 2)
***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, "tail")
***** error<nctcdf: invalid argument for upper tail.> nctcdf (1, 2, 3, 4)
***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
 nctcdf (ones (3), ones (2), ones (2))
***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
 nctcdf (ones (2), ones (3), ones (2))
***** error<nctcdf: X, DF, and MU must be of common size or scalars.> ...
 nctcdf (ones (2), ones (2), ones (3))
***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (i, 2, 2)
***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, i, 2)
***** error<nctcdf: X, DF, and MU must not be complex.> nctcdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/vmpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/vmpdf.m
***** demo
 ## Plot various PDFs from the von Mises distribution
 x1 = [-pi:0.1:pi];
 y1 = vmpdf (x1, 0, 0.5);
 y2 = vmpdf (x1, 0, 1);
 y3 = vmpdf (x1, 0, 2);
 y4 = vmpdf (x1, 0, 4);
 plot (x1, y1, "-r", x1, y2, "-g", x1, y3, "-b", x1, y4, "-c")
 grid on
 xlim ([-pi, pi])
 ylim ([0, 0.8])
 legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
          "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
 title ("Von Mises PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y0, y1
 x = [-pi:pi/2:pi];
 y0 = [0.046245, 0.125708, 0.341710, 0.125708, 0.046245];
 y1 = [0.046245, 0.069817, 0.654958, 0.014082, 0.000039];
***** assert (vmpdf (x, 0, 1), y0, 1e-5)
***** assert (vmpdf (x, zeros (1,5), ones (1,5)), y0, 1e-6)
***** assert (vmpdf (x, 0, [1 2 3 4 5]), y1, 1e-6)
***** assert (isa (vmpdf (single (pi), 0, 1), "single"), true)
***** assert (isa (vmpdf (pi, single (0), 1), "single"), true)
***** assert (isa (vmpdf (pi, 0, single (1)), "single"), true)
***** error<vmpdf: function called with too few input arguments.> vmpdf ()
***** error<vmpdf: function called with too few input arguments.> vmpdf (1)
***** error<vmpdf: function called with too few input arguments.> vmpdf (1, 2)
***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
 vmpdf (ones (3), ones (2), ones (2))
***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
 vmpdf (ones (2), ones (3), ones (2))
***** error<vmpdf: X, MU, and K must be of common size or scalars.> ...
 vmpdf (ones (2), ones (2), ones (3))
***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (i, 2, 2)
***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, i, 2)
***** error<vmpdf: X, MU, and K must not be complex.> vmpdf (2, 2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/gumbelpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gumbelpdf.m
***** demo
 ## Plot various PDFs from the Extreme value distribution
 x = -5:0.001:20;
 y1 = gumbelpdf (x, 0.5, 2);
 y2 = gumbelpdf (x, 1.0, 2);
 y3 = gumbelpdf (x, 1.5, 3);
 y4 = gumbelpdf (x, 3.0, 4);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 ylim ([0, 0.2])
 legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
          "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northeast")
 title ("Extreme value PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y0, y1
 x = [-5, 0, 1, 2, 3];
 y0 = [0, 0.3679, 0.2547, 0.1182, 0.0474];
 y1 = [0, 0.1794, 0.3679, 0.2547, 0.1182];
***** assert (gumbelpdf (x), y0, 1e-4)
***** assert (gumbelpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
***** assert (gumbelpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
***** error<gumbelpdf: too few input arguments.> gumbelpdf ()
***** error<gumbelpdf: X, MU, and BETA must be of common size or scalars.> ...
 gumbelpdf (ones (3), ones (2), ones (2))
***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (i, 2, 2)
***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, i, 2)
***** error<gumbelpdf: X, MU, and BETA must not be complex.> gumbelpdf (2, 2, i)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/cauchyinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/cauchyinv.m
***** demo
 ## Plot various iCDFs from the Cauchy distribution
 p = 0.001:0.001:0.999;
 x1 = cauchyinv (p, 0, 0.5);
 x2 = cauchyinv (p, 0, 1);
 x3 = cauchyinv (p, 0, 2);
 x4 = cauchyinv (p, -2, 1);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-5, 5])
 legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
          "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northwest")
 title ("Cauchy iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (cauchyinv (p, ones (1,5), 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
***** assert (cauchyinv (p, 1, 2 * ones (1,5)), [NaN -Inf 1 Inf NaN], eps)
***** assert (cauchyinv (p, ones (1,5), 2), [NaN -Inf 1 Inf NaN], eps)
***** assert (cauchyinv (p, [1 -Inf NaN Inf 1], 2), [NaN NaN NaN NaN NaN])
***** assert (cauchyinv (p, 1, 2 * [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (cauchyinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN -Inf NaN Inf NaN])
***** assert (cauchyinv ([p, NaN], 1, 2), [NaN -Inf 1 Inf NaN NaN], eps)
***** assert (cauchyinv (single ([p, NaN]), 1, 2), ...
 single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
***** assert (cauchyinv ([p, NaN], single (1), 2), ...
 single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
***** assert (cauchyinv ([p, NaN], 1, single (2)), ...
 single ([NaN -Inf 1 Inf NaN NaN]), eps ("single"))
***** error<cauchyinv: function called with too few input arguments.> cauchyinv ()
***** error<cauchyinv: function called with too few input arguments.> cauchyinv (1)
***** error<cauchyinv: function called with too few input arguments.> ...
 cauchyinv (1, 2)
***** error<cauchyinv: function called with too many inputs> cauchyinv (1, 2, 3, 4)
***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
 cauchyinv (ones (3), ones (2), ones(2))
***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
 cauchyinv (ones (2), ones (3), ones(2))
***** error<cauchyinv: P, X0, and GAMMA must be of common size or scalars.> ...
 cauchyinv (ones (2), ones (2), ones(3))
***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (i, 4, 3)
***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, i, 3)
***** error<cauchyinv: P, X0, and GAMMA must not be complex.> cauchyinv (1, 4, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncx2rnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/ncx2rnd.m
***** assert (size (ncx2rnd (1, 1)), [1, 1])
***** assert (size (ncx2rnd (1, ones (2, 1))), [2, 1])
***** assert (size (ncx2rnd (1, ones (2, 2))), [2, 2])
***** assert (size (ncx2rnd (ones (2, 1), 1)), [2, 1])
***** assert (size (ncx2rnd (ones (2, 2), 1)), [2, 2])
***** assert (size (ncx2rnd (1, 1, 3)), [3, 3])
***** assert (size (ncx2rnd (1, 1, [4, 1])), [4, 1])
***** assert (size (ncx2rnd (1, 1, 4, 1)), [4, 1])
***** assert (size (ncx2rnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (ncx2rnd (1, 1, 0, 1)), [0, 1])
***** assert (size (ncx2rnd (1, 1, 1, 0)), [1, 0])
***** assert (size (ncx2rnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (ncx2rnd (1, 1, [])), [0, 0])
***** assert (size (ncx2rnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (ncx2rnd (1, 1)), "double")
***** assert (class (ncx2rnd (1, single (1))), "single")
***** assert (class (ncx2rnd (1, single ([1, 1]))), "single")
***** assert (class (ncx2rnd (single (1), 1)), "single")
***** assert (class (ncx2rnd (single ([1, 1]), 1)), "single")
***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd ()
***** error<ncx2rnd: function called with too few input arguments.> ncx2rnd (1)
***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
 ncx2rnd (ones (3), ones (2))
***** error<ncx2rnd: DF and LAMBDA must be of common size or scalars.> ...
 ncx2rnd (ones (2), ones (3))
***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (i, 2)
***** error<ncx2rnd: DF and LAMBDA must not be complex.> ncx2rnd (1, i)
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, -1)
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, 1.2)
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, ones (2))
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, [2 -1 2])
***** error<ncx2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncx2rnd (1, 2, [2 0 2.5])
***** error<ncx2rnd: dimensions must be non-negative integers.> ...
 ncx2rnd (1, 2, 2, -1, 5)
***** error<ncx2rnd: dimensions must be non-negative integers.> ...
 ncx2rnd (1, 2, 2, 1.5, 5)
***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
 ncx2rnd (2, ones (2), 3)
***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
 ncx2rnd (2, ones (2), [3, 2])
***** error<ncx2rnd: DF and LAMBDA must be scalars or of size SZ.> ...
 ncx2rnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/lognpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/lognpdf.m
***** demo
 ## Plot various PDFs from the log-normal distribution
 x = 0:0.01:5;
 y1 = lognpdf (x, 0, 1);
 y2 = lognpdf (x, 0, 0.5);
 y3 = lognpdf (x, 0, 0.25);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
 grid on
 ylim ([0, 2])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
          "location", "northeast")
 title ("Log-normal PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 e Inf];
 y = [0, 0, 1/(e*sqrt(2*pi)) * exp(-1/2), 0];
***** assert (lognpdf (x, zeros (1,4), ones (1,4)), y, eps)
***** assert (lognpdf (x, 0, ones (1,4)), y, eps)
***** assert (lognpdf (x, zeros (1,4), 1), y, eps)
***** assert (lognpdf (x, [0 1 NaN 0], 1), [0 0 NaN y(4)], eps)
***** assert (lognpdf (x, 0, [0 NaN Inf 1]), [NaN NaN NaN y(4)], eps)
***** assert (lognpdf ([x, NaN], 0, 1), [y, NaN], eps)
***** assert (lognpdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
***** assert (lognpdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
***** assert (lognpdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
***** error lognpdf ()
***** error lognpdf (1,2,3,4)
***** error lognpdf (ones (3), ones (2), ones (2))
***** error lognpdf (ones (2), ones (3), ones (2))
***** error lognpdf (ones (2), ones (2), ones (3))
***** error lognpdf (i, 2, 2)
***** error lognpdf (2, i, 2)
***** error lognpdf (2, 2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/burrcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/burrcdf.m
***** demo
 ## Plot various CDFs from the Burr type XII distribution
 x = 0.001:0.001:5;
 p1 = burrcdf (x, 1, 1, 1);
 p2 = burrcdf (x, 1, 1, 2);
 p3 = burrcdf (x, 1, 1, 3);
 p4 = burrcdf (x, 1, 2, 1);
 p5 = burrcdf (x, 1, 3, 1);
 p6 = burrcdf (x, 1, 0.5, 2);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
       x, p4, "-c", x, p5, "-m", x, p6, "-k")
 grid on
 legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
          "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
          "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
         "location", "southeast")
 title ("Burr type XII CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 1/2, 2/3, 1];
***** assert (burrcdf (x, ones(1,5), ones (1,5), ones (1,5)), y, eps)
***** assert (burrcdf (x, 1, 1, 1), y, eps)
***** assert (burrcdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (burrcdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (burrcdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
***** assert (burrcdf ([x, NaN], 1, 1, 1), [y, NaN], eps)
***** assert (burrcdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
***** assert (burrcdf ([x, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
***** assert (burrcdf ([x, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
***** assert (burrcdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
***** error<burrcdf: function called with too few input arguments.> burrcdf ()
***** error<burrcdf: function called with too few input arguments.> burrcdf (1)
***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2)
***** error<burrcdf: function called with too few input arguments.> burrcdf (1, 2, 3)
***** error<burrcdf: function called with too many inputs> ...
 burrcdf (1, 2, 3, 4, 5, 6)
***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, "tail")
***** error<burrcdf: invalid argument for upper tail.> burrcdf (1, 2, 3, 4, 5)
***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrcdf (ones (3), ones (2), ones(2), ones(2))
***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrcdf (ones (2), ones (3), ones(2), ones(2))
***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrcdf (ones (2), ones (2), ones(3), ones(2))
***** error<burrcdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrcdf (ones (2), ones (2), ones(2), ones(3))
***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (i, 2, 3, 4)
***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, i, 3, 4)
***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, i, 4)
***** error<burrcdf: X, LAMBDA, C, and K must not be complex.> burrcdf (1, 2, 3, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/invginv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/invginv.m
***** demo
 ## Plot various iCDFs from the inverse Gaussian distribution
 p = 0.001:0.001:0.999;
 x1 = invginv (p, 1, 0.2);
 x2 = invginv (p, 1, 1);
 x3 = invginv (p, 1, 3);
 x4 = invginv (p, 3, 0.2);
 x5 = invginv (p, 3, 1);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-y")
 grid on
 ylim ([0, 3])
 legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
          "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northwest")
 title ("Inverse Gaussian iCDF")
 xlabel ("probability")
 ylabel ("x")
***** shared p, x
 p = [0, 0.3829, 0.6827, 1];
 x = [0, 0.5207, 1.0376, Inf];
***** assert (invginv (p, 1, 1), x, 1e-4);
***** assert (invginv (p, 1, ones (1,4)), x, 1e-4);
***** assert (invginv (p, 1, [-1, 0, 1, 1]), [NaN, NaN, x(3:4)], 1e-4)
***** assert (invginv (p, [-1, 0, 1, 1], 1), [NaN, NaN, x(3:4)], 1e-4)
***** assert (class (invginv (single ([p, NaN]), 0, 1)), "single")
***** assert (class (invginv ([p, NaN], single (0), 1)), "single")
***** assert (class (invginv ([p, NaN], 0, single (1))), "single")
***** error<invginv: function called with too few input arguments.> invginv (1)
***** error<invginv: function called with too few input arguments.> invginv (1, 2)
***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
 invginv (1, ones (2), ones (3))
***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
 invginv (ones (2), 1, ones (3))
***** error<invginv: P, MU, and LAMBDA must be of common size or scalars.> ...
 invginv (ones (2), ones (3), 1)
***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (i, 2, 3)
***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, i, 3)
***** error<invginv: P, MU, and LAMBDA must not be complex.> invginv (1, 2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/geoinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/geoinv.m
***** demo
 ## Plot various iCDFs from the geometric distribution
 p = 0.001:0.001:0.999;
 x1 = geoinv (p, 0.2);
 x2 = geoinv (p, 0.5);
 x3 = geoinv (p, 0.7);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 10])
 legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northwest")
 title ("Geometric iCDF")
 xlabel ("probability")
 ylabel ("values in x (number of failures)")
***** shared p
 p = [-1 0 0.75 1 2];
***** assert (geoinv (p, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (geoinv (p, 0.5), [NaN 0 1 Inf NaN])
***** assert (geoinv (p, 0.5*[1 -1 NaN 4 1]), [NaN NaN NaN NaN NaN])
***** assert (geoinv ([p(1:2) NaN p(4:5)], 0.5), [NaN 0 NaN Inf NaN])
***** assert (geoinv ([p, NaN], 0.5), [NaN 0 1 Inf NaN NaN])
***** assert (geoinv (single ([p, NaN]), 0.5), single ([NaN 0 1 Inf NaN NaN]))
***** assert (geoinv ([p, NaN], single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
***** error<geoinv: function called with too few input arguments.> geoinv ()
***** error<geoinv: function called with too few input arguments.> geoinv (1)
***** error<geoinv: P and PS must be of common size or scalars.> ...
 geoinv (ones (3), ones (2))
***** error<geoinv: P and PS must be of common size or scalars.> ...
 geoinv (ones (2), ones (3))
***** error<geoinv: P and PS must not be complex.> ...
 geoinv (i, 2)
***** error<geoinv: P and PS must not be complex.> ...
 geoinv (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/gprnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gprnd.m
***** assert (size (gprnd (0, 1, 0)), [1, 1])
***** assert (size (gprnd (0, 1, zeros (2,1))), [2, 1])
***** assert (size (gprnd (0, 1, zeros (2,2))), [2, 2])
***** assert (size (gprnd (0, ones (2,1), 0)), [2, 1])
***** assert (size (gprnd (0, ones (2,2), 0)), [2, 2])
***** assert (size (gprnd (zeros (2,1), 1, 0)), [2, 1])
***** assert (size (gprnd (zeros (2,2), 1, 0)), [2, 2])
***** assert (size (gprnd (0, 1, 0, 3)), [3, 3])
***** assert (size (gprnd (0, 1, 0, [4 1])), [4, 1])
***** assert (size (gprnd (0, 1, 0, 4, 1)), [4, 1])
***** assert (size (gprnd (1,1,0)), [1, 1])
***** assert (size (gprnd (1, 1, zeros (2,1))), [2, 1])
***** assert (size (gprnd (1, 1, zeros (2,2))), [2, 2])
***** assert (size (gprnd (1, ones (2,1), 0)), [2, 1])
***** assert (size (gprnd (1, ones (2,2), 0)), [2, 2])
***** assert (size (gprnd (ones (2,1), 1, 0)), [2, 1])
***** assert (size (gprnd (ones (2,2), 1, 0)), [2, 2])
***** assert (size (gprnd (1, 1, 0, 3)), [3, 3])
***** assert (size (gprnd (1, 1, 0, [4 1])), [4, 1])
***** assert (size (gprnd (1, 1, 0, 4, 1)), [4, 1])
***** assert (size (gprnd (-1, 1, 0)), [1, 1])
***** assert (size (gprnd (-1, 1, zeros (2,1))), [2, 1])
***** assert (size (gprnd (1, -1, zeros (2,2))), [2, 2])
***** assert (size (gprnd (-1, ones (2,1), 0)), [2, 1])
***** assert (size (gprnd (-1, ones (2,2), 0)), [2, 2])
***** assert (size (gprnd (-ones (2,1), 1, 0)), [2, 1])
***** assert (size (gprnd (-ones (2,2), 1, 0)), [2, 2])
***** assert (size (gprnd (-1, 1, 0, 3)), [3, 3])
***** assert (size (gprnd (-1, 1, 0, [4, 1])), [4, 1])
***** assert (size (gprnd (-1, 1, 0, 4, 1)), [4, 1])
***** assert (size (gprnd (-1, 1, 0, [])), [0, 0])
***** assert (size (gprnd (-1, 1, 0, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (gprnd (0, 1, 0)), "double")
***** assert (class (gprnd (0, 1, single (0))), "single")
***** assert (class (gprnd (0, 1, single ([0, 0]))), "single")
***** assert (class (gprnd (0, single (1),0)), "single")
***** assert (class (gprnd (0, single ([1, 1]),0)), "single")
***** assert (class (gprnd (single (0), 1, 0)), "single")
***** assert (class (gprnd (single ([0, 0]), 1, 0)), "single")
***** error<gprnd: function called with too few input arguments.> gprnd ()
***** error<gprnd: function called with too few input arguments.> gprnd (1)
***** error<gprnd: function called with too few input arguments.> gprnd (1, 2)
***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
 gprnd (ones (3), ones (2), ones (2))
***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
 gprnd (ones (2), ones (3), ones (2))
***** error<gprnd: K, SIGMA, and THETA must be of common size or scalars.> ...
 gprnd (ones (2), ones (2), ones (3))
***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (i, 2, 3)
***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, i, 3)
***** error<gprnd: K, SIGMA, and THETA must not be complex.> gprnd (1, 2, i)
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, -1)
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, 1.2)
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, ones (2))
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, [2 -1 2])
***** error<gprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gprnd (1, 2, 3, [2 0 2.5])
***** error<gprnd: dimensions must be non-negative integers.> ...
 gprnd (1, 2, 3, 2, -1, 5)
***** error<gprnd: dimensions must be non-negative integers.> ...
 gprnd (1, 2, 3, 2, 1.5, 5)
***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
 gprnd (2, ones (2), 2, 3)
***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
 gprnd (2, ones (2), 2, [3, 2])
***** error<gprnd: K, SIGMA, and THETA must be scalars or of size SZ.> ...
 gprnd (2, ones (2), 2, 3, 2)
58 tests, 58 passed, 0 known failure, 0 skipped
[inst/dist_fun/nakainv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/nakainv.m
***** demo
 ## Plot various iCDFs from the Nakagami distribution
 p = 0.001:0.001:0.999;
 x1 = nakainv (p, 0.5, 1);
 x2 = nakainv (p, 1, 1);
 x3 = nakainv (p, 1, 2);
 x4 = nakainv (p, 1, 3);
 x5 = nakainv (p, 2, 1);
 x6 = nakainv (p, 2, 2);
 x7 = nakainv (p, 5, 1);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
       p, x5, "-k", p, x6, "-b", p, x7, "-c")
 grid on
 ylim ([0, 3])
 legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
          "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
          "μ = 5, ω = 1"}, "location", "northwest")
 title ("Nakagami iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y
 p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
 y = [NaN, NaN, 0, 0.83255461115769769, Inf, NaN, NaN];
***** assert (nakainv (p, ones (1,7), ones (1,7)), y, eps)
***** assert (nakainv (p, 1, 1), y, eps)
***** assert (nakainv (p, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
***** assert (nakainv (p, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
***** assert (nakainv ([p, NaN], 1, 1), [y, NaN], eps)
***** assert (nakainv (single ([p, NaN]), 1, 1), single ([y, NaN]))
***** assert (nakainv ([p, NaN], single (1), 1), single ([y, NaN]))
***** assert (nakainv ([p, NaN], 1, single (1)), single ([y, NaN]))
***** error<nakainv: function called with too few input arguments.> nakainv ()
***** error<nakainv: function called with too few input arguments.> nakainv (1)
***** error<nakainv: function called with too few input arguments.> nakainv (1, 2)
***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
 nakainv (ones (3), ones (2), ones(2))
***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
 nakainv (ones (2), ones (3), ones(2))
***** error<nakainv: P, MU, and OMEGA must be of common size or scalars.> ...
 nakainv (ones (2), ones (2), ones(3))
***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (i, 4, 3)
***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, i, 3)
***** error<nakainv: P, MU, and OMEGA must not be complex.> nakainv (1, 4, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/ricecdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/ricecdf.m
***** demo
 ## Plot various CDFs from the Rician distribution
 x = 0:0.01:10;
 p1 = ricecdf (x, 0, 1);
 p2 = ricecdf (x, 0.5, 1);
 p3 = ricecdf (x, 1, 1);
 p4 = ricecdf (x, 2, 1);
 p5 = ricecdf (x, 4, 1);
 plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
 grid on
 ylim ([0, 1])
 xlim ([0, 8])
 legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
          "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "southeast")
 title ("Rician CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Plot various CDFs from the Rician distribution
 x = 0:0.01:10;
 p1 = ricecdf (x, 0, 0.5);
 p2 = ricecdf (x, 0, 2);
 p3 = ricecdf (x, 0, 3);
 p4 = ricecdf (x, 2, 2);
 p5 = ricecdf (x, 4, 2);
 plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
 grid on
 ylim ([0, 1])
 xlim ([0, 8])
 legend ({"ν = 0, σ = 0.5", "ν = 0, σ = 2", "ν = 0, σ = 3", ...
          "ν = 2, σ = 2", "ν = 4, σ = 2"}, "location", "southeast")
 title ("Rician CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = 0:0.5:2.5;
 s = 1:6;
 p = ricecdf (x, s, 1);
 expected_p = [0.0000, 0.0179, 0.0108, 0.0034, 0.0008, 0.0001];
 assert (p, expected_p, 0.001);
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 p = ricecdf (x, 1, sigma);
 expected_p = [0.0000, 0.0272, 0.0512, 0.0659, 0.0754, 0.0820];
 assert (p, expected_p, 0.001);
***** test
 x = 0:0.5:2.5;
 p = ricecdf (x, 0, 1);
 expected_p = [0.0000, 0.1175, 0.3935, 0.6753, 0.8647, 0.9561];
 assert (p, expected_p, 0.001);
***** test
 x = 0:0.5:2.5;
 p = ricecdf (x, 1, 1);
 expected_p = [0.0000, 0.0735, 0.2671, 0.5120, 0.7310, 0.8791];
 assert (p, expected_p, 0.001);
***** shared x, p
 x = [-1, 0, 1, 2, Inf];
 p = [0, 0, 0.26712019620318, 0.73098793996409, 1];
***** assert (ricecdf (x, 1, 1), p, 1e-14)
***** assert (ricecdf (x, 1, 1, "upper"), 1 - p, 1e-14)
***** error<ricecdf: function called with too few input arguments.> ricecdf ()
***** error<ricecdf: function called with too few input arguments.> ricecdf (1)
***** error<ricecdf: function called with too few input arguments.> ricecdf (1, 2)
***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, "uper")
***** error<ricecdf: invalid argument for upper tail.> ricecdf (1, 2, 3, 4)
***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricecdf (ones (3), ones (2), ones (2))
***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricecdf (ones (2), ones (3), ones (2))
***** error<ricecdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricecdf (ones (2), ones (2), ones (3))
***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (i, 2, 3)
***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, i, 3)
***** error<ricecdf: X, S, and SIGMA must not be complex.> ricecdf (2, 2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/binocdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/binocdf.m
***** demo
 ## Plot various CDFs from the binomial distribution
 x = 0:40;
 p1 = binocdf (x, 20, 0.5);
 p2 = binocdf (x, 20, 0.7);
 p3 = binocdf (x, 40, 0.5);
 plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
 grid on
 legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
          "n = 40, ps = 0.5"}, "location", "southeast")
 title ("Binomial CDF")
 xlabel ("values in x (number of successes)")
 ylabel ("probability")
***** shared x, p, p1
 x = [-1 0 1 2 3];
 p = [0 1/4 3/4 1 1];
 p1 = 1 - p;
***** assert (binocdf (x, 2 * ones (1, 5), 0.5 * ones (1, 5)), p, eps)
***** assert (binocdf (x, 2, 0.5 * ones (1, 5)), p, eps)
***** assert (binocdf (x, 2 * ones (1, 5), 0.5), p, eps)
***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5), [0 NaN NaN NaN 1])
***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1]), [0 NaN NaN NaN 1])
***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5), [p(1:2) NaN p(4:5)], eps)
***** assert (binocdf (99, 100, 0.1, "upper"), 1e-100, 1e-112);
***** assert (binocdf (x, 2 * ones (1, 5), 0.5*ones (1,5), "upper"), p1, eps)
***** assert (binocdf (x, 2, 0.5 * ones (1, 5), "upper"), p1, eps)
***** assert (binocdf (x, 2 * ones (1, 5), 0.5, "upper"), p1, eps)
***** assert (binocdf (x, 2 * [0 -1 NaN 1.1 1], 0.5, "upper"), [1 NaN NaN NaN 0])
***** assert (binocdf (x, 2, 0.5 * [0 -1 NaN 3 1], "upper"), [1 NaN NaN NaN 0])
***** assert (binocdf ([x(1:2) NaN x(4:5)], 2, 0.5, "upper"), [p1(1:2) NaN p1(4:5)])
***** assert (binocdf ([x, NaN], 2, 0.5), [p, NaN], eps)
***** assert (binocdf (single ([x, NaN]), 2, 0.5), single ([p, NaN]))
***** assert (binocdf ([x, NaN], single (2), 0.5), single ([p, NaN]))
***** assert (binocdf ([x, NaN], 2, single (0.5)), single ([p, NaN]))
***** error<binocdf: function called with too few input arguments.> binocdf ()
***** error<binocdf: function called with too few input arguments.> binocdf (1)
***** error<binocdf: function called with too few input arguments.> binocdf (1, 2)
***** error<binocdf: function called with too many inputs> binocdf (1, 2, 3, 4, 5)
***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, "tail")
***** error<binocdf: invalid argument for upper tail.> binocdf (1, 2, 3, 4)
***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
 binocdf (ones (3), ones (2), ones (2))
***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
 binocdf (ones (2), ones (3), ones (2))
***** error<binocdf: X, N, and PS must be of common size or scalars.> ...
 binocdf (ones (2), ones (2), ones (3))
***** error<binocdf: X, N, and PS must not be complex.> binocdf (i, 2, 2)
***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, i, 2)
***** error<binocdf: X, N, and PS must not be complex.> binocdf (2, 2, i)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/laplacepdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/laplacepdf.m
***** demo
 ## Plot various PDFs from the Laplace distribution
 x = -10:0.01:10;
 y1 = laplacepdf (x, 0, 1);
 y2 = laplacepdf (x, 0, 2);
 y3 = laplacepdf (x, 0, 4);
 y4 = laplacepdf (x, -5, 4);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([-10, 10])
 ylim ([0, 0.6])
 legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
          "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northeast")
 title ("Laplace PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf -log(2) 0 log(2) Inf];
 y = [0, 1/4, 1/2, 1/4, 0];
***** assert (laplacepdf ([x, NaN], 0, 1), [y, NaN])
***** assert (laplacepdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.25, 0])
***** assert (laplacepdf (single ([x, NaN]), 0, 1), single ([y, NaN]))
***** assert (laplacepdf ([x, NaN], single (0), 1), single ([y, NaN]))
***** assert (laplacepdf ([x, NaN], 0, single (1)), single ([y, NaN]))
***** error<laplacepdf: function called with too few input arguments.> laplacepdf ()
***** error<laplacepdf: function called with too few input arguments.> laplacepdf (1)
***** error<laplacepdf: function called with too few input arguments.> ...
 laplacepdf (1, 2)
***** error<laplacepdf: function called with too many inputs> laplacepdf (1, 2, 3, 4)
***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacepdf (1, ones (2), ones (3))
***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacepdf (ones (2), 1, ones (3))
***** error<laplacepdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacepdf (ones (2), ones (3), 1)
***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (i, 2, 3)
***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, i, 3)
***** error<laplacepdf: X, MU, and BETA must not be complex.> laplacepdf (1, 2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/exppdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/exppdf.m
***** demo
 ## Plot various PDFs from the exponential distribution
 x = 0:0.01:5;
 y1 = exppdf (x, 2/3);
 y2 = exppdf (x, 1.0);
 y3 = exppdf (x, 2.0);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r")
 grid on
 ylim ([0, 1.5])
 legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northeast")
 title ("Exponential PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x,y
 x = [-1 0 0.5 1 Inf];
 y = gampdf (x, 1, 2);
***** assert (exppdf (x, 2*ones (1,5)), y)
***** assert (exppdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
***** assert (exppdf ([x, NaN], 2), [y, NaN])
***** assert (exppdf (single ([x, NaN]), 2), single ([y, NaN]))
***** assert (exppdf ([x, NaN], single (2)), single ([y, NaN]))
***** error<exppdf: function called with too few input arguments.> exppdf ()
***** error<exppdf: function called with too many inputs> exppdf (1,2,3)
***** error<exppdf: X and MU must be of common size or scalars.> ...
 exppdf (ones (3), ones (2))
***** error<exppdf: X and MU must be of common size or scalars.> ...
 exppdf (ones (2), ones (3))
***** error<exppdf: X and MU must not be complex.> exppdf (i, 2)
***** error<exppdf: X and MU must not be complex.> exppdf (2, i)
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fun/poisspdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/poisspdf.m
***** demo
 ## Plot various PDFs from the Poisson distribution
 x = 0:20;
 y1 = poisspdf (x, 1);
 y2 = poisspdf (x, 4);
 y3 = poisspdf (x, 10);
 plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
 grid on
 ylim ([0, 0.4])
 legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "northeast")
 title ("Poisson PDF")
 xlabel ("values in x (number of occurences)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 Inf];
 y = [0, exp(-1)*[1 1 0.5], 0];
***** assert (poisspdf (x, ones (1,5)), y, eps)
***** assert (poisspdf (x, 1), y, eps)
***** assert (poisspdf (x, [1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)], eps)
***** assert (poisspdf ([x, NaN], 1), [y, NaN], eps)
***** assert (poisspdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
***** assert (poisspdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
***** error<poisspdf: function called with too few input arguments.> poisspdf ()
***** error<poisspdf: function called with too few input arguments.> poisspdf (1)
***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
 poisspdf (ones (3), ones (2))
***** error<poisspdf: X and LAMBDA must be of common size or scalars.> ...
 poisspdf (ones (2), ones (3))
***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (i, 2)
***** error<poisspdf: X and LAMBDA must not be complex.> poisspdf (2, i)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/gevcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gevcdf.m
***** demo
 ## Plot various CDFs from the generalized extreme value distribution
 x = -1:0.001:10;
 p1 = gevcdf (x, 1, 1, 1);
 p2 = gevcdf (x, 0.5, 1, 1);
 p3 = gevcdf (x, 1, 1, 5);
 p4 = gevcdf (x, 1, 2, 5);
 p5 = gevcdf (x, 1, 5, 5);
 p6 = gevcdf (x, 1, 0.5, 5);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
       x, p4, "-c", x, p5, "-m", x, p6, "-k")
 grid on
 xlim ([-1, 10])
 legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
          "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
          "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
         "location", "southeast")
 title ("Generalized extreme value CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 k = 1;
 mu = 0;
 p = gevcdf (x, k, sigma, mu);
 expected_p = [0.36788, 0.44933, 0.47237, 0.48323, 0.48954, 0.49367];
 assert (p, expected_p, 0.001);
***** test
 x = -0.5:0.5:2.5;
 sigma = 0.5;
 k = 1;
 mu = 0;
 p = gevcdf (x, k, sigma, mu);
 expected_p = [0, 0.36788, 0.60653, 0.71653, 0.77880, 0.81873, 0.84648];
 assert (p, expected_p, 0.001);
***** test # check for continuity for k near 0
 x = 1;
 sigma = 0.5;
 k = -0.03:0.01:0.03;
 mu = 0;
 p = gevcdf (x, k, sigma, mu);
 expected_p = [0.88062, 0.87820, 0.87580, 0.87342, 0.87107, 0.86874, 0.86643];
 assert (p, expected_p, 0.001);
***** error<gevcdf: function called with too few input arguments.> gevcdf ()
***** error<gevcdf: function called with too few input arguments.> gevcdf (1)
***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2)
***** error<gevcdf: function called with too few input arguments.> gevcdf (1, 2, 3)
***** error<gevcdf: function called with too many inputs> ...
 gevcdf (1, 2, 3, 4, 5, 6)
***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, "tail")
***** error<gevcdf: invalid argument for upper tail.> gevcdf (1, 2, 3, 4, 5)
***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevcdf (ones (3), ones (2), ones(2), ones(2))
***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevcdf (ones (2), ones (3), ones(2), ones(2))
***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevcdf (ones (2), ones (2), ones(3), ones(2))
***** error<gevcdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevcdf (ones (2), ones (2), ones(2), ones(3))
***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (i, 2, 3, 4)
***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, i, 3, 4)
***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, i, 4)
***** error<gevcdf: X, K, SIGMA, and MU must not be complex.> gevcdf (1, 2, 3, i)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/bvncdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/bvncdf.m
***** demo
 mu = [1, -1];
 sigma = [0.9, 0.4; 0.4, 0.3];
 [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 x = [X1(:), X2(:)];
 p = bvncdf (x, mu, sigma);
 Z = reshape (p, 25, 25);
 surf (X1, X2, Z);
 title ("Bivariate Normal Distribution");
 ylabel "X1"
 xlabel "X2"
***** test
 mu = [1, -1];
 sigma = [0.9, 0.4; 0.4, 0.3];
 [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 x = [X1(:), X2(:)];
 p = bvncdf (x, mu, sigma);
 p_out = [0.00011878988774500, 0.00034404112322371, ...
          0.00087682502191813, 0.00195221905058185, ...
          0.00378235566873474, 0.00638175749734415, ...
          0.00943764224329656, 0.01239164888125426, ...
          0.01472750274376648, 0.01623228313374828]';
 assert (p([1:10]), p_out, 1e-16);
***** test
 mu = [1, -1];
 sigma = [0.9, 0.4; 0.4, 0.3];
 [X1,X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 x = [X1(:), X2(:)];
 p = bvncdf (x, mu, sigma);
 p_out = [0.8180695783608276, 0.8854485749482751, ...
          0.9308108777385832, 0.9579855743025508, ...
          0.9722897881414742, 0.9788150170059926, ...
          0.9813597788804785, 0.9821977956568989, ...
          0.9824283794464095, 0.9824809345614861]';
 assert (p([616:625]), p_out, 3e-16);
***** test
 ## Test infinite limits
 mu = [0, 0];
 sigma = [1 0.5; 0.5 1];
 assert (bvncdf ([Inf, Inf], mu, sigma), 1);
 assert (bvncdf ([-Inf, 2], mu, sigma), 0);
 assert (bvncdf ([1, -Inf], mu, sigma), 0);
 assert (bvncdf ([0.5, Inf], mu, sigma), normcdf (0.5), eps);
 assert (bvncdf ([Inf, 0.5], mu, sigma), normcdf (0.5), eps);
***** error bvncdf (randn (25,3), [], [1, 1; 1, 1]);
***** error bvncdf (randn (25,2), [], [1, 1; 1, 1]);
***** error bvncdf (randn (25,2), [], ones (3, 2));
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/dist_fun/laplacecdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/laplacecdf.m
***** demo
 ## Plot various CDFs from the Laplace distribution
 x = -10:0.01:10;
 p1 = laplacecdf (x, 0, 1);
 p2 = laplacecdf (x, 0, 2);
 p3 = laplacecdf (x, 0, 4);
 p4 = laplacecdf (x, -5, 4);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([-10, 10])
 legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
          "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "southeast")
 title ("Laplace CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf, -log(2), 0, log(2), Inf];
 y = [0, 1/4, 1/2, 3/4, 1];
***** assert (laplacecdf ([x, NaN], 0, 1), [y, NaN])
***** assert (laplacecdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1])
***** assert (laplacecdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
***** assert (laplacecdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
***** assert (laplacecdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
***** error<laplacecdf: function called with too few input arguments.> laplacecdf ()
***** error<laplacecdf: function called with too few input arguments.> laplacecdf (1)
***** error<laplacecdf: function called with too few input arguments.> ...
 laplacecdf (1, 2)
***** error<laplacecdf: function called with too many inputs> ...
 laplacecdf (1, 2, 3, 4, 5)
***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, "tail")
***** error<laplacecdf: invalid argument for upper tail.> laplacecdf (1, 2, 3, 4)
***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacecdf (ones (3), ones (2), ones (2))
***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacecdf (ones (2), ones (3), ones (2))
***** error<laplacecdf: X, MU, and BETA must be of common size or scalars.> ...
 laplacecdf (ones (2), ones (2), ones (3))
***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (i, 2, 2)
***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, i, 2)
***** error<laplacecdf: X, MU, and BETA must not be complex.> laplacecdf (2, 2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/normrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/normrnd.m
***** assert (size (normrnd (1, 1)), [1, 1])
***** assert (size (normrnd (1, ones (2, 1))), [2, 1])
***** assert (size (normrnd (1, ones (2, 2))), [2, 2])
***** assert (size (normrnd (ones (2, 1), 1)), [2, 1])
***** assert (size (normrnd (ones (2, 2), 1)), [2, 2])
***** assert (size (normrnd (1, 1, 3)), [3, 3])
***** assert (size (normrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (normrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (normrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (normrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (normrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (normrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (normrnd (1, 1, [])), [0, 0])
***** assert (size (normrnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (normrnd (1, 1)), "double")
***** assert (class (normrnd (1, single (1))), "single")
***** assert (class (normrnd (1, single ([1, 1]))), "single")
***** assert (class (normrnd (single (1), 1)), "single")
***** assert (class (normrnd (single ([1, 1]), 1)), "single")
***** error<normrnd: function called with too few input arguments.> normrnd ()
***** error<normrnd: function called with too few input arguments.> normrnd (1)
***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
 normrnd (ones (3), ones (2))
***** error<normrnd: MU and SIGMA must be of common size or scalars.> ...
 normrnd (ones (2), ones (3))
***** error<normrnd: MU and SIGMA must not be complex.> normrnd (i, 2, 3)
***** error<normrnd: MU and SIGMA must not be complex.> normrnd (1, i, 3)
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, -1)
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, 1.2)
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, ones (2))
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, [2 -1 2])
***** error<normrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 normrnd (1, 2, [2 0 2.5])
***** error<normrnd: dimensions must be non-negative integers.> ...
 normrnd (1, 2, 2, -1, 5)
***** error<normrnd: dimensions must be non-negative integers.> ...
 normrnd (1, 2, 2, 1.5, 5)
***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
 normrnd (2, ones (2), 3)
***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
 normrnd (2, ones (2), [3, 2])
***** error<normrnd: MU and SIGMA must be scalars or of size SZ.> ...
 normrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/vminv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/vminv.m
***** demo
 ## Plot various iCDFs from the von Mises distribution
 p1 = [0,0.005,0.01:0.01:0.1,0.15,0.2:0.1:0.8,0.85,0.9:0.01:0.99,0.995,1];
 x1 = vminv (p1, 0, 0.5);
 x2 = vminv (p1, 0, 1);
 x3 = vminv (p1, 0, 2);
 x4 = vminv (p1, 0, 4);
 plot (p1, x1, "-r", p1, x2, "-g", p1, x3, "-b", p1, x4, "-c")
 grid on
 ylim ([-pi, pi])
 legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
          "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
 title ("Von Mises iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared x, p0, p1
 x = [-pi:pi/2:pi];
 p0 = [0, 0.10975, 0.5, 0.89025, 1];
 p1 = [0, 0.03752, 0.5, 0.99622, 1];
***** assert (vminv (p0, 0, 1), x, 5e-5)
***** assert (vminv (p0, zeros (1,5), ones (1,5)), x, 5e-5)
***** assert (vminv (p1, 0, [1 2 3 4 5]), x, [5e-5, 5e-4, 5e-5, 5e-4, 5e-5])
***** error<vminv: function called with too few input arguments.> vminv ()
***** error<vminv: function called with too few input arguments.> vminv (1)
***** error<vminv: function called with too few input arguments.> vminv (1, 2)
***** error<vminv: P, MU, and K must be of common size or scalars.> ...
 vminv (ones (3), ones (2), ones (2))
***** error<vminv: P, MU, and K must be of common size or scalars.> ...
 vminv (ones (2), ones (3), ones (2))
***** error<vminv: P, MU, and K must be of common size or scalars.> ...
 vminv (ones (2), ones (2), ones (3))
***** error<vminv: P, MU, and K must not be complex.> vminv (i, 2, 2)
***** error<vminv: P, MU, and K must not be complex.> vminv (2, i, 2)
***** error<vminv: P, MU, and K must not be complex.> vminv (2, 2, i)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvtcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/mvtcdf.m
***** demo
 ## Compute the cdf of a multivariate Student's t distribution with
 ## correlation parameters rho = [1, 0.4; 0.4, 1] and 2 degrees of freedom.

 rho = [1, 0.4; 0.4, 1];
 df = 2;
 [X1, X2] = meshgrid (linspace (-2, 2, 25)', linspace (-2, 2, 25)');
 X = [X1(:), X2(:)];
 p = mvtcdf (X, rho, df);
 surf (X1, X2, reshape (p, 25, 25));
 title ("Bivariate Student's t cumulative distribution function");
***** test
 x = [1, 2];
 rho = [1, 0.5; 0.5, 1];
 df = 4;
 a = [-1, 0];
 assert (mvtcdf(a, x, rho, df), 0.294196905339283, 1e-14);
***** test
 x = [1, 2;2, 4;1, 5];
 rho = [1, 0.5; 0.5, 1];
 df = 4;
 p =[0.790285178602166; 0.938703291727784; 0.81222737321336];
 assert (mvtcdf(x, rho, df), p, 1e-14);
***** test
 x = [1, 2, 2, 4, 1, 5];
 rho = eye (6);
 rho(rho == 0) = 0.5;
 df = 4;
 assert (mvtcdf(x, rho, df), 0.6874, 1e-4);
***** error mvtcdf (1)
***** error mvtcdf (1, 2)
***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
 mvtcdf (1, [2, 3; 3, 2], 1)
***** error<mvtcdf: correlation matrix RHO does not match dimensions in data.> ...
 mvtcdf ([2, 3, 4], ones (2), 1)
***** error<mvtcdf: X_LO and X_UP must be of the same size.> ...
 mvtcdf ([1, 2, 3], [2, 3], ones (2), 1)
***** error<mvtcdf: correlation matrix RHO must be positive semi-definite.> ...
 mvtcdf ([2, 3], ones (2), [1, 2, 3])
***** error<mvtcdf: DF must be a scalar or a vector with the same samples as in> ...
 mvtcdf ([2, 3], [1, 0.5; 0.5, 1], [1, 2, 3])
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fun/lognrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/lognrnd.m
***** assert (size (lognrnd (1, 1)), [1, 1])
***** assert (size (lognrnd (1, ones (2, 1))), [2, 1])
***** assert (size (lognrnd (1, ones (2, 2))), [2, 2])
***** assert (size (lognrnd (ones (2, 1), 1)), [2, 1])
***** assert (size (lognrnd (ones (2, 2), 1)), [2, 2])
***** assert (size (lognrnd (1, 1, 3)), [3, 3])
***** assert (size (lognrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (lognrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (lognrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (lognrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (lognrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (lognrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (lognrnd (1, 1, [])), [0, 0])
***** assert (size (lognrnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (lognrnd (1, 1)), "double")
***** assert (class (lognrnd (1, single (1))), "single")
***** assert (class (lognrnd (1, single ([1, 1]))), "single")
***** assert (class (lognrnd (single (1), 1)), "single")
***** assert (class (lognrnd (single ([1, 1]), 1)), "single")
***** error<lognrnd: function called with too few input arguments.> lognrnd ()
***** error<lognrnd: function called with too few input arguments.> lognrnd (1)
***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
 lognrnd (ones (3), ones (2))
***** error<lognrnd: MU and SIGMA must be of common size or scalars.> ...
 lognrnd (ones (2), ones (3))
***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (i, 2, 3)
***** error<lognrnd: MU and SIGMA must not be complex.> lognrnd (1, i, 3)
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, -1)
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, 1.2)
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, ones (2))
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, [2 -1 2])
***** error<lognrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 lognrnd (1, 2, [2 0 2.5])
***** error<lognrnd: dimensions must be non-negative integers.> ...
 lognrnd (1, 2, 2, -1, 5)
***** error<lognrnd: dimensions must be non-negative integers.> ...
 lognrnd (1, 2, 2, 1.5, 5)
***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
 lognrnd (2, ones (2), 3)
***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
 lognrnd (2, ones (2), [3, 2])
***** error<lognrnd: MU and SIGMA must be scalars or of size SZ.> ...
 lognrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncx2inv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/ncx2inv.m
***** demo
 ## Plot various iCDFs from the noncentral chi-squared distribution
 p = 0.001:0.001:0.999;
 x1 = ncx2inv (p, 2, 1);
 x2 = ncx2inv (p, 2, 2);
 x3 = ncx2inv (p, 2, 3);
 x4 = ncx2inv (p, 4, 1);
 x5 = ncx2inv (p, 4, 2);
 x6 = ncx2inv (p, 4, 3);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", ...
       p, x4, "-m", p, x5, "-c", p, x6, "-y")
 grid on
 ylim ([0, 10])
 legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
          "df = 2, λ = 3", "df = 4, λ = 1", ...
          "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northwest")
 title ("Noncentral chi-squared iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** demo
 ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
 ## chi-squared CDF with the same number of degrees of freedom (4).

 p = 0.001:0.001:0.999;
 x1 = ncx2inv (p, 4, 2);
 x2 = chi2inv (p, 4);
 plot (p, x1, "-", p, x2, "-");
 grid on
 ylim ([0, 10])
 legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
 title ("Noncentral chi-squared vs chi-squared quantile functions")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 x = [0,0.3443,0.7226,1.1440,1.6220,2.1770,2.8436,3.6854,4.8447,6.7701,Inf];
 assert (ncx2inv ([0:0.1:1], 2, 1), x, 1e-4);
***** test
 x = [0,0.8295,1.6001,2.3708,3.1785,4.0598,5.0644,6.2765,7.8763,10.4199,Inf];
 assert (ncx2inv ([0:0.1:1], 2, 3), x, 1e-4);
***** test
 x = [0,0.5417,1.3483,2.1796,3.0516,4.0003,5.0777,6.3726,8.0748,10.7686,Inf];
 assert (ncx2inv ([0:0.1:1], 1, 4), x, 1e-4);
***** test
 x = [0.1808, 0.6456, 1.1842, 1.7650, 2.3760, 3.0105];
 assert (ncx2inv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
***** test
 x = [0.4887, 0.6699, 0.9012, 1.1842, 1.5164, 1.8927];
 assert (ncx2inv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
***** test
 x = [1.3941, 1.6824, 2.0103, 2.3760, NaN, 3.2087];
 assert (ncx2inv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
***** test
 assert (ncx2inv (0.996, 5, 8), 35.51298862765576, 3e-13);
***** error<ncx2inv: function called with too few input arguments.> ncx2inv ()
***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1)
***** error<ncx2inv: function called with too few input arguments.> ncx2inv (1, 2)
***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2inv (ones (3), ones (2), ones (2))
***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2inv (ones (2), ones (3), ones (2))
***** error<ncx2inv: P, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2inv (ones (2), ones (2), ones (3))
***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (i, 2, 2)
***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, i, 2)
***** error<ncx2inv: P, DF, and LAMBDA must not be complex.> ncx2inv (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/evpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/evpdf.m
***** demo
 ## Plot various PDFs from the Extreme value distribution
 x = -10:0.001:10;
 y1 = evpdf (x, 0.5, 2);
 y2 = evpdf (x, 1.0, 2);
 y3 = evpdf (x, 1.5, 3);
 y4 = evpdf (x, 3.0, 4);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 ylim ([0, 0.2])
 legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
          "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northeast")
 title ("Extreme value PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y0, y1
 x = [-5, 0, 1, 2, 3];
 y0 = [0.0067, 0.3679, 0.1794, 0.0046, 0];
 y1 = [0.0025, 0.2546, 0.3679, 0.1794, 0.0046];
***** assert (evpdf (x), y0, 1e-4)
***** assert (evpdf (x, zeros (1,5), ones (1,5)), y0, 1e-4)
***** assert (evpdf (x, ones (1,5), ones (1,5)), y1, 1e-4)
***** error<evpdf: function called with too few input arguments.> evpdf ()
***** error<evpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 evpdf (ones (3), ones (2), ones (2))
***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (i, 2, 2)
***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, i, 2)
***** error<evpdf: X, MU, and SIGMA must not be complex.> evpdf (2, 2, i)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/binoinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/binoinv.m
***** demo
 ## Plot various iCDFs from the binomial distribution
 p = 0.001:0.001:0.999;
 x1 = binoinv (p, 20, 0.5);
 x2 = binoinv (p, 20, 0.7);
 x3 = binoinv (p, 40, 0.5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 legend ({"n = 20, ps = 0.5", "n = 20, ps = 0.7", ...
          "n = 40, ps = 0.5"}, "location", "southeast")
 title ("Binomial iCDF")
 xlabel ("probability")
 ylabel ("values in x (number of successes)")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (binoinv (p, 2*ones (1,5), 0.5*ones (1,5)), [NaN 0 1 2 NaN])
***** assert (binoinv (p, 2, 0.5*ones (1,5)), [NaN 0 1 2 NaN])
***** assert (binoinv (p, 2*ones (1,5), 0.5), [NaN 0 1 2 NaN])
***** assert (binoinv (p, 2*[0 -1 NaN 1.1 1], 0.5), [NaN NaN NaN NaN NaN])
***** assert (binoinv (p, 2, 0.5*[0 -1 NaN 3 1]), [NaN NaN NaN NaN NaN])
***** assert (binoinv ([p(1:2) NaN p(4:5)], 2, 0.5), [NaN 0 NaN 2 NaN])
***** assert (binoinv ([p, NaN], 2, 0.5), [NaN 0 1 2 NaN NaN])
***** assert (binoinv (single ([p, NaN]), 2, 0.5), single ([NaN 0 1 2 NaN NaN]))
***** assert (binoinv ([p, NaN], single (2), 0.5), single ([NaN 0 1 2 NaN NaN]))
***** assert (binoinv ([p, NaN], 2, single (0.5)), single ([NaN 0 1 2 NaN NaN]))
***** shared x, tol
 x = magic (3) + 1;
 tol = 1;
***** assert (binoinv (binocdf (1:10, 11, 0.1), 11, 0.1), 1:10, tol)
***** assert (binoinv (binocdf (1:10, 2*(1:10), 0.1), 2*(1:10), 0.1), 1:10, tol)
***** assert (binoinv (binocdf (x, 2*x, 1./x), 2*x, 1./x), x, tol)
***** error<binoinv: function called with too few input arguments.> binoinv ()
***** error<binoinv: function called with too few input arguments.> binoinv (1)
***** error<binoinv: function called with too few input arguments.> binoinv (1,2)
***** error<binoinv: function called with too many inputs> binoinv (1,2,3,4)
***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
 binoinv (ones (3), ones (2), ones (2))
***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
 binoinv (ones (2), ones (3), ones (2))
***** error<binoinv: P, N, and PS must be of common size or scalars.> ...
 binoinv (ones (2), ones (2), ones (3))
***** error<binoinv: P, N, and PS must not be complex.> binoinv (i, 2, 2)
***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, i, 2)
***** error<binoinv: P, N, and PS must not be complex.> binoinv (2, 2, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/hygecdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/hygecdf.m
***** demo
 ## Plot various CDFs from the hypergeometric distribution
 x = 0:60;
 p1 = hygecdf (x, 500, 50, 100);
 p2 = hygecdf (x, 500, 60, 200);
 p3 = hygecdf (x, 500, 70, 300);
 plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
 grid on
 xlim ([0, 60])
 legend ({"m = 500, k = 50, n = 100", "m = 500, k = 60, n = 200", ...
          "m = 500, k = 70, n = 300"}, "location", "southeast")
 title ("Hypergeometric CDF")
 xlabel ("values in x (number of successes)")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 1 2 3];
 y = [0 1/6 5/6 1 1];
***** assert (hygecdf (x, 4*ones (1,5), 2, 2), y, 5*eps)
***** assert (hygecdf (x, 4, 2*ones (1,5), 2), y, 5*eps)
***** assert (hygecdf (x, 4, 2, 2*ones (1,5)), y, 5*eps)
***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2), [y(1) NaN NaN NaN y(5)], 5*eps)
***** assert (hygecdf (x, 4*[1 -1 NaN 1.1 1], 2, 2, "upper"), ...
 [y(5) NaN NaN NaN y(1)], 5*eps)
***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2), [y(1) NaN NaN NaN y(5)], 5*eps)
***** assert (hygecdf (x, 4, 2*[1 -1 NaN 1.1 1], 2, "upper"), ...
 [y(5) NaN NaN NaN y(1)], 5*eps)
***** assert (hygecdf (x, 4, 5, 2), [NaN NaN NaN NaN NaN])
***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1]), [y(1) NaN NaN NaN y(5)], 5*eps)
***** assert (hygecdf (x, 4, 2, 2*[1 -1 NaN 1.1 1], "upper"), ...
 [y(5) NaN NaN NaN y(1)], 5*eps)
***** assert (hygecdf (x, 4, 2, 5), [NaN NaN NaN NaN NaN])
***** assert (hygecdf ([x(1:2) NaN x(4:5)], 4, 2, 2), [y(1:2) NaN y(4:5)], 5*eps)
***** test
 p = hygecdf (x, 10, [1 2 3 4 5], 2, "upper");
 assert (p, [1, 34/90, 2/30, 0, 0], 10*eps);
***** test
 p = hygecdf (2*x, 10, [1 2 3 4 5], 2, "upper");
 assert (p, [1, 34/90, 0, 0, 0], 10*eps);
***** assert (hygecdf ([x, NaN], 4, 2, 2), [y, NaN], 5*eps)
***** assert (hygecdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), ...
 eps ("single"))
***** assert (hygecdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), ...
 eps ("single"))
***** assert (hygecdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), ...
 eps ("single"))
***** assert (hygecdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), ...
 eps ("single"))
***** error<hygecdf: function called with too few input arguments.> hygecdf ()
***** error<hygecdf: function called with too few input arguments.> hygecdf (1)
***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2)
***** error<hygecdf: function called with too few input arguments.> hygecdf (1,2,3)
***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,5)
***** error<hygecdf: invalid argument for upper tail.> hygecdf (1,2,3,4,"uper")
***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
 hygecdf (ones (2), ones (3), 1, 1)
***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
 hygecdf (1, ones (2), ones (3), 1)
***** error<hygecdf: X, T, k, and N must be of common size or scalars.> ...
 hygecdf (1, 1, ones (2), ones (3))
***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (i, 2, 2, 2)
***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, i, 2, 2)
***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, i, 2)
***** error<hygecdf: X, T, k, and N must not be complex.> hygecdf (2, 2, 2, i)
32 tests, 32 passed, 0 known failure, 0 skipped
[inst/dist_fun/nctrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/nctrnd.m
***** assert (size (nctrnd (1, 1)), [1, 1])
***** assert (size (nctrnd (1, ones (2, 1))), [2, 1])
***** assert (size (nctrnd (1, ones (2, 2))), [2, 2])
***** assert (size (nctrnd (ones (2, 1), 1)), [2, 1])
***** assert (size (nctrnd (ones (2, 2), 1)), [2, 2])
***** assert (size (nctrnd (1, 1, 3)), [3, 3])
***** assert (size (nctrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (nctrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (nctrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (nctrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (nctrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (nctrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (nctrnd (1, 1, [])), [0, 0])
***** assert (size (nctrnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (nctrnd (1, 1)), "double")
***** assert (class (nctrnd (1, single (1))), "single")
***** assert (class (nctrnd (1, single ([1, 1]))), "single")
***** assert (class (nctrnd (single (1), 1)), "single")
***** assert (class (nctrnd (single ([1, 1]), 1)), "single")
***** error<nctrnd: function called with too few input arguments.> nctrnd ()
***** error<nctrnd: function called with too few input arguments.> nctrnd (1)
***** error<nctrnd: DF and MU must be of common size or scalars.> ...
 nctrnd (ones (3), ones (2))
***** error<nctrnd: DF and MU must be of common size or scalars.> ...
 nctrnd (ones (2), ones (3))
***** error<nctrnd: DF and MU must not be complex.> nctrnd (i, 2)
***** error<nctrnd: DF and MU must not be complex.> nctrnd (1, i)
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, -1)
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, 1.2)
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, ones (2))
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, [2 -1 2])
***** error<nctrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nctrnd (1, 2, [2 0 2.5])
***** error<nctrnd: dimensions must be non-negative integers.> ...
 nctrnd (1, 2, 2, -1, 5)
***** error<nctrnd: dimensions must be non-negative integers.> ...
 nctrnd (1, 2, 2, 1.5, 5)
***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
 nctrnd (2, ones (2), 3)
***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
 nctrnd (2, ones (2), [3, 2])
***** error<nctrnd: DF and MU must be scalars or of size SZ.> ...
 nctrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/chi2rnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/chi2rnd.m
***** assert (size (chi2rnd (2)), [1, 1])
***** assert (size (chi2rnd (ones (2,1))), [2, 1])
***** assert (size (chi2rnd (ones (2,2))), [2, 2])
***** assert (size (chi2rnd (1, 3)), [3, 3])
***** assert (size (chi2rnd (1, [4, 1])), [4, 1])
***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
***** assert (size (chi2rnd (1, 4, 1)), [4, 1])
***** assert (size (chi2rnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (chi2rnd (1, 0, 1)), [0, 1])
***** assert (size (chi2rnd (1, 1, 0)), [1, 0])
***** assert (size (chi2rnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (chi2rnd (1, [])), [0, 0])
***** assert (size (chi2rnd (1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (chi2rnd (2)), "double")
***** assert (class (chi2rnd (single (2))), "single")
***** assert (class (chi2rnd (single ([2 2]))), "single")
***** error<chi2rnd: function called with too few input arguments.> chi2rnd ()
***** error<chi2rnd: DF must not be complex.> chi2rnd (i)
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, -1)
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, 1.2)
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, ones (2))
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, [2 -1 2])
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (1, [2 0 2.5])
***** error<chi2rnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 chi2rnd (ones (2), ones (2))
***** error<chi2rnd: dimensions must be non-negative integers.> ...
 chi2rnd (1, 2, -1, 5)
***** error<chi2rnd: dimensions must be non-negative integers.> ...
 chi2rnd (1, 2, 1.5, 5)
***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 3)
***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), [3, 2])
***** error<chi2rnd: DF must be scalar or of size SZ.> chi2rnd (ones (2,2), 2, 3)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/bisainv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/bisainv.m
***** demo
 ## Plot various iCDFs from the Birnbaum-Saunders distribution
 p = 0.001:0.001:0.999;
 x1 = bisainv (p, 1, 0.5);
 x2 = bisainv (p, 1, 1);
 x3 = bisainv (p, 1, 2);
 x4 = bisainv (p, 1, 5);
 x5 = bisainv (p, 1, 10);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 ylim ([0, 10])
 legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
          "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "northwest")
 title ("Birnbaum-Saunders iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** demo
 ## Plot various iCDFs from the Birnbaum-Saunders distribution
 p = 0.001:0.001:0.999;
 x1 = bisainv (p, 1, 0.3);
 x2 = bisainv (p, 2, 0.3);
 x3 = bisainv (p, 1, 0.5);
 x4 = bisainv (p, 3, 0.5);
 x5 = bisainv (p, 5, 0.5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 ylim ([0, 10])
 legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
          "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "northwest")
 title ("Birnbaum-Saunders iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y, f
 f = @(p,b,c) (b * (c * norminv (p) + sqrt (4 + (c * norminv(p))^2))^2) / 4;
 p = [-1, 0, 1/4, 1/2, 1, 2];
 y = [NaN, 0, f(1/4, 1, 1), 1, Inf, NaN];
***** assert (bisainv (p, ones (1,6), ones (1,6)), y)
***** assert (bisainv (p, 1, ones (1,6)), y)
***** assert (bisainv (p, ones (1,6), 1), y)
***** assert (bisainv (p, 1, 1), y)
***** assert (bisainv (p, 1, [1, 1, 1, NaN, 1, 1]), [y(1:3), NaN, y(5:6)])
***** assert (bisainv (p, [1, 1, 1, NaN, 1, 1], 1), [y(1:3), NaN, y(5:6)])
***** assert (bisainv ([p, NaN], 1, 1), [y, NaN])
***** assert (bisainv (single ([p, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (bisainv ([p, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** assert (bisainv ([p, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** error<bisainv: function called with too few input arguments.> bisainv ()
***** error<bisainv: function called with too few input arguments.> bisainv (1)
***** error<bisainv: function called with too few input arguments.> bisainv (1, 2)
***** error<bisainv: function called with too many inputs> bisainv (1, 2, 3, 4)
***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
 bisainv (ones (3), ones (2), ones(2))
***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
 bisainv (ones (2), ones (3), ones(2))
***** error<bisainv: P, BETA, and GAMMA must be of common size or scalars.> ...
 bisainv (ones (2), ones (2), ones(3))
***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (i, 4, 3)
***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, i, 3)
***** error<bisainv: P, BETA, and GAMMA must not be complex.> bisainv (1, 4, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/unidrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/unidrnd.m
***** assert (size (unidrnd (2)), [1, 1])
***** assert (size (unidrnd (ones (2, 1))), [2, 1])
***** assert (size (unidrnd (ones (2, 2))), [2, 2])
***** assert (size (unidrnd (1, 3)), [3, 3])
***** assert (size (unidrnd (1, [4, 1])), [4, 1])
***** assert (size (unidrnd (1, 4, 1)), [4, 1])
***** assert (size (unidrnd (1, 4, 1)), [4, 1])
***** assert (size (unidrnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (unidrnd (1, 0, 1)), [0, 1])
***** assert (size (unidrnd (1, 1, 0)), [1, 0])
***** assert (size (unidrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (unidrnd (1, [])), [0, 0])
***** assert (size (unidrnd (1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (unidrnd (0, 1, 1), NaN)
***** assert (unidrnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
***** assert (class (unidrnd (2)), "double")
***** assert (class (unidrnd (single (2))), "single")
***** assert (class (unidrnd (single ([2, 2]))), "single")
***** error<unidrnd: function called with too few input arguments.> unidrnd ()
***** error<unidrnd: N must not be complex.> unidrnd (i)
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, -1)
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, 1.2)
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, ones (2))
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, [2 -1 2])
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (1, [2 0 2.5])
***** error<unidrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unidrnd (ones (2), ones (2))
***** error<unidrnd: dimensions must be non-negative integers.> ...
 unidrnd (1, 2, -1, 5)
***** error<unidrnd: dimensions must be non-negative integers.> ...
 unidrnd (1, 2, 1.5, 5)
***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 3)
***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), [3, 2])
***** error<unidrnd: N must be scalar or of size SZ.> unidrnd (ones (2,2), 2, 3)
31 tests, 31 passed, 0 known failure, 0 skipped
[inst/dist_fun/unifcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/unifcdf.m
***** demo
 ## Plot various CDFs from the continuous uniform distribution
 x = 0:0.1:10;
 p1 = unifcdf (x, 2, 5);
 p2 = unifcdf (x, 3, 9);
 plot (x, p1, "-b", x, p2, "-g")
 grid on
 xlim ([0, 10])
 ylim ([0, 1])
 legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "southeast")
 title ("Continuous uniform CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 0.5 1 2] + 1;
 y = [0 0 0.5 1 1];
***** assert (unifcdf (x, ones (1,5), 2*ones (1,5)), y)
***** assert (unifcdf (x, ones (1,5), 2*ones (1,5), "upper"), 1 - y)
***** assert (unifcdf (x, 1, 2*ones (1,5)), y)
***** assert (unifcdf (x, 1, 2*ones (1,5), "upper"), 1 - y)
***** assert (unifcdf (x, ones (1,5), 2), y)
***** assert (unifcdf (x, ones (1,5), 2, "upper"), 1 - y)
***** assert (unifcdf (x, [2 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
***** assert (unifcdf (x, [2 1 NaN 1 1], 2, "upper"), 1 - [NaN 0 NaN 1 1])
***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1]), [NaN 0 NaN 1 1])
***** assert (unifcdf (x, 1, 2*[0 1 NaN 1 1], "upper"), 1 - [NaN 0 NaN 1 1])
***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
***** assert (unifcdf ([x(1:2) NaN x(4:5)], 1, 2, "upper"), 1 - [y(1:2) NaN y(4:5)])
***** assert (unifcdf ([x, NaN], 1, 2), [y, NaN])
***** assert (unifcdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
***** assert (unifcdf ([x, NaN], single (1), 2), single ([y, NaN]))
***** assert (unifcdf ([x, NaN], 1, single (2)), single ([y, NaN]))
***** error<unifcdf: function called with too few input arguments.> unifcdf ()
***** error<unifcdf: function called with too few input arguments.> unifcdf (1)
***** error<unifcdf: function called with too few input arguments.> unifcdf (1, 2)
***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, 4)
***** error<unifcdf: invalid argument for upper tail.> unifcdf (1, 2, 3, "tail")
***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
 unifcdf (ones (3), ones (2), ones (2))
***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
 unifcdf (ones (2), ones (3), ones (2))
***** error<unifcdf: X, A, and B must be of common size or scalars.> ...
 unifcdf (ones (2), ones (2), ones (3))
***** error<unifcdf: X, A, and B must not be complex.> unifcdf (i, 2, 2)
***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, i, 2)
***** error<unifcdf: X, A, and B must not be complex.> unifcdf (2, 2, i)
27 tests, 27 passed, 0 known failure, 0 skipped
[inst/dist_fun/gpinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gpinv.m
***** demo
 ## Plot various iCDFs from the generalized Pareto distribution
 p = 0.001:0.001:0.999;
 x1 = gpinv (p, 1, 1, 0);
 x2 = gpinv (p, 5, 1, 0);
 x3 = gpinv (p, 20, 1, 0);
 x4 = gpinv (p, 1, 2, 0);
 x5 = gpinv (p, 5, 2, 0);
 x6 = gpinv (p, 20, 2, 0);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
       p, x4, "-c", p, x5, "-m", p, x6, "-k")
 grid on
 ylim ([0, 5])
 legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
          "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
          "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
         "location", "southeast")
 title ("Generalized Pareto iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y1, y2, y3
 p = [-1, 0, 1/2, 1, 2];
 y1 = [NaN, 0, 0.6931471805599453, Inf, NaN];
 y2 = [NaN, 0, 1, Inf, NaN];
 y3 = [NaN, 0, 1/2, 1, NaN];
***** assert (gpinv (p, zeros (1,5), ones (1,5), zeros (1,5)), y1)
***** assert (gpinv (p, 0, 1, zeros (1,5)), y1)
***** assert (gpinv (p, 0, ones (1,5), 0), y1)
***** assert (gpinv (p, zeros (1,5), 1, 0), y1)
***** assert (gpinv (p, 0, 1, 0), y1)
***** assert (gpinv (p, 0, 1, [0, 0, NaN, 0, 0]), [y1(1:2), NaN, y1(4:5)])
***** assert (gpinv (p, 0, [1, 1, NaN, 1, 1], 0), [y1(1:2), NaN, y1(4:5)])
***** assert (gpinv (p, [0, 0, NaN, 0, 0], 1, 0), [y1(1:2), NaN, y1(4:5)])
***** assert (gpinv ([p(1:2), NaN, p(4:5)], 0, 1, 0), [y1(1:2), NaN, y1(4:5)])
***** assert (gpinv (p, ones (1,5), ones (1,5), zeros (1,5)), y2)
***** assert (gpinv (p, 1, 1, zeros (1,5)), y2)
***** assert (gpinv (p, 1, ones (1,5), 0), y2)
***** assert (gpinv (p, ones (1,5), 1, 0), y2)
***** assert (gpinv (p, 1, 1, 0), y2)
***** assert (gpinv (p, 1, 1, [0, 0, NaN, 0, 0]), [y2(1:2), NaN, y2(4:5)])
***** assert (gpinv (p, 1, [1, 1, NaN, 1, 1], 0), [y2(1:2), NaN, y2(4:5)])
***** assert (gpinv (p, [1, 1, NaN, 1, 1], 1, 0), [y2(1:2), NaN, y2(4:5)])
***** assert (gpinv ([p(1:2), NaN, p(4:5)], 1, 1, 0), [y2(1:2), NaN, y2(4:5)])
***** assert (gpinv (p, -ones (1,5), ones (1,5), zeros (1,5)), y3)
***** assert (gpinv (p, -1, 1, zeros (1,5)), y3)
***** assert (gpinv (p, -1, ones (1,5), 0), y3)
***** assert (gpinv (p, -ones (1,5), 1, 0), y3)
***** assert (gpinv (p, -1, 1, 0), y3)
***** assert (gpinv (p, -1, 1, [0, 0, NaN, 0, 0]), [y3(1:2), NaN, y3(4:5)])
***** assert (gpinv (p, -1, [1, 1, NaN, 1, 1], 0), [y3(1:2), NaN, y3(4:5)])
***** assert (gpinv (p, -[1, 1, NaN, 1, 1], 1, 0), [y3(1:2), NaN, y3(4:5)])
***** assert (gpinv ([p(1:2), NaN, p(4:5)], -1, 1, 0), [y3(1:2), NaN, y3(4:5)])
***** assert (gpinv (single ([p, NaN]), 0, 1, 0), single ([y1, NaN]))
***** assert (gpinv ([p, NaN], 0, 1, single (0)), single ([y1, NaN]))
***** assert (gpinv ([p, NaN], 0, single (1), 0), single ([y1, NaN]))
***** assert (gpinv ([p, NaN], single (0), 1, 0), single ([y1, NaN]))
***** assert (gpinv (single ([p, NaN]), 1, 1, 0), single ([y2, NaN]))
***** assert (gpinv ([p, NaN], 1, 1, single (0)), single ([y2, NaN]))
***** assert (gpinv ([p, NaN], 1, single (1), 0), single ([y2, NaN]))
***** assert (gpinv ([p, NaN], single (1), 1, 0), single ([y2, NaN]))
***** assert (gpinv (single ([p, NaN]), -1, 1, 0), single ([y3, NaN]))
***** assert (gpinv ([p, NaN], -1, 1, single (0)), single ([y3, NaN]))
***** assert (gpinv ([p, NaN], -1, single (1), 0), single ([y3, NaN]))
***** assert (gpinv ([p, NaN], single (-1), 1, 0), single ([y3, NaN]))
***** error<gpinv: function called with too few input arguments.> gpinv ()
***** error<gpinv: function called with too few input arguments.> gpinv (1)
***** error<gpinv: function called with too few input arguments.> gpinv (1, 2)
***** error<gpinv: function called with too few input arguments.> gpinv (1, 2, 3)
***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpinv (ones (3), ones (2), ones(2), ones(2))
***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpinv (ones (2), ones (3), ones(2), ones(2))
***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpinv (ones (2), ones (2), ones(3), ones(2))
***** error<gpinv: P, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpinv (ones (2), ones (2), ones(2), ones(3))
***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (i, 2, 3, 4)
***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, i, 3, 4)
***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, i, 4)
***** error<gpinv: P, K, SIGMA, and THETA must not be complex.> gpinv (1, 2, 3, i)
51 tests, 51 passed, 0 known failure, 0 skipped
[inst/dist_fun/bisarnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/bisarnd.m
***** assert (size (bisarnd (1, 1)), [1 1])
***** assert (size (bisarnd (1, ones (2,1))), [2, 1])
***** assert (size (bisarnd (1, ones (2,2))), [2, 2])
***** assert (size (bisarnd (ones (2,1), 1)), [2, 1])
***** assert (size (bisarnd (ones (2,2), 1)), [2, 2])
***** assert (size (bisarnd (1, 1, 3)), [3, 3])
***** assert (size (bisarnd (1, 1, [4, 1])), [4, 1])
***** assert (size (bisarnd (1, 1, 4, 1)), [4, 1])
***** assert (size (bisarnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (bisarnd (1, 1, 0, 1)), [0, 1])
***** assert (size (bisarnd (1, 1, 1, 0)), [1, 0])
***** assert (size (bisarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (bisarnd (1, 1, [])), [0, 0])
***** assert (size (bisarnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (bisarnd (1, 1)), "double")
***** assert (class (bisarnd (1, single (1))), "single")
***** assert (class (bisarnd (1, single ([1, 1]))), "single")
***** assert (class (bisarnd (single (1), 1)), "single")
***** assert (class (bisarnd (single ([1, 1]), 1)), "single")
***** error<bisarnd: function called with too few input arguments.> bisarnd ()
***** error<bisarnd: function called with too few input arguments.> bisarnd (1)
***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
 bisarnd (ones (3), ones (2))
***** error<bisarnd: BETA and GAMMA must be of common size or scalars.> ...
 bisarnd (ones (2), ones (3))
***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (i, 2, 3)
***** error<bisarnd: BETA and GAMMA must not be complex.> bisarnd (1, i, 3)
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, -1)
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, 1.2)
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, ones (2))
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, [2 -1 2])
***** error<bisarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 bisarnd (1, 2, [2 0 2.5])
***** error<bisarnd: dimensions must be non-negative integers.> ...
 bisarnd (1, 2, 2, -1, 5)
***** error<bisarnd: dimensions must be non-negative integers.> ...
 bisarnd (1, 2, 2, 1.5, 5)
***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
 bisarnd (2, ones (2), 3)
***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
 bisarnd (2, ones (2), [3, 2])
***** error<bisarnd: BETA and GAMMA must be scalars or of size SZ.> ...
 bisarnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/fcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/fcdf.m
***** demo
 ## Plot various CDFs from the F distribution
 x = 0.01:0.01:4;
 p1 = fcdf (x, 1, 2);
 p2 = fcdf (x, 2, 1);
 p3 = fcdf (x, 5, 2);
 p4 = fcdf (x, 10, 1);
 p5 = fcdf (x, 100, 100);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
          "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
          "df1 = 100, df2 = 100"}, "location", "southeast")
 title ("F CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 0.5, 1, 2, Inf];
 y = [0, 0, 1/3, 1/2, 2/3, 1];
***** assert (fcdf (x, 2*ones (1,6), 2*ones (1,6)), y, eps)
***** assert (fcdf (x, 2, 2*ones (1,6)), y, eps)
***** assert (fcdf (x, 2*ones (1,6), 2), y, eps)
***** assert (fcdf (x, [0 NaN Inf 2 2 2], 2), [NaN NaN 0.1353352832366127 y(4:6)], eps)
***** assert (fcdf (x, 2, [0 NaN Inf 2 2 2]), [NaN NaN 0.3934693402873666 y(4:6)], eps)
***** assert (fcdf ([x(1:2) NaN x(4:6)], 2, 2), [y(1:2) NaN y(4:6)], eps)
***** assert (fcdf ([x, NaN], 2, 2), [y, NaN], eps)
***** assert (fcdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
***** assert (fcdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
***** assert (fcdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
***** error<fcdf: function called with too few input arguments.> fcdf ()
***** error<fcdf: function called with too few input arguments.> fcdf (1)
***** error<fcdf: function called with too few input arguments.> fcdf (1, 2)
***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, 4)
***** error<fcdf: invalid argument for upper tail.> fcdf (1, 2, 3, "tail")
***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fcdf (ones (3), ones (2), ones (2))
***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fcdf (ones (2), ones (3), ones (2))
***** error<fcdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fcdf (ones (2), ones (2), ones (3))
***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (i, 2, 2)
***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, i, 2)
***** error<fcdf: X, DF1, and DF2 must not be complex.> fcdf (2, 2, i)
21 tests, 21 passed, 0 known failure, 0 skipped
[inst/dist_fun/loglinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/loglinv.m
***** demo
 ## Plot various iCDFs from the log-logistic distribution
 p = 0.001:0.001:0.999;
 x1 = loglinv (p, log (1), 1/0.5);
 x2 = loglinv (p, log (1), 1);
 x3 = loglinv (p, log (1), 1/2);
 x4 = loglinv (p, log (1), 1/4);
 x5 = loglinv (p, log (1), 1/8);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 ylim ([0, 20])
 grid on
 legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
          "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
 title ("Log-logistic iCDF")
 xlabel ("probability")
 ylabel ("x")
 text (0.03, 12.5, "μ = 0 (α = 1), values of σ (β) as shown in legend")
***** shared p, out1, out2
 p = [-1, 0, 0.2, 0.5, 0.8, 0.95, 1, 2];
 out1 = [NaN, 0, 0.25, 1, 4, 19, Inf, NaN];
 out2 = [NaN, 0, 0.0424732, 2.718282, 173.970037, 18644.695061, Inf, NaN];
***** assert (loglinv (p, 0, 1), out1, 1e-8)
***** assert (loglinv (p, 0, 1), out1, 1e-8)
***** assert (loglinv (p, 1, 3), out2, 1e-6)
***** assert (class (loglinv (single (1), 2, 3)), "single")
***** assert (class (loglinv (1, single (2), 3)), "single")
***** assert (class (loglinv (1, 2, single (3))), "single")
***** error<loglinv: function called with too few input arguments.> loglinv (1)
***** error<loglinv: function called with too few input arguments.> loglinv (1, 2)
***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
 loglinv (1, ones (2), ones (3))
***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
 loglinv (ones (2), 1, ones (3))
***** error<loglinv: P, MU, and SIGMA must be of common size or scalars.> ...
 loglinv (ones (2), ones (3), 1)
***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (i, 2, 3)
***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, i, 3)
***** error<loglinv: P, MU, and SIGMA must not be complex.> loglinv (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/gumbelcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gumbelcdf.m
***** demo
 ## Plot various CDFs from the Gumbel distribution
 x = -5:0.01:20;
 p1 = gumbelcdf (x, 0.5, 2);
 p2 = gumbelcdf (x, 1.0, 2);
 p3 = gumbelcdf (x, 1.5, 3);
 p4 = gumbelcdf (x, 3.0, 4);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
          "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "southeast")
 title ("Gumbel CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf, 1, 2, Inf];
 y = [0, 0.3679, 0.6922, 1];
***** assert (gumbelcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
***** assert (gumbelcdf (x, 1, ones (1,4)), y, 1e-4)
***** assert (gumbelcdf (x, ones (1,4), 1), y, 1e-4)
***** assert (gumbelcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
***** assert (gumbelcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
***** assert (gumbelcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
***** assert (gumbelcdf (x, "upper"), [1, 0.3078, 0.1266, 0], 1e-4)
***** assert (gumbelcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
***** assert (gumbelcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
***** assert (gumbelcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
***** assert (gumbelcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf ()
***** error<gumbelcdf: invalid number of input arguments.> gumbelcdf (1,2,3,4,5,6,7)
***** error<gumbelcdf: invalid argument for upper tail.> gumbelcdf (1, 2, 3, 4, "uper")
***** error<gumbelcdf: X, MU, and BETA must be of common size or scalars.> ...
 gumbelcdf (ones (3), ones (2), ones (2))
***** error<gumbelcdf: invalid size of covariance matrix.> gumbelcdf (2, 3, 4, [1, 2])
***** error<gumbelcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = gumbelcdf (1, 2, 3)
***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
 gumbelcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
 gumbelcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<gumbelcdf: invalid value for alpha.> [p, plo, pup] = ...
 gumbelcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (i, 2, 2)
***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, i, 2)
***** error<gumbelcdf: X, MU, and BETA must not be complex.> gumbelcdf (2, 2, i)
***** error<gumbelcdf: bad covariance matrix.> ...
 [p, plo, pup] = gumbelcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvnrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/mvnrnd.m
***** error<mvnrnd: too few input arguments.> mvnrnd ()
***** error<mvnrnd: too few input arguments.> mvnrnd ([2, 3, 4])
***** error<mvnrnd: wrong size of MU.> mvnrnd (ones (2, 2, 2), ones (1, 2, 3, 4))
***** error<mvnrnd: wrong size of SIGMA.> mvnrnd (ones (1, 3), ones (1, 2, 3, 4))
***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2])), [1, 3])
***** assert (size (mvnrnd ([2, 3, 4], [2, 2, 2], 10)), [10, 3])
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/dist_fun/bisacdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/bisacdf.m
***** demo
 ## Plot various CDFs from the Birnbaum-Saunders distribution
 x = 0.01:0.01:4;
 p1 = bisacdf (x, 1, 0.5);
 p2 = bisacdf (x, 1, 1);
 p3 = bisacdf (x, 1, 2);
 p4 = bisacdf (x, 1, 5);
 p5 = bisacdf (x, 1, 10);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"β = 1, γ = 0.5", "β = 1, γ = 1", "β = 1, γ = 2", ...
          "β = 1, γ = 5", "β = 1, γ = 10"}, "location", "southeast")
 title ("Birnbaum-Saunders CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Plot various CDFs from the Birnbaum-Saunders distribution
 x = 0.01:0.01:6;
 p1 = bisacdf (x, 1, 0.3);
 p2 = bisacdf (x, 2, 0.3);
 p3 = bisacdf (x, 1, 0.5);
 p4 = bisacdf (x, 3, 0.5);
 p5 = bisacdf (x, 5, 0.5);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"β = 1, γ = 0.3", "β = 2, γ = 0.3", "β = 1, γ = 0.5", ...
          "β = 3, γ = 0.5", "β = 5, γ = 0.5"}, "location", "southeast")
 title ("Birnbaum-Saunders CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 1/2, 0.76024993890652337, 1];
***** assert (bisacdf (x, ones (1,5), ones (1,5)), y, eps)
***** assert (bisacdf (x, 1, 1), y, eps)
***** assert (bisacdf (x, 1, ones (1,5)), y, eps)
***** assert (bisacdf (x, ones (1,5), 1), y, eps)
***** assert (bisacdf (x, 1, 1), y, eps)
***** assert (bisacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
***** assert (bisacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (bisacdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (bisacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (bisacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** assert (bisacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** error<bisacdf: function called with too few input arguments.> bisacdf ()
***** error<bisacdf: function called with too few input arguments.> bisacdf (1)
***** error<bisacdf: function called with too few input arguments.> bisacdf (1, 2)
***** error<bisacdf: function called with too many inputs> ...
 bisacdf (1, 2, 3, 4, 5)
***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, "tail")
***** error<bisacdf: invalid argument for upper tail.> bisacdf (1, 2, 3, 4)
***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisacdf (ones (3), ones (2), ones(2))
***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisacdf (ones (2), ones (3), ones(2))
***** error<bisacdf: X, BETA, and GAMMA must be of common size or scalars.> ...
 bisacdf (ones (2), ones (2), ones(3))
***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (i, 4, 3)
***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, i, 3)
***** error<bisacdf: X, BETA, and GAMMA must not be complex.> bisacdf (1, 4, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/evrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/evrnd.m
***** assert (size (evrnd (1, 1)), [1 1])
***** assert (size (evrnd (1, ones (2,1))), [2, 1])
***** assert (size (evrnd (1, ones (2,2))), [2, 2])
***** assert (size (evrnd (ones (2,1), 1)), [2, 1])
***** assert (size (evrnd (ones (2,2), 1)), [2, 2])
***** assert (size (evrnd (1, 1, 3)), [3, 3])
***** assert (size (evrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (evrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (evrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (evrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (evrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (evrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (evrnd (1, 1, [])), [0, 0])
***** assert (size (evrnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (evrnd (1, 1)), "double")
***** assert (class (evrnd (1, single (1))), "single")
***** assert (class (evrnd (1, single ([1, 1]))), "single")
***** assert (class (evrnd (single (1), 1)), "single")
***** assert (class (evrnd (single ([1, 1]), 1)), "single")
***** error<evrnd: function called with too few input arguments.> evrnd ()
***** error<evrnd: function called with too few input arguments.> evrnd (1)
***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
 evrnd (ones (3), ones (2))
***** error<evrnd: MU and SIGMA must be of common size or scalars.> ...
 evrnd (ones (2), ones (3))
***** error<evrnd: MU and SIGMA must not be complex.> evrnd (i, 2, 3)
***** error<evrnd: MU and SIGMA must not be complex.> evrnd (1, i, 3)
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, -1)
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, 1.2)
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, ones (2))
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, [2 -1 2])
***** error<evrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 evrnd (1, 2, [2 0 2.5])
***** error<evrnd: dimensions must be non-negative integers.> ...
 evrnd (1, 2, 2, -1, 5)
***** error<evrnd: dimensions must be non-negative integers.> ...
 evrnd (1, 2, 2, 1.5, 5)
***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
 evrnd (2, ones (2), 3)
***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
 evrnd (2, ones (2), [3, 2])
***** error<evrnd: MU and SIGMA must be scalars or of size SZ.> ...
 evrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncfrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/ncfrnd.m
***** assert (size (ncfrnd (1, 1, 1)), [1, 1])
***** assert (size (ncfrnd (1, ones (2, 1), 1)), [2, 1])
***** assert (size (ncfrnd (1, ones (2, 2), 1)), [2, 2])
***** assert (size (ncfrnd (ones (2, 1), 1, 1)), [2, 1])
***** assert (size (ncfrnd (ones (2, 2), 1, 1)), [2, 2])
***** assert (size (ncfrnd (1, 1, 1, 3)), [3, 3])
***** assert (size (ncfrnd (1, 1, 1, [4, 1])), [4, 1])
***** assert (size (ncfrnd (1, 1, 1, 4, 1)), [4, 1])
***** assert (size (ncfrnd (1, 1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (ncfrnd (1, 1, 1, 0, 1)), [0, 1])
***** assert (size (ncfrnd (1, 1, 1, 1, 0)), [1, 0])
***** assert (size (ncfrnd (1, 1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (ncfrnd (1, 1, 1, [])), [0, 0])
***** assert (size (ncfrnd (1, 1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (ncfrnd (1, 1, 1)), "double")
***** assert (class (ncfrnd (1, single (1), 1)), "single")
***** assert (class (ncfrnd (1, 1, single (1))), "single")
***** assert (class (ncfrnd (1, single ([1, 1]), 1)), "single")
***** assert (class (ncfrnd (1, 1, single ([1, 1]))), "single")
***** assert (class (ncfrnd (single (1), 1, 1)), "single")
***** assert (class (ncfrnd (single ([1, 1]), 1, 1)), "single")
***** error<ncfrnd: function called with too few input arguments.> ncfrnd ()
***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1)
***** error<ncfrnd: function called with too few input arguments.> ncfrnd (1, 2)
***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfrnd (ones (3), ones (2), ones (2))
***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfrnd (ones (2), ones (3), ones (2))
***** error<ncfrnd: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfrnd (ones (2), ones (2), ones (3))
***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (i, 2, 3)
***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, i, 3)
***** error<ncfrnd: DF1, DF2, and LAMBDA must not be complex.> ncfrnd (1, 2, i)
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, -1)
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, 1.2)
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, ones (2))
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, [2 -1 2])
***** error<ncfrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ncfrnd (1, 2, 3, [2 0 2.5])
***** error<ncfrnd: dimensions must be non-negative integers.> ...
 ncfrnd (1, 2, 3, 2, -1, 5)
***** error<ncfrnd: dimensions must be non-negative integers.> ...
 ncfrnd (1, 2, 3, 2, 1.5, 5)
***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
 ncfrnd (2, ones (2), 2, 3)
***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
 ncfrnd (2, ones (2), 2, [3, 2])
***** error<ncfrnd: DF1, DF2, and LAMBDA must be scalars or of size SZ.> ...
 ncfrnd (2, ones (2), 2, 3, 2)
40 tests, 40 passed, 0 known failure, 0 skipped
[inst/dist_fun/ricernd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/ricernd.m
***** assert (size (ricernd (2, 1/2)), [1, 1])
***** assert (size (ricernd (2 * ones (2, 1), 1/2)), [2, 1])
***** assert (size (ricernd (2 * ones (2, 2), 1/2)), [2, 2])
***** assert (size (ricernd (2, 1/2 * ones (2, 1))), [2, 1])
***** assert (size (ricernd (1, 1/2 * ones (2, 2))), [2, 2])
***** assert (size (ricernd (ones (2, 1), 1)), [2, 1])
***** assert (size (ricernd (ones (2, 2), 1)), [2, 2])
***** assert (size (ricernd (2, 1/2, 3)), [3, 3])
***** assert (size (ricernd (1, 1, [4, 1])), [4, 1])
***** assert (size (ricernd (1, 1, 4, 1)), [4, 1])
***** assert (size (ricernd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (ricernd (1, 1, 0, 1)), [0, 1])
***** assert (size (ricernd (1, 1, 1, 0)), [1, 0])
***** assert (size (ricernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (ricernd (1, 1, [])), [0, 0])
***** assert (size (ricernd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (ricernd (1, 1)), "double")
***** assert (class (ricernd (1, single (0))), "single")
***** assert (class (ricernd (1, single ([0, 0]))), "single")
***** assert (class (ricernd (1, single (1), 2)), "single")
***** assert (class (ricernd (1, single ([1, 1]), 1, 2)), "single")
***** assert (class (ricernd (single (1), 1, 2)), "single")
***** assert (class (ricernd (single ([1, 1]), 1, 1, 2)), "single")
***** error<ricernd: function called with too few input arguments.> ricernd ()
***** error<ricernd: function called with too few input arguments.> ricernd (1)
***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
 ricernd (ones (3), ones (2))
***** error<ricernd: S and SIGMA must be of common size or scalars.> ...
 ricernd (ones (2), ones (3))
***** error<ricernd: S and SIGMA must not be complex.> ricernd (i, 2)
***** error<ricernd: S and SIGMA must not be complex.> ricernd (1, i)
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, -1)
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, 1.2)
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, ones (2))
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, [2 -1 2])
***** error<ricernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 ricernd (1, 1/2, [2 0 2.5])
***** error<ricernd: dimensions must be non-negative integers.> ...
 ricernd (1, 1/2, 2, -1, 5)
***** error<ricernd: dimensions must be non-negative integers.> ...
 ricernd (1, 1/2, 2, 1.5, 5)
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2, 1/2 * ones (2), 3)
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2, 1/2 * ones (2), [3, 2])
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2, 1/2 * ones (2), 3, 2)
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2 * ones (2), 1/2, 3)
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2 * ones (2), 1/2, [3, 2])
***** error<ricernd: S and SIGMA must be scalars or of size SZ.> ...
 ricernd (2 * ones (2), 1/2, 3, 2)
42 tests, 42 passed, 0 known failure, 0 skipped
[inst/dist_fun/tlscdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/tlscdf.m
***** demo
 ## Plot various CDFs from the location-scale Student's T distribution
 x = -8:0.01:8;
 p1 = tlscdf (x, 0, 1, 1);
 p2 = tlscdf (x, 0, 2, 2);
 p3 = tlscdf (x, 3, 2, 5);
 p4 = tlscdf (x, -1, 3, Inf);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
 grid on
 xlim ([-8, 8])
 ylim ([0, 1])
 legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
          "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
         "location", "northwest")
 title ("Location-scale Student's T CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x,y
 x = [-Inf 0 1 Inf];
 y = [0 1/2 3/4 1];
***** assert (tlscdf (x, 0, 1, ones (1,4)), y, eps)
***** assert (tlscdf (x, 0, 1, 1), y, eps)
***** assert (tlscdf (x, 0, 1, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
***** assert (tlscdf ([x(1:2) NaN x(4)], 0, 1, 1), [y(1:2) NaN y(4)], eps)
***** assert (tlscdf (2, 0, 1, 3, "upper"), 0.0697, 1e-4)
***** assert (tlscdf (205, 0, 1, 5, "upper"), 2.6206e-11, 1e-14)
***** assert (tlscdf ([x, NaN], 0, 1, 1), [y, NaN], eps)
***** assert (tlscdf (single ([x, NaN]), 0, 1, 1), single ([y, NaN]), eps ("single"))
***** assert (tlscdf ([x, NaN], single (0), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (tlscdf ([x, NaN], 0, single (1), 1), single ([y, NaN]), eps ("single"))
***** assert (tlscdf ([x, NaN], 0, 1, single (1)), single ([y, NaN]), eps ("single"))
***** error<tlscdf: function called with too few input arguments.> tlscdf ()
***** error<tlscdf: function called with too few input arguments.> tlscdf (1)
***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2)
***** error<tlscdf: function called with too few input arguments.> tlscdf (1, 2, 3)
***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, "uper")
***** error<tlscdf: invalid argument for upper tail.> tlscdf (1, 2, 3, 4, 5)
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), ones (2), 1, 1)
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), 1, ones (2), 1)
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), 1, 1, ones (2))
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), ones (2), 1, 1, "upper")
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), 1, ones (2), 1, "upper")
***** error<tlscdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlscdf (ones (3), 1, 1, ones (2), "upper")
***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (i, 2, 1, 1)
***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, i, 1, 1)
***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, i, 1)
***** error<tlscdf: X, MU, SIGMA, and NU must not be complex.> tlscdf (2, 1, 1, i)
27 tests, 27 passed, 0 known failure, 0 skipped
[inst/dist_fun/betarnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/betarnd.m
***** assert (size (betarnd (2, 1/2)), [1 1])
***** assert (size (betarnd (2 * ones (2, 1), 1/2)), [2, 1])
***** assert (size (betarnd (2 * ones (2, 2), 1/2)), [2, 2])
***** assert (size (betarnd (2, 1/2 * ones (2, 1))), [2, 1])
***** assert (size (betarnd (1, 1/2 * ones (2, 2))), [2, 2])
***** assert (size (betarnd (ones (2, 1), 1)), [2, 1])
***** assert (size (betarnd (ones (2, 2), 1)), [2, 2])
***** assert (size (betarnd (2, 1/2, 3)), [3, 3])
***** assert (size (betarnd (1, 1, [4, 1])), [4, 1])
***** assert (size (betarnd (1, 1, 4, 1)), [4, 1])
***** assert (size (betarnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (betarnd (1, 1, 0, 1)), [0, 1])
***** assert (size (betarnd (1, 1, 1, 0)), [1, 0])
***** assert (size (betarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (betarnd (1, 1, [])), [0, 0])
***** assert (size (betarnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (betarnd (1, 1)), "double")
***** assert (class (betarnd (1, single (0))), "single")
***** assert (class (betarnd (1, single ([0, 0]))), "single")
***** assert (class (betarnd (1, single (1), 2)), "single")
***** assert (class (betarnd (1, single ([1, 1]), 1, 2)), "single")
***** assert (class (betarnd (single (1), 1, 2)), "single")
***** assert (class (betarnd (single ([1, 1]), 1, 1, 2)), "single")
***** error<betarnd: function called with too few input arguments.> betarnd ()
***** error<betarnd: function called with too few input arguments.> betarnd (1)
***** error<betarnd: A and B must be of common size or scalars.> ...
 betarnd (ones (3), ones (2))
***** error<betarnd: A and B must be of common size or scalars.> ...
 betarnd (ones (2), ones (3))
***** error<betarnd: A and B must not be complex.> betarnd (i, 2)
***** error<betarnd: A and B must not be complex.> betarnd (1, i)
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, -1)
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, 1.2)
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, ones (2))
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, [2 -1 2])
***** error<betarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 betarnd (1, 1/2, [2 0 2.5])
***** error<betarnd: dimensions must be non-negative integers.> ...
 betarnd (1, 1/2, 2, -1, 5)
***** error<betarnd: dimensions must be non-negative integers.> ...
 betarnd (1, 1/2, 2, 1.5, 5)
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2, 1/2 * ones (2), 3)
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2, 1/2 * ones (2), [3, 2])
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2, 1/2 * ones (2), 3, 2)
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2 * ones (2), 1/2, 3)
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2 * ones (2), 1/2, [3, 2])
***** error<betarnd: A and B must be scalars or of size SZ.> ...
 betarnd (2 * ones (2), 1/2, 3, 2)
42 tests, 42 passed, 0 known failure, 0 skipped
[inst/dist_fun/burrpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/burrpdf.m
***** demo
 ## Plot various PDFs from the Burr type XII distribution
 x = 0.001:0.001:3;
 y1 = burrpdf (x, 1, 1, 1);
 y2 = burrpdf (x, 1, 1, 2);
 y3 = burrpdf (x, 1, 1, 3);
 y4 = burrpdf (x, 1, 2, 1);
 y5 = burrpdf (x, 1, 3, 1);
 y6 = burrpdf (x, 1, 0.5, 2);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
       x, y4, "-c", x, y5, "-m", x, y6, "-k")
 grid on
 ylim ([0, 2])
 legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
          "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
          "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
         "location", "northeast")
 title ("Burr type XII PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 1, 1/4, 1/9, 0];
***** assert (burrpdf (x, ones(1,5), ones (1,5), ones (1,5)), y)
***** assert (burrpdf (x, 1, 1, 1), y)
***** assert (burrpdf (x, [1, 1, NaN, 1, 1], 1, 1), [y(1:2), NaN, y(4:5)])
***** assert (burrpdf (x, 1, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
***** assert (burrpdf (x, 1, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
***** assert (burrpdf ([x, NaN], 1, 1, 1), [y, NaN])
***** assert (burrpdf (single ([x, NaN]), 1, 1, 1), single ([y, NaN]))
***** assert (burrpdf ([x, NaN], single (1), 1, 1), single ([y, NaN]))
***** assert (burrpdf ([x, NaN], 1, single (1), 1), single ([y, NaN]))
***** assert (burrpdf ([x, NaN], 1, 1, single (1)), single ([y, NaN]))
***** error<burrpdf: function called with too few input arguments.> burrpdf ()
***** error<burrpdf: function called with too few input arguments.> burrpdf (1)
***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2)
***** error<burrpdf: function called with too few input arguments.> burrpdf (1, 2, 3)
***** error<burrpdf: function called with too many inputs> ...
 burrpdf (1, 2, 3, 4, 5)
***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrpdf (ones (3), ones (2), ones(2), ones(2))
***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrpdf (ones (2), ones (3), ones(2), ones(2))
***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrpdf (ones (2), ones (2), ones(3), ones(2))
***** error<burrpdf: X, LAMBDA, C, and K must be of common size or scalars.> ...
 burrpdf (ones (2), ones (2), ones(2), ones(3))
***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (i, 2, 3, 4)
***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, i, 3, 4)
***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, i, 4)
***** error<burrpdf: X, LAMBDA, C, and K must not be complex.> burrpdf (1, 2, 3, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/invgpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/invgpdf.m
***** demo
 ## Plot various PDFs from the inverse Gaussian distribution
 x = 0:0.001:3;
 y1 = invgpdf (x, 1, 0.2);
 y2 = invgpdf (x, 1, 1);
 y3 = invgpdf (x, 1, 3);
 y4 = invgpdf (x, 3, 0.2);
 y5 = invgpdf (x, 3, 1);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-y")
 grid on
 xlim ([0, 3])
 ylim ([0, 3])
 legend ({"μ = 1, σ = 0.2", "μ = 1, σ = 1", "μ = 1, σ = 3", ...
          "μ = 3, σ = 0.2", "μ = 3, σ = 1"}, "location", "northeast")
 title ("Inverse Gaussian PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y = [0, 0, 0, 0.8788, 0.3989, 0];
***** assert (invgpdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
***** assert (invgpdf (x, 1, [-2, -1, 0, 1, 1, 1]), [nan(1,3), y([4:6])], 1e-4)
***** assert (class (hncdf (single ([x, NaN]), 1, 1)), "single")
***** assert (class (hncdf ([x, NaN], 1, single (1))), "single")
***** assert (class (hncdf ([x, NaN], single (1), 1)), "single")
***** error<invgpdf: function called with too few input arguments.> invgpdf ()
***** error<invgpdf: function called with too few input arguments.> invgpdf (1)
***** error<invgpdf: function called with too few input arguments.> invgpdf (1, 2)
***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgpdf (1, ones (2), ones (3))
***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgpdf (ones (2), 1, ones (3))
***** error<invgpdf: X, MU, and LAMBDA must be of common size or scalars.> ...
 invgpdf (ones (2), ones (3), 1)
***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (i, 2, 3)
***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, i, 3)
***** error<invgpdf: X, MU, and LAMBDA must not be complex.> invgpdf (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/chi2cdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/chi2cdf.m
***** demo
 ## Plot various CDFs from the chi-squared distribution
 x = 0:0.01:8;
 p1 = chi2cdf (x, 1);
 p2 = chi2cdf (x, 2);
 p3 = chi2cdf (x, 3);
 p4 = chi2cdf (x, 4);
 p5 = chi2cdf (x, 6);
 p6 = chi2cdf (x, 9);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
       x, p4, "-c", x, p5, "-m", x, p6, "-y")
 grid on
 xlim ([0, 8])
 legend ({"df = 1", "df = 2", "df = 3", ...
          "df = 4", "df = 6", "df = 9"}, "location", "southeast")
 title ("Chi-squared CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p, u
 x = [-1, 0, 0.5, 1, 2];
 p = [0, (1 - exp (-x(2:end) / 2))];
 u = [1, 0, NaN, 0.606530659712633, 0.367879441171442];
***** assert (chi2cdf (x, 2 * ones (1,5)), p, eps)
***** assert (chi2cdf (x, 2), p, eps)
***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1]), [0, 1, NaN, p(4:5)], eps)
***** assert (chi2cdf (x, 2 * [1, 0, NaN, 1, 1], "upper"), u, 3 * eps)
***** assert (chi2cdf ([x(1:2), NaN, x(4:5)], 2), [p(1:2), NaN, p(4:5)], eps)
***** assert (chi2cdf ([x, NaN], 2), [p, NaN], eps)
***** assert (chi2cdf (single ([x, NaN]), 2), single ([p, NaN]), eps ("single"))
***** assert (chi2cdf ([x, NaN], single (2)), single ([p, NaN]), eps ("single"))
***** error<chi2cdf: function called with too few input arguments.> chi2cdf ()
***** error<chi2cdf: function called with too few input arguments.> chi2cdf (1)
***** error<chi2cdf: function called with too many inputs> chi2cdf (1, 2, 3, 4)
***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, 3)
***** error<chi2cdf: invalid argument for upper tail.> chi2cdf (1, 2, "uper")
***** error<chi2cdf: X and DF must be of common size or scalars.> ...
 chi2cdf (ones (3), ones (2))
***** error<chi2cdf: X and DF must be of common size or scalars.> ...
 chi2cdf (ones (2), ones (3))
***** error<chi2cdf: X and DF must not be complex.> chi2cdf (i, 2)
***** error<chi2cdf: X and DF must not be complex.> chi2cdf (2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/hncdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/hncdf.m
***** demo
 ## Plot various CDFs from the half-normal distribution
 x = 0:0.001:10;
 p1 = hncdf (x, 0, 1);
 p2 = hncdf (x, 0, 2);
 p3 = hncdf (x, 0, 3);
 p4 = hncdf (x, 0, 5);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([0, 10])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 2", ...
          "μ = 0, σ = 3", "μ = 0, σ = 5"}, "location", "southeast")
 title ("Half-normal CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Plot half-normal against normal cumulative distribution function
 x = -5:0.001:5;
 p1 = hncdf (x, 0, 1);
 p2 = normcdf (x);
 plot (x, p1, "-b", x, p2, "-g")
 grid on
 xlim ([-5, 5])
 legend ({"half-normal with μ = 0, σ = 1", ...
          "standart normal (μ = 0, σ = 1)"}, "location", "southeast")
 title ("Half-normal against standard normal CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p1, p1u, y2, y2u, y3, y3u
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 p1 = [0, 0, 0, 0.3829, 0.6827, 1];
 p1u = [1, 1, 1, 0.6171, 0.3173, 0];
***** assert (hncdf (x, zeros (1,6), ones (1,6)), p1, 1e-4)
***** assert (hncdf (x, 0, 1), p1, 1e-4)
***** assert (hncdf (x, 0, ones (1,6)), p1, 1e-4)
***** assert (hncdf (x, zeros (1,6), 1), p1, 1e-4)
***** assert (hncdf (x, 0, [1, 1, 1, NaN, 1, 1]), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (hncdf (x, [0, 0, 0, NaN, 0, 0], 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (hncdf ([x(1:3), NaN, x(5:6)], 0, 1), [p1(1:3), NaN, p1(5:6)], 1e-4)
***** assert (hncdf (x, zeros (1,6), ones (1,6), "upper"), p1u, 1e-4)
***** assert (hncdf (x, 0, 1, "upper"), p1u, 1e-4)
***** assert (hncdf (x, 0, ones (1,6), "upper"), p1u, 1e-4)
***** assert (hncdf (x, zeros (1,6), 1, "upper"), p1u, 1e-4)
***** assert (class (hncdf (single ([x, NaN]), 0, 1)), "single")
***** assert (class (hncdf ([x, NaN], 0, single (1))), "single")
***** assert (class (hncdf ([x, NaN], single (0), 1)), "single")
***** error<hncdf: function called with too few input arguments.> hncdf ()
***** error<hncdf: function called with too few input arguments.> hncdf (1)
***** error<hncdf: function called with too few input arguments.> hncdf (1, 2)
***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, "tail")
***** error<hncdf: invalid argument for upper tail.> hncdf (1, 2, 3, 5)
***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hncdf (ones (3), ones (2), ones(2))
***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hncdf (ones (2), ones (3), ones(2))
***** error<hncdf: X, MU, and SIGMA must be of common size or scalars.> ...
 hncdf (ones (2), ones (2), ones(3))
***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (i, 2, 3)
***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, i, 3)
***** error<hncdf: X, MU, and SIGMA must not be complex.> hncdf (1, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/trirnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/trirnd.m
***** assert (size (trirnd (1, 1.5, 2)), [1, 1])
***** assert (size (trirnd (1 * ones (2, 1), 1.5, 2)), [2, 1])
***** assert (size (trirnd (1 * ones (2, 2), 1.5, 2)), [2, 2])
***** assert (size (trirnd (1, 1.5 * ones (2, 1), 2)), [2, 1])
***** assert (size (trirnd (1, 1.5 * ones (2, 2), 2)), [2, 2])
***** assert (size (trirnd (1, 1.5, 2 * ones (2, 1))), [2, 1])
***** assert (size (trirnd (1, 1.5, 2 * ones (2, 2))), [2, 2])
***** assert (size (trirnd (1, 1.5, 2, 3)), [3, 3])
***** assert (size (trirnd (1, 1.5, 2, [4, 1])), [4, 1])
***** assert (size (trirnd (1, 1.5, 2, 4, 1)), [4, 1])
***** assert (size (trirnd (1, 1.5, 2, [])), [0, 0])
***** assert (size (trirnd (1, 1.5, 2, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (trirnd (1, 1.5, 2)), "double")
***** assert (class (trirnd (single (1), 1.5, 2)), "single")
***** assert (class (trirnd (single ([1, 1]), 1.5, 2)), "single")
***** assert (class (trirnd (1, single (1.5), 2)), "single")
***** assert (class (trirnd (1, single ([1.5, 1.5]), 2)), "single")
***** assert (class (trirnd (1, 1.5, single (1.5))), "single")
***** assert (class (trirnd (1, 1.5, single ([2, 2]))), "single")
***** error<trirnd: function called with too few input arguments.> trirnd ()
***** error<trirnd: function called with too few input arguments.> trirnd (1)
***** error<trirnd: function called with too few input arguments.> trirnd (1, 2)
***** error<trirnd: A, B, and C must be of common size or scalars.> ...
 trirnd (ones (3), 5 * ones (2), ones (2))
***** error<trirnd: A, B, and C must be of common size or scalars.> ...
 trirnd (ones (2), 5 * ones (3), ones (2))
***** error<trirnd: A, B, and C must be of common size or scalars.> ...
 trirnd (ones (2), 5 * ones (2), ones (3))
***** error<trirnd: A, B, and C must not be complex.> trirnd (i, 5, 3)
***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5+i, 3)
***** error<trirnd: A, B, and C must not be complex.> trirnd (1, 5, i)
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, -1)
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, 1.2)
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, ones (2))
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, [2 -1 2])
***** error<trirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trirnd (1, 5, 3, [2 0 2.5])
***** error<trirnd: dimensions must be non-negative integers.> ...
 trirnd (1, 5, 3, 2, -1, 5)
***** error<trirnd: dimensions must be non-negative integers.> ...
 trirnd (1, 5, 3, 2, 1.5, 5)
***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
 trirnd (2, 5 * ones (2), 2, 3)
***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
 trirnd (2, 5 * ones (2), 2, [3, 2])
***** error<trirnd: A, B, and C must be scalar or of size SZ.> ...
 trirnd (2, 5 * ones (2), 2, 3, 2)
38 tests, 38 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvnpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/mvnpdf.m
***** demo
 mu = [1, -1];
 sigma = [0.9, 0.4; 0.4, 0.3];
 [X1, X2] = meshgrid (linspace (-1, 3, 25)', linspace (-3, 1, 25)');
 x = [X1(:), X2(:)];
 p = mvnpdf (x, mu, sigma);
 surf (X1, X2, reshape (p, 25, 25));
***** error<mvnpdf: too few input arguments.> y = mvnpdf ();
***** error<mvnpdf: too few dimensions in X.> y = mvnpdf ([]);
***** error<mvnpdf: wrong dimensions in X.> y = mvnpdf (ones (3,3,3));
***** error<mvnpdf: columns in X and MU mismatch.> ...
 y = mvnpdf (ones (10, 2), [4, 2, 3]);
***** error<mvnpdf: rows in X and MU mismatch.> ...
 y = mvnpdf (ones (10, 2), [4, 2; 3, 2]);
***** error<mvnpdf: wrong size of MU.> ...
 y = mvnpdf (ones (10, 2), ones (3, 3, 3));
***** shared x, mu, sigma
 x = [1, 2, 5, 4, 6];
 mu = [2, 0, -1, 1, 4];
 sigma = [2, 2, 2, 2, 2];
***** assert (mvnpdf (x), 1.579343404440977e-20, 1e-30);
***** assert (mvnpdf (x, mu), 1.899325144348102e-14, 1e-25);
***** assert (mvnpdf (x, mu, sigma), 2.449062307156273e-09, 1e-20);
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fun/gumbelinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gumbelinv.m
***** demo
 ## Plot various iCDFs from the Gumbel distribution
 p = 0.001:0.001:0.999;
 x1 = gumbelinv (p, 0.5, 2);
 x2 = gumbelinv (p, 1.0, 2);
 x3 = gumbelinv (p, 1.5, 3);
 x4 = gumbelinv (p, 3.0, 4);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-5, 20])
 legend ({"μ = 0.5, β = 2", "μ = 1.0, β = 2", ...
          "μ = 1.5, β = 3", "μ = 3.0, β = 4"}, "location", "northwest")
 title ("Gumbel iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, x
 p = [0, 0.05, 0.5 0.95];
 x = [-Inf, -1.0972, 0.3665, 2.9702];
***** assert (gumbelinv (p), x, 1e-4)
***** assert (gumbelinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
***** assert (gumbelinv (p, 0, ones (1,4)), x, 1e-4)
***** assert (gumbelinv (p, zeros (1,4), 1), x, 1e-4)
***** assert (gumbelinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
***** assert (gumbelinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
***** assert (gumbelinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
***** assert (gumbelinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
***** assert (gumbelinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
***** assert (gumbelinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
***** assert (gumbelinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
 p = [0.05, 0.5, 0.95];
 x = gumbelinv(p);
***** assert (gumbelcdf(x), p, 1e-4)
***** error<gumbelinv: invalid number of input arguments.> gumbelinv ()
***** error gumbelinv (1,2,3,4,5,6)
***** error<gumbelinv: P, MU, and BETA must be of common size or scalars.> ...
 gumbelinv (ones (3), ones (2), ones (2))
***** error<gumbelinv: invalid size of covariance matrix.> ...
 [p, plo, pup] = gumbelinv (2, 3, 4, [1, 2])
***** error<gumbelinv: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = gumbelinv (1, 2, 3)
***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
 gumbelinv (1, 2, 3, [1, 0; 0, 1], 0)
***** error<gumbelinv: invalid value for alpha.> [p, plo, pup] = ...
 gumbelinv (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (i, 2, 2)
***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, i, 2)
***** error<gumbelinv: P, MU, and BETA must not be complex.> gumbelinv (2, 2, i)
***** error<gumbelinv: bad covariance matrix.> ...
 [p, plo, pup] = gumbelinv (1, 2, 3, [-1, 10; -Inf, -Inf], 0.04)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/laplacernd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/laplacernd.m
***** assert (size (laplacernd (1, 1)), [1, 1])
***** assert (size (laplacernd (1, ones (2, 1))), [2, 1])
***** assert (size (laplacernd (1, ones (2, 2))), [2, 2])
***** assert (size (laplacernd (ones (2, 1), 1)), [2, 1])
***** assert (size (laplacernd (ones (2, 2), 1)), [2, 2])
***** assert (size (laplacernd (1, 1, 3)), [3, 3])
***** assert (size (laplacernd (1, 1, [4, 1])), [4, 1])
***** assert (size (laplacernd (1, 1, 4, 1)), [4, 1])
***** assert (size (laplacernd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (laplacernd (1, 1, 0, 1)), [0, 1])
***** assert (size (laplacernd (1, 1, 1, 0)), [1, 0])
***** assert (size (laplacernd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (laplacernd (1, 1, [])), [0, 0])
***** assert (size (laplacernd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (laplacernd (1, 1)), "double")
***** assert (class (laplacernd (1, single (1))), "single")
***** assert (class (laplacernd (1, single ([1, 1]))), "single")
***** assert (class (laplacernd (single (1), 1)), "single")
***** assert (class (laplacernd (single ([1, 1]), 1)), "single")
***** error<laplacernd: function called with too few input arguments.> laplacernd ()
***** error<laplacernd: function called with too few input arguments.> laplacernd (1)
***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
 laplacernd (ones (3), ones (2))
***** error<laplacernd: MU and BETA must be of common size or scalars.> ...
 laplacernd (ones (2), ones (3))
***** error<laplacernd: MU and BETA must not be complex.> laplacernd (i, 2, 3)
***** error<laplacernd: MU and BETA must not be complex.> laplacernd (1, i, 3)
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, -1)
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, 1.2)
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, ones (2))
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, [2 -1 2])
***** error<laplacernd: SZ must be a scalar or a row vector of non-negative integers.> ...
 laplacernd (1, 2, [2 0 2.5])
***** error<laplacernd: dimensions must be non-negative integers.> ...
 laplacernd (1, 2, 2, -1, 5)
***** error<laplacernd: dimensions must be non-negative integers.> ...
 laplacernd (1, 2, 2, 1.5, 5)
***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
 laplacernd (2, ones (2), 3)
***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
 laplacernd (2, ones (2), [3, 2])
***** error<laplacernd: MU and BETA must be scalars or of size SZ.> ...
 laplacernd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/tlsinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/tlsinv.m
***** demo
 ## Plot various iCDFs from the location-scale Student's T distribution
 p = 0.001:0.001:0.999;
 x1 = tlsinv (p, 0, 1, 1);
 x2 = tlsinv (p, 0, 2, 2);
 x3 = tlsinv (p, 3, 2, 5);
 x4 = tlsinv (p, -1, 3, Inf);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
 grid on
 xlim ([0, 1])
 ylim ([-8, 8])
 legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
          "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
         "location", "southeast")
 title ("Location-scale Student's T iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (tlsinv (p, 0, 1, ones (1,5)), [NaN -Inf 0 Inf NaN])
***** assert (tlsinv (p, 0, 1, 1), [NaN -Inf 0 Inf NaN], eps)
***** assert (tlsinv (p, 0, 1, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
***** assert (tlsinv ([p(1:2) NaN p(4:5)], 0, 1, 1), [NaN -Inf NaN Inf NaN])
***** assert (class (tlsinv ([p, NaN], 0, 1, 1)), "double")
***** assert (class (tlsinv (single ([p, NaN]), 0, 1, 1)), "single")
***** assert (class (tlsinv ([p, NaN], single (0), 1, 1)), "single")
***** assert (class (tlsinv ([p, NaN], 0, single (1), 1)), "single")
***** assert (class (tlsinv ([p, NaN], 0, 1, single (1))), "single")
***** error<tlsinv: function called with too few input arguments.> tlsinv ()
***** error<tlsinv: function called with too few input arguments.> tlsinv (1)
***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2)
***** error<tlsinv: function called with too few input arguments.> tlsinv (1, 2, 3)
***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsinv (ones (3), ones (2), 1, 1)
***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsinv (ones (2), 1, ones (3), 1)
***** error<tlsinv: P, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsinv (ones (2), 1, 1, ones (3))
***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (i, 2, 3, 4)
***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, i, 3, 4)
***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, i, 4)
***** error<tlsinv: P, MU, SIGMA, and NU must not be complex.> tlsinv (2, 2, 3, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/nbinpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/nbinpdf.m
***** demo
 ## Plot various PDFs from the negative binomial distribution
 x = 0:40;
 y1 = nbinpdf (x, 2, 0.15);
 y2 = nbinpdf (x, 5, 0.2);
 y3 = nbinpdf (x, 4, 0.4);
 y4 = nbinpdf (x, 10, 0.3);
 plot (x, y1, "*r", x, y2, "*g", x, y3, "*k", x, y4, "*m")
 grid on
 xlim ([0, 40])
 ylim ([0, 0.12])
 legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
          "r = 10, ps = 0.3"}, "location", "northeast")
 title ("Negative binomial PDF")
 xlabel ("values in x (number of failures)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 Inf];
 y = [0 1/2 1/4 1/8 NaN];
***** assert (nbinpdf (x, ones (1,5), 0.5*ones (1,5)), y)
***** assert (nbinpdf (x, 1, 0.5*ones (1,5)), y)
***** assert (nbinpdf (x, ones (1,5), 0.5), y)
***** assert (nbinpdf (x, [0 1 NaN 1.5 Inf], 0.5), [NaN 1/2 NaN 1.875*0.5^1.5/4 NaN], eps)
***** assert (nbinpdf (x, 1, 0.5*[-1 NaN 4 1 1]), [NaN NaN NaN y(4:5)])
***** assert (nbinpdf ([x, NaN], 1, 0.5), [y, NaN])
***** assert (nbinpdf (single ([x, NaN]), 1, 0.5), single ([y, NaN]))
***** assert (nbinpdf ([x, NaN], single (1), 0.5), single ([y, NaN]))
***** assert (nbinpdf ([x, NaN], 1, single (0.5)), single ([y, NaN]))
***** error<nbinpdf: function called with too few input arguments.> nbinpdf ()
***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1)
***** error<nbinpdf: function called with too few input arguments.> nbinpdf (1, 2)
***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
 nbinpdf (ones (3), ones (2), ones (2))
***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
 nbinpdf (ones (2), ones (3), ones (2))
***** error<nbinpdf: X, R, and PS must be of common size or scalars.> ...
 nbinpdf (ones (2), ones (2), ones (3))
***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (i, 2, 2)
***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, i, 2)
***** error<nbinpdf: X, R, and PS must not be complex.> nbinpdf (2, 2, i)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/tlspdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/tlspdf.m
***** demo
 ## Plot various PDFs from the Student's T distribution
 x = -8:0.01:8;
 y1 = tlspdf (x, 0, 1, 1);
 y2 = tlspdf (x, 0, 2, 2);
 y3 = tlspdf (x, 3, 2, 5);
 y4 = tlspdf (x, -1, 3, Inf);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
 grid on
 xlim ([-8, 8])
 ylim ([0, 0.41])
 legend ({"mu = 0, sigma = 1, nu = 1", "mu = 0, sigma = 2, nu = 2", ...
          "mu = 3, sigma = 2, nu = 5", 'mu = -1, sigma = 3, nu = \infty'}, ...
         "location", "northwest")
 title ("Location-scale Student's T PDF")
 xlabel ("values in x")
 ylabel ("density")
***** test
 x = rand (10,1);
 y = 1./(pi * (1 + x.^2));
 assert (tlspdf (x, 0, 1, 1), y, 5*eps);
 assert (tlspdf (x+5, 5, 1, 1), y, 5*eps);
 assert (tlspdf (x.*2, 0, 2, 1), y./2, 5*eps);
***** shared x, y
 x = [-Inf 0 0.5 1 Inf];
 y = 1./(pi * (1 + x.^2));
***** assert (tlspdf (x, 0, 1, ones (1,5)), y, eps)
***** assert (tlspdf (x, 0, 1, 1), y, eps)
***** assert (tlspdf (x, 0, 1, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
***** assert (tlspdf (x, 0, 1, Inf), normpdf (x))
***** assert (class (tlspdf ([x, NaN], 1, 1, 1)), "double")
***** assert (class (tlspdf (single ([x, NaN]), 1, 1, 1)), "single")
***** assert (class (tlspdf ([x, NaN], single (1), 1, 1)), "single")
***** assert (class (tlspdf ([x, NaN], 1, single (1), 1)), "single")
***** assert (class (tlspdf ([x, NaN], 1, 1, single (1))), "single")
***** error<tlspdf: function called with too few input arguments.> tlspdf ()
***** error<tlspdf: function called with too few input arguments.> tlspdf (1)
***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2)
***** error<tlspdf: function called with too few input arguments.> tlspdf (1, 2, 3)
***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlspdf (ones (3), ones (2), 1, 1)
***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlspdf (ones (2), 1, ones (3), 1)
***** error<tlspdf: X, MU, SIGMA, and NU must be of common size or scalars.> ...
 tlspdf (ones (2), 1, 1, ones (3))
***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (i, 2, 1, 1)
***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, i, 1, 1)
***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, i, 1)
***** error<tlspdf: X, MU, SIGMA, and NU must not be complex.> tlspdf (2, 1, 1, i)
21 tests, 21 passed, 0 known failure, 0 skipped
[inst/dist_fun/nakacdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/nakacdf.m
***** demo
 ## Plot various CDFs from the Nakagami distribution
 x = 0:0.01:3;
 p1 = nakacdf (x, 0.5, 1);
 p2 = nakacdf (x, 1, 1);
 p3 = nakacdf (x, 1, 2);
 p4 = nakacdf (x, 1, 3);
 p5 = nakacdf (x, 2, 1);
 p6 = nakacdf (x, 2, 2);
 p7 = nakacdf (x, 5, 1);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-y", x, p4, "-m", ...
       x, p5, "-k", x, p6, "-b", x, p7, "-c")
 grid on
 xlim ([0, 3])
 legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
          "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
          "μ = 5, ω = 1"}, "location", "southeast")
 title ("Nakagami CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 0.63212055882855778, 0.98168436111126578, 1];
***** assert (nakacdf (x, ones (1,5), ones (1,5)), y, eps)
***** assert (nakacdf (x, 1, 1), y, eps)
***** assert (nakacdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)])
***** assert (nakacdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)])
***** assert (nakacdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (nakacdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps("single"))
***** assert (nakacdf ([x, NaN], single (1), 1), single ([y, NaN]), eps("single"))
***** assert (nakacdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps("single"))
***** error<nakacdf: function called with too few input arguments.> nakacdf ()
***** error<nakacdf: function called with too few input arguments.> nakacdf (1)
***** error<nakacdf: function called with too few input arguments.> nakacdf (1, 2)
***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, "tail")
***** error<nakacdf: invalid argument for upper tail.> nakacdf (1, 2, 3, 4)
***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakacdf (ones (3), ones (2), ones (2))
***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakacdf (ones (2), ones (3), ones (2))
***** error<nakacdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakacdf (ones (2), ones (2), ones (3))
***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (i, 2, 2)
***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, i, 2)
***** error<nakacdf: X, MU, and OMEGA must not be complex.> nakacdf (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/laplaceinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/laplaceinv.m
***** demo
 ## Plot various iCDFs from the Laplace distribution
 p = 0.001:0.001:0.999;
 x1 = cauchyinv (p, 0, 1);
 x2 = cauchyinv (p, 0, 2);
 x3 = cauchyinv (p, 0, 4);
 x4 = cauchyinv (p, -5, 4);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-10, 10])
 legend ({"μ = 0, β = 1", "μ = 0, β = 2", ...
          "μ = 0, β = 4", "μ = -5, β = 4"}, "location", "northwest")
 title ("Laplace iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, x
 p = [-1 0 0.5 1 2];
 x = [NaN, -Inf, 0, Inf, NaN];
***** assert (laplaceinv (p, 0, 1), x)
***** assert (laplaceinv (p, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), Inf, NaN])
***** assert (laplaceinv ([p, NaN], 0, 1), [x, NaN])
***** assert (laplaceinv (single ([p, NaN]), 0, 1), single ([x, NaN]))
***** assert (laplaceinv ([p, NaN], single (0), 1), single ([x, NaN]))
***** assert (laplaceinv ([p, NaN], 0, single (1)), single ([x, NaN]))
***** error<laplaceinv: function called with too few input arguments.> laplaceinv ()
***** error<laplaceinv: function called with too few input arguments.> laplaceinv (1)
***** error<laplaceinv: function called with too few input arguments.> ...
 laplaceinv (1, 2)
***** error<laplaceinv: function called with too many inputs> laplaceinv (1, 2, 3, 4)
***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
 laplaceinv (1, ones (2), ones (3))
***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
 laplaceinv (ones (2), 1, ones (3))
***** error<laplaceinv: P, MU, and BETA must be of common size or scalars.> ...
 laplaceinv (ones (2), ones (3), 1)
***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (i, 2, 3)
***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, i, 3)
***** error<laplaceinv: P, MU, and BETA must not be complex.> laplaceinv (1, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/wishpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/wishpdf.m
***** assert(wishpdf(4, 3, 3.1), 0.07702496, 1E-7);
***** assert(wishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 0.004529741, 1E-7);
***** assert(wishpdf([6 2 5; 2 10 -5; 5 -5 25], [9 5 5; 5 10 -8; 5 -8 22], 5.1), 4.474865e-10, 1E-15);
***** error wishpdf ()
***** error wishpdf (1, 2)
***** error wishpdf (1, 2, 0)
***** error wishpdf (1, 2)
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/dist_fun/ricepdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/ricepdf.m
***** demo
 ## Plot various PDFs from the Rician distribution
 x = 0:0.01:8;
 y1 = ricepdf (x, 0, 1);
 y2 = ricepdf (x, 0.5, 1);
 y3 = ricepdf (x, 1, 1);
 y4 = ricepdf (x, 2, 1);
 y5 = ricepdf (x, 4, 1);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
 grid on
 ylim ([0, 0.65])
 xlim ([0, 8])
 legend ({"s = 0, σ = 1", "s = 0.5, σ = 1", "s = 1, σ = 1", ...
          "s = 2, σ = 1", "s = 4, σ = 1"}, "location", "northeast")
 title ("Rician PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 2];
 y = [0 0 0.1073 0.1978 0.2846];
***** assert (ricepdf (x, ones (1, 5), 2 * ones (1, 5)), y, 1e-4)
***** assert (ricepdf (x, 1, 2 * ones (1, 5)), y, 1e-4)
***** assert (ricepdf (x, ones (1, 5), 2), y, 1e-4)
***** assert (ricepdf (x, [0 NaN 1 1 1], 2), [0 NaN y(3:5)], 1e-4)
***** assert (ricepdf (x, 1, 2 * [0 NaN 1 1 1]), [0 NaN y(3:5)], 1e-4)
***** assert (ricepdf ([x, NaN], 1, 2), [y, NaN], 1e-4)
***** assert (ricepdf (single ([x, NaN]), 1, 2), single ([y, NaN]), 1e-4)
***** assert (ricepdf ([x, NaN], single (1), 2), single ([y, NaN]), 1e-4)
***** assert (ricepdf ([x, NaN], 1, single (2)), single ([y, NaN]), 1e-4)
***** error<ricepdf: function called with too few input arguments.> ricepdf ()
***** error<ricepdf: function called with too few input arguments.> ricepdf (1)
***** error<ricepdf: function called with too few input arguments.> ricepdf (1,2)
***** error<ricepdf: function called with too many inputs> ricepdf (1,2,3,4)
***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricepdf (ones (3), ones (2), ones (2))
***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricepdf (ones (2), ones (3), ones (2))
***** error<ricepdf: X, S, and SIGMA must be of common size or scalars.> ...
 ricepdf (ones (2), ones (2), ones (3))
***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (i, 2, 2)
***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, i, 2)
***** error<ricepdf: X, S, and SIGMA must not be complex.> ricepdf (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/normpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/normpdf.m
***** demo
 ## Plot various PDFs from the normal distribution
 x = -5:0.01:5;
 y1 = normpdf (x, 0, 0.5);
 y2 = normpdf (x, 0, 1);
 y3 = normpdf (x, 0, 2);
 y4 = normpdf (x, -2, 0.8);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.9])
 legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
          "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northeast")
 title ("Normal PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf, 1, 2, Inf];
 y = 1 / sqrt (2 * pi) * exp (-(x - 1) .^ 2 / 2);
***** assert (normpdf (x, ones (1,4), ones (1,4)), y, eps)
***** assert (normpdf (x, 1, ones (1,4)), y, eps)
***** assert (normpdf (x, ones (1,4), 1), y, eps)
***** assert (normpdf (x, [0 -Inf NaN Inf], 1), [y(1) NaN NaN NaN], eps)
***** assert (normpdf (x, 1, [Inf NaN -1 0]), [NaN NaN NaN NaN], eps)
***** assert (normpdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (normpdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (normpdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** assert (normpdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** error<normpdf: function called with too few input arguments.> normpdf ()
***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 normpdf (ones (3), ones (2), ones (2))
***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 normpdf (ones (2), ones (3), ones (2))
***** error<normpdf: X, MU, and SIGMA must be of common size or scalars.> ...
 normpdf (ones (2), ones (2), ones (3))
***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (i, 2, 2)
***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, i, 2)
***** error<normpdf: X, MU, and SIGMA must not be complex.> normpdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/unifpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/unifpdf.m
***** demo
 ## Plot various PDFs from the continuous uniform distribution
 x = 0:0.001:10;
 y1 = unifpdf (x, 2, 5);
 y2 = unifpdf (x, 3, 9);
 plot (x, y1, "-b", x, y2, "-g")
 grid on
 xlim ([0, 10])
 ylim ([0, 0.4])
 legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northeast")
 title ("Continuous uniform PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 2] + 1;
 y = [0 1 1 1 0];
***** assert (unifpdf (x, ones (1,5), 2*ones (1,5)), y)
***** assert (unifpdf (x, 1, 2*ones (1,5)), y)
***** assert (unifpdf (x, ones (1,5), 2), y)
***** assert (unifpdf (x, [2 NaN 1 1 1], 2), [NaN NaN y(3:5)])
***** assert (unifpdf (x, 1, 2*[0 NaN 1 1 1]), [NaN NaN y(3:5)])
***** assert (unifpdf ([x, NaN], 1, 2), [y, NaN])
***** assert (unifpdf (x, 0, 1), [1 1 0 0 0])
***** assert (unifpdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
***** assert (unifpdf (single ([x, NaN]), single (1), 2), single ([y, NaN]))
***** assert (unifpdf ([x, NaN], 1, single (2)), single ([y, NaN]))
***** error<unifpdf: function called with too few input arguments.> unifpdf ()
***** error<unifpdf: function called with too few input arguments.> unifpdf (1)
***** error<unifpdf: function called with too few input arguments.> unifpdf (1, 2)
***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
 unifpdf (ones (3), ones (2), ones (2))
***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
 unifpdf (ones (2), ones (3), ones (2))
***** error<unifpdf: X, A, and B must be of common size or scalars.> ...
 unifpdf (ones (2), ones (2), ones (3))
***** error<unifpdf: X, A, and B must not be complex.> unifpdf (i, 2, 2)
***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, i, 2)
***** error<unifpdf: X, A, and B must not be complex.> unifpdf (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/trnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/trnd.m
***** assert (size (trnd (2)), [1, 1])
***** assert (size (trnd (ones (2, 1))), [2, 1])
***** assert (size (trnd (ones (2, 2))), [2, 2])
***** assert (size (trnd (1, 3)), [3, 3])
***** assert (size (trnd (1, [4, 1])), [4, 1])
***** assert (size (trnd (1, 4, 1)), [4, 1])
***** assert (size (trnd (1, 4, 1)), [4, 1])
***** assert (size (trnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (trnd (1, 0, 1)), [0, 1])
***** assert (size (trnd (1, 1, 0)), [1, 0])
***** assert (size (trnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (trnd (1, [])), [0, 0])
***** assert (size (trnd (1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (trnd (0, 1, 1), NaN)
***** assert (trnd ([0, 0, 0], [1, 3]), [NaN, NaN, NaN])
***** assert (class (trnd (2)), "double")
***** assert (class (trnd (single (2))), "single")
***** assert (class (trnd (single ([2, 2]))), "single")
***** error<trnd: function called with too few input arguments.> trnd ()
***** error<trnd: DF must not be complex.> trnd (i)
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, -1)
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, 1.2)
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, ones (2))
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, [2 -1 2])
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (1, [2 0 2.5])
***** error<trnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 trnd (ones (2), ones (2))
***** error<trnd: dimensions must be non-negative integers.> ...
 trnd (1, 2, -1, 5)
***** error<trnd: dimensions must be non-negative integers.> ...
 trnd (1, 2, 1.5, 5)
***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 3)
***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), [3, 2])
***** error<trnd: DF must be scalar or of size SZ.> trnd (ones (2,2), 2, 3)
31 tests, 31 passed, 0 known failure, 0 skipped
[inst/dist_fun/wblinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/wblinv.m
***** demo
 ## Plot various iCDFs from the Weibull distribution
 p = 0.001:0.001:0.999;
 x1 = wblinv (p, 1, 0.5);
 x2 = wblinv (p, 1, 1);
 x3 = wblinv (p, 1, 1.5);
 x4 = wblinv (p, 1, 5);
 plot (p, x1, "-b", p, x2, "-r", p, x3, "-m", p, x4, "-g")
 ylim ([0, 2.5])
 grid on
 legend ({"λ = 1, k = 0.5", "λ = 1, k = 1",  ...
          "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "northwest")
 title ("Weibull iCDF")
 xlabel ("probability")
 ylabel ("x")
***** shared p
 p = [-1 0 0.63212055882855778 1 2];
***** assert (wblinv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (wblinv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (wblinv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
***** assert (wblinv (p, [1 -1 NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
***** assert (wblinv (p, 1, [1 -1 NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (wblinv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
***** assert (wblinv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
***** assert (wblinv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
***** assert (wblinv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
***** assert (wblinv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), eps ("single"))
***** error<wblinv: invalid number of input arguments.> wblinv ()
***** error<wblinv: invalid number of input arguments.> wblinv (1,2,3,4)
***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
 wblinv (ones (3), ones (2), ones (2))
***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
 wblinv (ones (2), ones (3), ones (2))
***** error<wblinv: P, LAMBDA, and K must be of common size or scalars.> ...
 wblinv (ones (2), ones (2), ones (3))
***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (i, 2, 2)
***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, i, 2)
***** error<wblinv: P, LAMBDA, and K must not be complex.> wblinv (2, 2, i)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/copulacdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/copulacdf.m
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 theta = [1; 2];
 p = copulacdf ("Clayton", x, theta);
 expected_p = [0.1395; 0.1767];
 assert (p, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 p = copulacdf ("Gumbel", x, 2);
 expected_p = [0.1464; 0.1464];
 assert (p, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 theta = [1; 2];
 p = copulacdf ("Frank", x, theta);
 expected_p = [0.0699; 0.0930];
 assert (p, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 theta = [0.3; 0.7];
 p = copulacdf ("AMH", x, theta);
 expected_p = [0.0629; 0.0959];
 assert (p, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.1:0.4];
 theta = [0.2, 0.1, 0.1, 0.05];
 p = copulacdf ("FGM", x, theta);
 expected_p = [0.0558; 0.0293];
 assert (p, expected_p, 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_fun/cauchyrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/cauchyrnd.m
***** assert (size (cauchyrnd (1, 1)), [1 1])
***** assert (size (cauchyrnd (1, ones (2,1))), [2, 1])
***** assert (size (cauchyrnd (1, ones (2,2))), [2, 2])
***** assert (size (cauchyrnd (ones (2,1), 1)), [2, 1])
***** assert (size (cauchyrnd (ones (2,2), 1)), [2, 2])
***** assert (size (cauchyrnd (1, 1, 3)), [3, 3])
***** assert (size (cauchyrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (cauchyrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (cauchyrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (cauchyrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (cauchyrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (cauchyrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (cauchyrnd (1, 1, [])), [0, 0])
***** assert (size (cauchyrnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (cauchyrnd (1, 1)), "double")
***** assert (class (cauchyrnd (1, single (1))), "single")
***** assert (class (cauchyrnd (1, single ([1, 1]))), "single")
***** assert (class (cauchyrnd (single (1), 1)), "single")
***** assert (class (cauchyrnd (single ([1, 1]), 1)), "single")
***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd ()
***** error<cauchyrnd: function called with too few input arguments.> cauchyrnd (1)
***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
 cauchyrnd (ones (3), ones (2))
***** error<cauchyrnd: X0 and GAMMA must be of common size or scalars.> ...
 cauchyrnd (ones (2), ones (3))
***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (i, 2, 3)
***** error<cauchyrnd: X0 and GAMMA must not be complex.> cauchyrnd (1, i, 3)
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, -1)
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, 1.2)
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, ones (2))
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, [2 -1 2])
***** error<cauchyrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 cauchyrnd (1, 2, [2 0 2.5])
***** error<cauchyrnd: dimensions must be non-negative integers.> ...
 cauchyrnd (1, 2, 2, -1, 5)
***** error<cauchyrnd: dimensions must be non-negative integers.> ...
 cauchyrnd (1, 2, 2, 1.5, 5)
***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
 cauchyrnd (2, ones (2), 3)
***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
 cauchyrnd (2, ones (2), [3, 2])
***** error<cauchyrnd: X0 and GAMMA must be scalars or of size SZ.> ...
 cauchyrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/nctinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/nctinv.m
***** demo
 ## Plot various iCDFs from the noncentral T distribution
 p = 0.001:0.001:0.999;
 x1 = nctinv (p, 1, 0);
 x2 = nctinv (p, 4, 0);
 x3 = nctinv (p, 1, 2);
 x4 = nctinv (p, 4, 2);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
 grid on
 ylim ([-5, 5])
 legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
          "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northwest")
 title ("Noncentral T iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** demo
 ## Compare the noncentral T iCDF with MU = 1 to the T iCDF
 ## with the same number of degrees of freedom (10).

 p = 0.001:0.001:0.999;
 x1 = nctinv (p, 10, 1);
 x2 = tinv (p, 10);
 plot (p, x1, "-", p, x2, "-");
 grid on
 ylim ([-5, 5])
 legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
 title ("Noncentral T vs T quantile functions")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 x = [-Inf,-0.3347,0.1756,0.5209,0.8279,1.1424,1.5021,1.9633,2.6571,4.0845,Inf];
 assert (nctinv ([0:0.1:1], 2, 1), x, 1e-4);
***** test
 x = [-Inf,1.5756,2.0827,2.5343,3.0043,3.5406,4.2050,5.1128,6.5510,9.6442,Inf];
 assert (nctinv ([0:0.1:1], 2, 3), x, 1e-4);
***** test
 x = [-Inf,2.2167,2.9567,3.7276,4.6464,5.8455,7.5619,10.3327,15.7569,31.8159,Inf];
 assert (nctinv ([0:0.1:1], 1, 4), x, 1e-4);
***** test
 x = [1.7791   1.9368   2.0239   2.0801   2.1195   2.1489];
 assert (nctinv (0.05, [1, 2, 3, 4, 5, 6], 4), x, 1e-4);
***** test
 x = [-0.7755, 0.3670, 1.2554, 2.0239, 2.7348, 3.4154];
 assert (nctinv (0.05, 3, [1, 2, 3, 4, 5, 6]), x, 1e-4);
***** test
 x = [-0.7183, 0.3624, 1.2878, 2.1195, -3.5413, 3.6430];
 assert (nctinv (0.05, 5, [1, 2, 3, 4, -1, 6]), x, 1e-4);
***** test
 assert (nctinv (0.996, 5, 8), 30.02610554063658, 2e-11);
***** error<nctinv: function called with too few input arguments.> nctinv ()
***** error<nctinv: function called with too few input arguments.> nctinv (1)
***** error<nctinv: function called with too few input arguments.> nctinv (1, 2)
***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
 nctinv (ones (3), ones (2), ones (2))
***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
 nctinv (ones (2), ones (3), ones (2))
***** error<nctinv: P, DF, and MU must be of common size or scalars.> ...
 nctinv (ones (2), ones (2), ones (3))
***** error<nctinv: P, DF, and MU must not be complex.> nctinv (i, 2, 2)
***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, i, 2)
***** error<nctinv: P, DF, and MU must not be complex.> nctinv (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/wblrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/wblrnd.m
***** assert (size (wblrnd (1, 1)), [1, 1])
***** assert (size (wblrnd (1, ones (2, 1))), [2, 1])
***** assert (size (wblrnd (1, ones (2, 2))), [2, 2])
***** assert (size (wblrnd (ones (2, 1), 1)), [2, 1])
***** assert (size (wblrnd (ones (2, 2), 1)), [2, 2])
***** assert (size (wblrnd (1, 1, 3)), [3, 3])
***** assert (size (wblrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (wblrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (wblrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (wblrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (wblrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (wblrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (wblrnd (1, 1, [])), [0, 0])
***** assert (size (wblrnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (wblrnd (1, 1)), "double")
***** assert (class (wblrnd (1, single (1))), "single")
***** assert (class (wblrnd (1, single ([1, 1]))), "single")
***** assert (class (wblrnd (single (1), 1)), "single")
***** assert (class (wblrnd (single ([1, 1]), 1)), "single")
***** error<wblrnd: function called with too few input arguments.> wblrnd ()
***** error<wblrnd: function called with too few input arguments.> wblrnd (1)
***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
 wblrnd (ones (3), ones (2))
***** error<wblrnd: LAMBDA and K must be of common size or scalars.> ...
 wblrnd (ones (2), ones (3))
***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (i, 2, 3)
***** error<wblrnd: LAMBDA and K must not be complex.> wblrnd (1, i, 3)
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, -1)
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, 1.2)
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, ones (2))
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, [2 -1 2])
***** error<wblrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 wblrnd (1, 2, [2 0 2.5])
***** error<wblrnd: dimensions must be non-negative integers.> ...
 wblrnd (1, 2, 2, -1, 5)
***** error<wblrnd: dimensions must be non-negative integers.> ...
 wblrnd (1, 2, 2, 1.5, 5)
***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
 wblrnd (2, ones (2), 3)
***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
 wblrnd (2, ones (2), [3, 2])
***** error<wblrnd: LAMBDA and K must be scalar or of size SZ.> ...
 wblrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/gampdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gampdf.m
***** demo
 ## Plot various PDFs from the Gamma distribution
 x = 0:0.01:20;
 y1 = gampdf (x, 1, 2);
 y2 = gampdf (x, 2, 2);
 y3 = gampdf (x, 3, 2);
 y4 = gampdf (x, 5, 1);
 y5 = gampdf (x, 9, 0.5);
 y6 = gampdf (x, 7.5, 1);
 y7 = gampdf (x, 0.5, 1);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
       x, y5, "-k", x, y6, "-b", x, y7, "-c")
 grid on
 ylim ([0,0.5])
 legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
          "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
          "α = 0.5, β = 1"}, "location", "northeast")
 title ("Gamma PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 Inf];
 y = [0 exp(-x(2:end))];
***** assert (gampdf (x, ones (1,5), ones (1,5)), y)
***** assert (gampdf (x, 1, ones (1,5)), y)
***** assert (gampdf (x, ones (1,5), 1), y)
***** assert (gampdf (x, [0 -Inf NaN Inf 1], 1), [NaN NaN NaN 0 y(5)])
***** assert (gampdf (x, [0 Inf NaN Inf 1], 1), [NaN 0 NaN 0 y(5)])
***** assert (gampdf (x, 1, [0 -Inf NaN Inf 1]), [NaN NaN NaN 0 y(5)])
***** assert (gampdf ([x, NaN], 1, 1), [y, NaN])
***** assert (gampdf (2, Inf, 4), 0)
***** assert (gampdf (2, 4, Inf), 0)
***** assert (gampdf (2, Inf, Inf), 0)
***** assert (gampdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
***** assert (gampdf ([x, NaN], single (1), 1), single ([y, NaN]))
***** assert (gampdf ([x, NaN], 1, single (1)), single ([y, NaN]))
***** error<gampdf: function called with too few input arguments.> gampdf ()
***** error<gampdf: function called with too few input arguments.> gampdf (1)
***** error<gampdf: function called with too few input arguments.> gampdf (1,2)
***** error<gampdf: X, A, and B must be of common size or scalars.> ...
 gampdf (ones (3), ones (2), ones (2))
***** error<gampdf: X, A, and B must be of common size or scalars.> ...
 gampdf (ones (2), ones (3), ones (2))
***** error<gampdf: X, A, and B must be of common size or scalars.> ...
 gampdf (ones (2), ones (2), ones (3))
***** error<gampdf: X, A, and B must not be complex.> gampdf (i, 2, 2)
***** error<gampdf: X, A, and B must not be complex.> gampdf (2, i, 2)
***** error<gampdf: X, A, and B must not be complex.> gampdf (2, 2, i)
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/dist_fun/raylpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/raylpdf.m
***** demo
 ## Plot various PDFs from the Rayleigh distribution
 x = 0:0.01:10;
 y1 = raylpdf (x, 0.5);
 y2 = raylpdf (x, 1);
 y3 = raylpdf (x, 2);
 y4 = raylpdf (x, 3);
 y5 = raylpdf (x, 4);
 plot (x, y1, "-b", x, y2, "g", x, y3, "-r", x, y4, "-m", x, y5, "-k")
 grid on
 ylim ([0, 1.25])
 legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
          "σ = 3", "σ = 4"}, "location", "northeast")
 title ("Rayleigh PDF")
 xlabel ("values in x")
 ylabel ("density")
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 y = raylpdf (x, sigma);
 expected_y = [0.0000, 0.1212, 0.1051, 0.0874, 0.0738, 0.0637];
 assert (y, expected_y, 0.001);
***** test
 x = 0:0.5:2.5;
 y = raylpdf (x, 0.5);
 expected_y = [0.0000, 1.2131, 0.5413, 0.0667, 0.0027, 0.0000];
 assert (y, expected_y, 0.001);
***** error<raylpdf: function called with too few input arguments.> raylpdf ()
***** error<raylpdf: function called with too few input arguments.> raylpdf (1)
***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
 raylpdf (ones (3), ones (2))
***** error<raylpdf: X and SIGMA must be of common size or scalars.> ...
 raylpdf (ones (2), ones (3))
***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (i, 2)
***** error<raylpdf: X and SIGMA must not be complex.> raylpdf (2, i)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/vmrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/vmrnd.m
***** assert (size (vmrnd (1, 1)), [1, 1])
***** assert (size (vmrnd (1, ones (2, 1))), [2, 1])
***** assert (size (vmrnd (1, ones (2, 2))), [2, 2])
***** assert (size (vmrnd (ones (2, 1), 1)), [2, 1])
***** assert (size (vmrnd (ones (2, 2), 1)), [2, 2])
***** assert (size (vmrnd (1, 1, 3)), [3, 3])
***** assert (size (vmrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (vmrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (vmrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (vmrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (vmrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (vmrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (vmrnd (1, 1, [])), [0, 0])
***** assert (size (vmrnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (vmrnd (1, 1)), "double")
***** assert (class (vmrnd (1, single (1))), "single")
***** assert (class (vmrnd (1, single ([1, 1]))), "single")
***** assert (class (vmrnd (single (1), 1)), "single")
***** assert (class (vmrnd (single ([1, 1]), 1)), "single")
***** error<vmrnd: function called with too few input arguments.> vmrnd ()
***** error<vmrnd: function called with too few input arguments.> vmrnd (1)
***** error<vmrnd: MU and K must be of common size or scalars.> ...
 vmrnd (ones (3), ones (2))
***** error<vmrnd: MU and K must be of common size or scalars.> ...
 vmrnd (ones (2), ones (3))
***** error<vmrnd: MU and K must not be complex.> vmrnd (i, 2, 3)
***** error<vmrnd: MU and K must not be complex.> vmrnd (1, i, 3)
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, -1)
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, 1.2)
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, ones (2))
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, [2 -1 2])
***** error<vmrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 vmrnd (1, 2, [2 0 2.5])
***** error<vmrnd: dimensions must be non-negative integers.> ...
 vmrnd (1, 2, 2, -1, 5)
***** error<vmrnd: dimensions must be non-negative integers.> ...
 vmrnd (1, 2, 2, 1.5, 5)
***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
 vmrnd (2, ones (2), 3)
***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
 vmrnd (2, ones (2), [3, 2])
***** error<vmrnd: MU and K must be scalars or of size SZ.> ...
 vmrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/gamrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gamrnd.m
***** assert (size (gamrnd (1, 1)), [1 1])
***** assert (size (gamrnd (1, ones (2,1))), [2, 1])
***** assert (size (gamrnd (1, ones (2,2))), [2, 2])
***** assert (size (gamrnd (ones (2,1), 1)), [2, 1])
***** assert (size (gamrnd (ones (2,2), 1)), [2, 2])
***** assert (size (gamrnd (1, 1, 3)), [3, 3])
***** assert (size (gamrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (gamrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (gamrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (gamrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (gamrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (gamrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (gamrnd (1, 1, [])), [0, 0])
***** assert (size (gamrnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (gamrnd (1, 1)), "double")
***** assert (class (gamrnd (1, single (1))), "single")
***** assert (class (gamrnd (1, single ([1, 1]))), "single")
***** assert (class (gamrnd (single (1), 1)), "single")
***** assert (class (gamrnd (single ([1, 1]), 1)), "single")
***** error<gamrnd: function called with too few input arguments.> gamrnd ()
***** error<gamrnd: function called with too few input arguments.> gamrnd (1)
***** error<gamrnd: A and B must be of common size or scalars.> ...
 gamrnd (ones (3), ones (2))
***** error<gamrnd: A and B must be of common size or scalars.> ...
 gamrnd (ones (2), ones (3))
***** error<gamrnd: A and B must not be complex.> gamrnd (i, 2, 3)
***** error<gamrnd: A and B must not be complex.> gamrnd (1, i, 3)
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, -1)
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, 1.2)
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, ones (2))
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, [2 -1 2])
***** error<gamrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 gamrnd (1, 2, [2 0 2.5])
***** error<gamrnd: dimensions must be non-negative integers.> ...
 gamrnd (1, 2, 2, -1, 5)
***** error<gamrnd: dimensions must be non-negative integers.> ...
 gamrnd (1, 2, 2, 1.5, 5)
***** error<gamrnd: A and B must be scalars or of size SZ.> ...
 gamrnd (2, ones (2), 3)
***** error<gamrnd: A and B must be scalars or of size SZ.> ...
 gamrnd (2, ones (2), [3, 2])
***** error<gamrnd: A and B must be scalars or of size SZ.> ...
 gamrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/jsucdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/jsucdf.m
***** error jsucdf ()
***** error jsucdf (1, 2, 3, 4)
***** error<jsucdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
 jsucdf (1, ones (2), ones (3))
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/dist_fun/tricdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/tricdf.m
***** demo
 ## Plot various CDFs from the triangular distribution
 x = 0.001:0.001:10;
 p1 = tricdf (x, 3, 4, 6);
 p2 = tricdf (x, 1, 2, 5);
 p3 = tricdf (x, 2, 3, 9);
 p4 = tricdf (x, 2, 5, 9);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([0, 10])
 legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
          "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
         "location", "southeast")
 title ("Triangular CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
 y = [0, 0, 0.02, 0.5, 0.98, 1 1];
***** assert (tricdf (x, ones (1,7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2), y, eps)
***** assert (tricdf (x, 1 * ones (1, 7), 1.5, 2, "upper"), 1 - y, eps)
***** assert (tricdf (x, 1, 1.5, 2 * ones (1, 7)), y, eps)
***** assert (tricdf (x, 1, 1.5 * ones (1, 7), 2), y, eps)
***** assert (tricdf (x, 1, 1.5, 2), y, eps)
***** assert (tricdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), ...
 [y(1:2), NaN, y(4:7)], eps)
***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
 [y(1:2), NaN, y(4:7)], eps)
***** assert (tricdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), ...
 [y(1:2), NaN, y(4:7)], eps)
***** assert (tricdf ([x, NaN], 1, 1.5, 2), [y, NaN], eps)
***** assert (tricdf (single ([x, NaN]), 1, 1.5, 2), ...
 single ([y, NaN]), eps("single"))
***** assert (tricdf ([x, NaN], single (1), 1.5, 2), ...
 single ([y, NaN]), eps("single"))
***** assert (tricdf ([x, NaN], 1, single (1.5), 2), ...
 single ([y, NaN]), eps("single"))
***** assert (tricdf ([x, NaN], 1, 1.5, single (2)), ...
 single ([y, NaN]), eps("single"))
***** error<tricdf: function called with too few input arguments.> tricdf ()
***** error<tricdf: function called with too few input arguments.> tricdf (1)
***** error<tricdf: function called with too few input arguments.> tricdf (1, 2)
***** error<tricdf: function called with too few input arguments.> tricdf (1, 2, 3)
***** error<tricdf: function called with too many inputs> ...
 tricdf (1, 2, 3, 4, 5, 6)
***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, "tail")
***** error<tricdf: invalid argument for upper tail.> tricdf (1, 2, 3, 4, 5)
***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
 tricdf (ones (3), ones (2), ones(2), ones(2))
***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
 tricdf (ones (2), ones (3), ones(2), ones(2))
***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
 tricdf (ones (2), ones (2), ones(3), ones(2))
***** error<tricdf: X, A, B, and C must be of common size or scalars.> ...
 tricdf (ones (2), ones (2), ones(2), ones(3))
***** error<tricdf: X, A, B, and C must not be complex.> tricdf (i, 2, 3, 4)
***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, i, 3, 4)
***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, i, 4)
***** error<tricdf: X, A, B, and C must not be complex.> tricdf (1, 2, 3, i)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/mvtcdfqmc.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/mvtcdfqmc.m
***** error mvtcdfqmc (1, 2, 3);
***** error mvtcdfqmc (1, 2, 3, 4, 5, 6, 7, 8);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/dist_fun/logirnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/logirnd.m
***** assert (size (logirnd (1, 1)), [1, 1])
***** assert (size (logirnd (1, ones (2, 1))), [2, 1])
***** assert (size (logirnd (1, ones (2, 2))), [2, 2])
***** assert (size (logirnd (ones (2, 1), 1)), [2, 1])
***** assert (size (logirnd (ones (2, 2), 1)), [2, 2])
***** assert (size (logirnd (1, 1, 3)), [3, 3])
***** assert (size (logirnd (1, 1, [4, 1])), [4, 1])
***** assert (size (logirnd (1, 1, 4, 1)), [4, 1])
***** assert (size (logirnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (logirnd (1, 1, 0, 1)), [0, 1])
***** assert (size (logirnd (1, 1, 1, 0)), [1, 0])
***** assert (size (logirnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (logirnd (1, 1, [])), [0, 0])
***** assert (size (logirnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (logirnd (1, 1)), "double")
***** assert (class (logirnd (1, single (1))), "single")
***** assert (class (logirnd (1, single ([1, 1]))), "single")
***** assert (class (logirnd (single (1), 1)), "single")
***** assert (class (logirnd (single ([1, 1]), 1)), "single")
***** error<logirnd: function called with too few input arguments.> logirnd ()
***** error<logirnd: function called with too few input arguments.> logirnd (1)
***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
 logirnd (ones (3), ones (2))
***** error<logirnd: MU and SIGMA must be of common size or scalars.> ...
 logirnd (ones (2), ones (3))
***** error<logirnd: MU and SIGMA must not be complex.> logirnd (i, 2, 3)
***** error<logirnd: MU and SIGMA must not be complex.> logirnd (1, i, 3)
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, -1)
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, 1.2)
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, ones (2))
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, [2 -1 2])
***** error<logirnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 logirnd (1, 2, [2 0 2.5])
***** error<logirnd: dimensions must be non-negative integers.> ...
 logirnd (1, 2, 2, -1, 5)
***** error<logirnd: dimensions must be non-negative integers.> ...
 logirnd (1, 2, 2, 1.5, 5)
***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
 logirnd (2, ones (2), 3)
***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
 logirnd (2, ones (2), [3, 2])
***** error<logirnd: MU and SIGMA must be scalars or of size SZ.> ...
 logirnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/poissrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/poissrnd.m
***** assert (size (poissrnd (2)), [1, 1])
***** assert (size (poissrnd (ones (2, 1))), [2, 1])
***** assert (size (poissrnd (ones (2, 2))), [2, 2])
***** assert (size (poissrnd (1, 3)), [3, 3])
***** assert (size (poissrnd (1, [4, 1])), [4, 1])
***** assert (size (poissrnd (1, 4, 1)), [4, 1])
***** assert (size (poissrnd (1, 4, 1)), [4, 1])
***** assert (size (poissrnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (poissrnd (1, 0, 1)), [0, 1])
***** assert (size (poissrnd (1, 1, 0)), [1, 0])
***** assert (size (poissrnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (poissrnd (1, [])), [0, 0])
***** assert (size (poissrnd (1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (poissrnd (0, 1, 1), 0)
***** assert (poissrnd ([0, 0, 0], [1, 3]), [0 0 0])
***** assert (class (poissrnd (2)), "double")
***** assert (class (poissrnd (single (2))), "single")
***** assert (class (poissrnd (single ([2 2]))), "single")
***** error<poissrnd: function called with too few input arguments.> poissrnd ()
***** error<poissrnd: LAMBDA must not be complex.> poissrnd (i)
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, -1)
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, 1.2)
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, ones (2))
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, [2 -1 2])
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (1, [2 0 2.5])
***** error<poissrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 poissrnd (ones (2), ones (2))
***** error<poissrnd: dimensions must be non-negative integers.> ...
 poissrnd (1, 2, -1, 5)
***** error<poissrnd: dimensions must be non-negative integers.> ...
 poissrnd (1, 2, 1.5, 5)
***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 3)
***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), [3, 2])
***** error<poissrnd: LAMBDA must be scalar or of size SZ.> poissrnd (ones (2,2), 2, 3)
31 tests, 31 passed, 0 known failure, 0 skipped
[inst/dist_fun/loglcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/loglcdf.m
***** demo
 ## Plot various CDFs from the log-logistic distribution
 x = 0:0.001:2;
 p1 = loglcdf (x, log (1), 1/0.5);
 p2 = loglcdf (x, log (1), 1);
 p3 = loglcdf (x, log (1), 1/2);
 p4 = loglcdf (x, log (1), 1/4);
 p5 = loglcdf (x, log (1), 1/8);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 legend ({"σ = 2 (β = 0.5)", "σ = 1 (β = 1)", "σ = 0.5 (β = 2)", ...
          "σ = 0.25 (β = 4)", "σ = 0.125 (β = 8)"}, "location", "northwest")
 grid on
 title ("Log-logistic CDF")
 xlabel ("values in x")
 ylabel ("probability")
 text (0.05, 0.64, "μ = 0 (α = 1), values of σ (β) as shown in legend")
***** shared out1, out2
 out1 = [0, 0.5, 0.66666667, 0.75, 0.8, 0.83333333];
 out2 = [0, 0.4174, 0.4745, 0.5082, 0.5321, 0.5506];
***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
***** assert (loglcdf ([0:5], 0, 1), out1, 1e-8)
***** assert (loglcdf ([0:5], 0, 1, "upper"), 1 - out1, 1e-8)
***** assert (loglcdf ([0:5], 1, 3), out2, 1e-4)
***** assert (loglcdf ([0:5], 1, 3, "upper"), 1 - out2, 1e-4)
***** assert (class (loglcdf (single (1), 2, 3)), "single")
***** assert (class (loglcdf (1, single (2), 3)), "single")
***** assert (class (loglcdf (1, 2, single (3))), "single")
***** error<loglcdf: function called with too few input arguments.> loglcdf (1)
***** error<loglcdf: function called with too few input arguments.> loglcdf (1, 2)
***** error<loglcdf: invalid argument for upper tail.> ...
 loglcdf (1, 2, 3, 4)
***** error<loglcdf: invalid argument for upper tail.> ...
 loglcdf (1, 2, 3, "uper")
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (1, ones (2), ones (3))
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (1, ones (2), ones (3), "upper")
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (ones (2), 1, ones (3))
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (ones (2), 1, ones (3), "upper")
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (ones (2), ones (3), 1)
***** error<loglcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 loglcdf (ones (2), ones (3), 1, "upper")
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3)
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (i, 2, 3, "upper")
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3)
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, i, 3, "upper")
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i)
***** error<loglcdf: X, MU, and SIGMA must not be complex.> loglcdf (1, 2, i, "upper")
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/plcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/plcdf.m
***** demo
 ## Plot various CDFs from the Piecewise linear distribution
 data = 0:0.01:10;
 x1 = [0, 1, 3, 4, 7, 10];
 Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
 x2 = [0, 2, 5, 6, 7, 8];
 Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
 p1 = plcdf (data, x1, Fx1);
 p2 = plcdf (data, x2, Fx2);
 plot (data, p1, "-b", data, p2, "g")
 grid on
 ylim ([0, 1])
 xlim ([0, 10])
 legend ({"x1, Fx1", "x2, Fx2"}, "location", "southeast")
 title ("Piecewise linear CDF")
 xlabel ("values in data")
 ylabel ("probability")
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 1], [0, 1]);
 assert (p, data);
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 2], [0, 1]);
 assert (p, 0.5 * data);
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 1], [0, 0.5]);
 assert (p, 0.5 * data);
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 0.5], [0, 1]);
 assert (p, [0, 0.4, 0.8, 1, 1, 1]);
***** test
 data = 0:0.2:1;
 p = plcdf (data, [0, 1], [0, 1], "upper");
 assert (p, 1 - data);
***** error<plcdf: function called with too few input arguments.> plcdf ()
***** error<plcdf: function called with too few input arguments.> plcdf (1)
***** error<plcdf: function called with too few input arguments.> plcdf (1, 2)
***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, "uper")
***** error<plcdf: invalid argument for upper tail.> plcdf (1, 2, 3, 4)
***** error<plcdf: X and FX must be vectors of equal size.> ...
 plcdf (1, [0, 1, 2], [0, 1])
***** error<plcdf: X and FX must be at least two-elements long.> ...
 plcdf (1, [0], [1])
***** error<plcdf: FX must be bounded in the range> ...
 plcdf (1, [0, 1, 2], [0, 1, 1.5])
***** error<plcdf: FX must be bounded in the range> ...
 plcdf (1, [0, 1, 2], [0, i, 1])
***** error<plcdf: DATA, X, and FX must not be complex.> ...
 plcdf (i, [0, 1, 2], [0, 0.5, 1])
***** error<plcdf: DATA, X, and FX must not be complex.> ...
 plcdf (1, [0, i, 2], [0, 0.5, 1])
***** error<plcdf: DATA, X, and FX must not be complex.> ...
 plcdf (1, [0, 1, 2], [0, 0.5i, 1])
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/evinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/evinv.m
***** demo
 ## Plot various iCDFs from the extreme value distribution
 p = 0.001:0.001:0.999;
 x1 = evinv (p, 0.5, 2);
 x2 = evinv (p, 1.0, 2);
 x3 = evinv (p, 1.5, 3);
 x4 = evinv (p, 3.0, 4);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-10, 10])
 legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
          "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "northwest")
 title ("Extreme value iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, x
 p = [0, 0.05, 0.5 0.95];
 x = [-Inf, -2.9702, -0.3665, 1.0972];
***** assert (evinv (p), x, 1e-4)
***** assert (evinv (p, zeros (1,4), ones (1,4)), x, 1e-4)
***** assert (evinv (p, 0, ones (1,4)), x, 1e-4)
***** assert (evinv (p, zeros (1,4), 1), x, 1e-4)
***** assert (evinv (p, [0, -Inf, NaN, Inf], 1), [-Inf, -Inf, NaN, Inf], 1e-4)
***** assert (evinv (p, 0, [Inf, NaN, -1, 0]), [-Inf, NaN, NaN, NaN], 1e-4)
***** assert (evinv ([p(1:2), NaN, p(4)], 0, 1), [x(1:2), NaN, x(4)], 1e-4)
***** assert (evinv ([p, NaN], 0, 1), [x, NaN], 1e-4)
***** assert (evinv (single ([p, NaN]), 0, 1), single ([x, NaN]), 1e-4)
***** assert (evinv ([p, NaN], single (0), 1), single ([x, NaN]), 1e-4)
***** assert (evinv ([p, NaN], 0, single (1)), single ([x, NaN]), 1e-4)
***** error<evinv: invalid number of input arguments.> evinv ()
***** error evinv (1,2,3,4,5,6)
***** error<evinv: P, MU, and SIGMA must be of common size or scalars.> ...
 evinv (ones (3), ones (2), ones (2))
***** error<evinv: invalid size of covariance matrix.> ...
 [p, plo, pup] = evinv (2, 3, 4, [1, 2])
***** error<evinv: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = evinv (1, 2, 3)
***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
 evinv (1, 2, 3, [1, 0; 0, 1], 0)
***** error<evinv: invalid value for alpha.> [p, plo, pup] = ...
 evinv (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (i, 2, 2)
***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, i, 2)
***** error<evinv: P, MU, and SIGMA must not be complex.> evinv (2, 2, i)
***** error<evinv: bad covariance matrix.> ...
 [p, plo, pup] = evinv (1, 2, 3, [-1, -10; -Inf, -Inf], 0.04)
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/dist_fun/wishrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/wishrnd.m
***** assert (size (wishrnd (1,2)), [1, 1]);
***** assert (size (wishrnd (1,2,[])), [1, 1]);
***** assert (size (wishrnd (1,2,1)), [1, 1]);
***** assert (size (wishrnd ([],2,1)), [1, 1]);
***** assert (size (wishrnd ([3 1; 1 3], 2.00001, [], 1)), [2, 2]);
***** assert (size (wishrnd (eye(2), 2, [], 3)), [2, 2, 3]);
***** error wishrnd ()
***** error wishrnd (1)
***** error wishrnd ([1; 1], 2)
***** test
 W = wishrnd (eye (3), 2.5);
 assert (size (W), [3, 3]);
***** warning <Wishart distribution undefined> wishrnd (eye (3), 1.5);
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fun/gpcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gpcdf.m
***** demo
 ## Plot various CDFs from the generalized Pareto distribution
 x = 0:0.001:5;
 p1 = gpcdf (x, 1, 1, 0);
 p2 = gpcdf (x, 5, 1, 0);
 p3 = gpcdf (x, 20, 1, 0);
 p4 = gpcdf (x, 1, 2, 0);
 p5 = gpcdf (x, 5, 2, 0);
 p6 = gpcdf (x, 20, 2, 0);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", ...
       x, p4, "-c", x, p5, "-m", x, p6, "-k")
 grid on
 xlim ([0, 5])
 legend ({"k = 1, σ = 1, θ = 0", "k = 5, σ = 1, θ = 0", ...
          "k = 20, σ = 1, θ = 0", "k = 1, σ = 2, θ = 0", ...
          "k = 5, σ = 2, θ = 0", "k = 20, σ = 2, θ = 0"}, ...
         "location", "northwest")
 title ("Generalized Pareto CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y1, y1u, y2, y2u, y3, y3u
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y1 = [0, 0, 0, 0.3934693402873666, 0.6321205588285577, 1];
 y1u = [1, 1, 1, 0.6065306597126334, 0.3678794411714423, 0];
 y2 = [0, 0, 0, 1/3, 1/2, 1];
 y2u = [1, 1, 1, 2/3, 1/2, 0];
 y3 = [0, 0, 0, 1/2, 1, 1];
 y3u = [1, 1, 1, 1/2, 0, 0];
***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6)), y1, eps)
***** assert (gpcdf (x, 0, 1, zeros (1,6)), y1, eps)
***** assert (gpcdf (x, 0, ones (1,6), 0), y1, eps)
***** assert (gpcdf (x, zeros (1,6), 1, 0), y1, eps)
***** assert (gpcdf (x, 0, 1, 0), y1, eps)
***** assert (gpcdf (x, 0, 1, [0, 0, 0, NaN, 0, 0]), [y1(1:3), NaN, y1(5:6)], eps)
***** assert (gpcdf (x, 0, [1, 1, 1, NaN, 1, 1], 0), [y1(1:3), NaN, y1(5:6)], eps)
***** assert (gpcdf (x, [0, 0, 0, NaN, 0, 0], 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 0, 1, 0), [y1(1:3), NaN, y1(5:6)], eps)
***** assert (gpcdf (x, zeros (1,6), ones (1,6), zeros (1,6), "upper"), y1u, eps)
***** assert (gpcdf (x, 0, 1, zeros (1,6), "upper"), y1u, eps)
***** assert (gpcdf (x, 0, ones (1,6), 0, "upper"), y1u, eps)
***** assert (gpcdf (x, zeros (1,6), 1, 0, "upper"), y1u, eps)
***** assert (gpcdf (x, 0, 1, 0, "upper"), y1u, eps)
***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6)), y2, eps)
***** assert (gpcdf (x, 1, 1, zeros (1,6)), y2, eps)
***** assert (gpcdf (x, 1, ones (1,6), 0), y2, eps)
***** assert (gpcdf (x, ones (1,6), 1, 0), y2, eps)
***** assert (gpcdf (x, 1, 1, 0), y2, eps)
***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0]), [y2(1:3), NaN, y2(5:6)], eps)
***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0), [y2(1:3), NaN, y2(5:6)], eps)
***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0), [y2(1:3), NaN, y2(5:6)], eps)
***** assert (gpcdf (x, ones (1,6), ones (1,6), zeros (1,6), "upper"), y2u, eps)
***** assert (gpcdf (x, 1, 1, zeros (1,6), "upper"), y2u, eps)
***** assert (gpcdf (x, 1, ones (1,6), 0, "upper"), y2u, eps)
***** assert (gpcdf (x, ones (1,6), 1, 0, "upper"), y2u, eps)
***** assert (gpcdf (x, 1, 1, 0, "upper"), y2u, eps)
***** assert (gpcdf (x, 1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
                        [y2u(1:3), NaN, y2u(5:6)], eps)
***** assert (gpcdf (x, 1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
                        [y2u(1:3), NaN, y2u(5:6)], eps)
***** assert (gpcdf (x, [1, 1, 1, NaN, 1, 1], 1, 0, "upper"), ...
                        [y2u(1:3), NaN, y2u(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], 1, 1, 0, "upper"), ...
                        [y2u(1:3), NaN, y2u(5:6)], eps)
***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6)), y3, eps)
***** assert (gpcdf (x, -1, 1, zeros (1,6)), y3, eps)
***** assert (gpcdf (x, -1, ones (1,6), 0), y3, eps)
***** assert (gpcdf (x, -ones (1,6), 1, 0), y3, eps)
***** assert (gpcdf (x, -1, 1, 0), y3, eps)
***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0]), [y3(1:3), NaN, y3(5:6)], eps)
***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0), [y3(1:3), NaN, y3(5:6)], eps)
***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0), [y3(1:3), NaN, y3(5:6)], eps)
***** assert (gpcdf (x, -ones (1,6), ones (1,6), zeros (1,6), "upper"), y3u, eps)
***** assert (gpcdf (x, -1, 1, zeros (1,6), "upper"), y3u, eps)
***** assert (gpcdf (x, -1, ones (1,6), 0, "upper"), y3u, eps)
***** assert (gpcdf (x, -ones (1,6), 1, 0, "upper"), y3u, eps)
***** assert (gpcdf (x, -1, 1, 0, "upper"), y3u, eps)
***** assert (gpcdf (x, -1, 1, [0, 0, 0, NaN, 0, 0], "upper"), ...
                         [y3u(1:3), NaN, y3u(5:6)], eps)
***** assert (gpcdf (x, -1, [1, 1, 1, NaN, 1, 1], 0, "upper"), ...
                          [y3u(1:3), NaN, y3u(5:6)], eps)
***** assert (gpcdf (x, [-1, -1, -1, NaN, -1, -1], 1, 0, "upper"), ...
                          [y3u(1:3), NaN, y3u(5:6)], eps)
***** assert (gpcdf ([x(1:3), NaN, x(5:6)], -1, 1, 0, "upper"), ...
                          [y3u(1:3), NaN, y3u(5:6)], eps)
***** assert (gpcdf (single ([x, NaN]), 0, 1, 0), single ([y1, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], 0, 1, single (0)), single ([y1, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], 0, single (1), 0), single ([y1, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], single (0), 1, 0), single ([y1, NaN]), eps("single"))
***** assert (gpcdf (single ([x, NaN]), 1, 1, 0), single ([y2, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], 1, 1, single (0)), single ([y2, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], 1, single (1), 0), single ([y2, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], single (1), 1, 0), single ([y2, NaN]), eps("single"))
***** assert (gpcdf (single ([x, NaN]), -1, 1, 0), single ([y3, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], -1, 1, single (0)), single ([y3, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], -1, single (1), 0), single ([y3, NaN]), eps("single"))
***** assert (gpcdf ([x, NaN], single (-1), 1, 0), single ([y3, NaN]), eps("single"))
***** error<gpcdf: function called with too few input arguments.> gpcdf ()
***** error<gpcdf: function called with too few input arguments.> gpcdf (1)
***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2)
***** error<gpcdf: function called with too few input arguments.> gpcdf (1, 2, 3)
***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, "tail")
***** error<gpcdf: invalid argument for upper tail.> gpcdf (1, 2, 3, 4, 5)
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (3), ones (2), ones(2), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (3), ones(2), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (2), ones(3), ones(2))
***** error<gpcdf: X, K, SIGMA, and THETA must be of common size or scalars.> ...
 gpcdf (ones (2), ones (2), ones(2), ones(3))
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (i, 2, 3, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, i, 3, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, i, 4)
***** error<gpcdf: X, K, SIGMA, and THETA must not be complex.> gpcdf (1, 2, 3, i)
76 tests, 76 passed, 0 known failure, 0 skipped
[inst/dist_fun/gaminv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gaminv.m
***** demo
 ## Plot various iCDFs from the Gamma distribution
 p = 0.001:0.001:0.999;
 x1 = gaminv (p, 1, 2);
 x2 = gaminv (p, 2, 2);
 x3 = gaminv (p, 3, 2);
 x4 = gaminv (p, 5, 1);
 x5 = gaminv (p, 9, 0.5);
 x6 = gaminv (p, 7.5, 1);
 x7 = gaminv (p, 0.5, 1);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-y", p, x4, "-m", ...
       p, x5, "-k", p, x6, "-b", p, x7, "-c")
 ylim ([0, 20])
 grid on
 legend ({"α = 1, β = 2", "α = 2, β = 2", "α = 3, β = 2", ...
          "α = 5, β = 1", "α = 9, β = 0.5", "α = 7.5, β = 1", ...
          "α = 0.5, β = 1"}, "location", "northwest")
 title ("Gamma iCDF")
 xlabel ("probability")
 ylabel ("x")
***** shared p
 p = [-1 0 0.63212055882855778 1 2];
***** assert (gaminv (p, ones (1,5), ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (gaminv (p, 1, ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (gaminv (p, ones (1,5), 1), [NaN 0 1 Inf NaN], eps)
***** assert (gaminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
***** assert (gaminv (p, 1, [1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
***** assert (gaminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN 0 NaN Inf NaN])
***** assert (gaminv (1e-16, 1, 1), 1e-16, eps)
***** assert (gaminv (1e-16, 1, 2), 2e-16, eps)
***** assert (gaminv (1e-20, 3, 5), 1.957434012161815e-06, eps)
***** assert (gaminv (1e-15, 1, 1), 1e-15, eps)
***** assert (gaminv (1e-35, 1, 1), 1e-35, eps)
***** assert (gaminv ([p, NaN], 1, 1), [NaN 0 1 Inf NaN NaN], eps)
***** assert (gaminv (single ([p, NaN]), 1, 1), single ([NaN 0 1 Inf NaN NaN]), ...
 eps ("single"))
***** assert (gaminv ([p, NaN], single (1), 1), single ([NaN 0 1 Inf NaN NaN]), ...
 eps ("single"))
***** assert (gaminv ([p, NaN], 1, single (1)), single ([NaN 0 1 Inf NaN NaN]), ...
 eps ("single"))
***** error<gaminv: function called with too few input arguments.> gaminv ()
***** error<gaminv: function called with too few input arguments.> gaminv (1)
***** error<gaminv: function called with too few input arguments.> gaminv (1,2)
***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
 gaminv (ones (3), ones (2), ones (2))
***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
 gaminv (ones (2), ones (3), ones (2))
***** error<gaminv: P, Α, and Β must be of common size or scalars.> ...
 gaminv (ones (2), ones (2), ones (3))
***** error<gaminv: P, Α, and Β must not be complex.> gaminv (i, 2, 2)
***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, i, 2)
***** error<gaminv: P, Α, and Β must not be complex.> gaminv (2, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/geopdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/geopdf.m
***** demo
 ## Plot various PDFs from the geometric distribution
 x = 0:10;
 y1 = geopdf (x, 0.2);
 y2 = geopdf (x, 0.5);
 y3 = geopdf (x, 0.7);
 plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
 grid on
 ylim ([0, 0.8])
 legend ({"ps = 0.2", "ps = 0.5", "ps = 0.7"}, "location", "northeast")
 title ("Geometric PDF")
 xlabel ("values in x (number of failures)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 Inf];
 y = [0, 1/2, 1/4, NaN];
***** assert (geopdf (x, 0.5*ones (1,4)), y)
***** assert (geopdf (x, 0.5), y)
***** assert (geopdf (x, 0.5*[-1 NaN 4 1]), [NaN NaN NaN y(4)])
***** assert (geopdf ([x, NaN], 0.5), [y, NaN])
***** assert (geopdf (single ([x, NaN]), 0.5), single ([y, NaN]), 5*eps ("single"))
***** assert (geopdf ([x, NaN], single (0.5)), single ([y, NaN]), 5*eps ("single"))
***** error geopdf ()
***** error geopdf (1)
***** error geopdf (1,2,3)
***** error geopdf (ones (3), ones (2))
***** error geopdf (ones (2), ones (3))
***** error geopdf (i, 2)
***** error geopdf (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/logiinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/logiinv.m
***** demo
 ## Plot various iCDFs from the logistic distribution
 p = 0.001:0.001:0.999;
 x1 = logiinv (p, 5, 2);
 x2 = logiinv (p, 9, 3);
 x3 = logiinv (p, 9, 4);
 x4 = logiinv (p, 6, 2);
 x5 = logiinv (p, 2, 1);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
          "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
 title ("Logistic iCDF")
 xlabel ("probability")
 ylabel ("x")
***** test
 p = [0.01:0.01:0.99];
 assert (logiinv (p, 0, 1), log (p ./ (1-p)), 25*eps);
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (logiinv (p, 0, 1), [NaN -Inf 0 Inf NaN])
***** assert (logiinv (p, 0, [-1, 0, 1, 2, 3]), [NaN NaN 0 Inf NaN])
***** assert (logiinv ([p, NaN], 0, 1), [NaN -Inf 0 Inf NaN NaN])
***** assert (logiinv (single ([p, NaN]), 0, 1), single ([NaN -Inf 0 Inf NaN NaN]))
***** assert (logiinv ([p, NaN], single (0), 1), single ([NaN -Inf 0 Inf NaN NaN]))
***** assert (logiinv ([p, NaN], 0, single (1)), single ([NaN -Inf 0 Inf NaN NaN]))
***** error<logiinv: function called with too few input arguments.> logiinv ()
***** error<logiinv: function called with too few input arguments.> logiinv (1)
***** error<logiinv: function called with too few input arguments.> ...
 logiinv (1, 2)
***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
 logiinv (1, ones (2), ones (3))
***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
 logiinv (ones (2), 1, ones (3))
***** error<logiinv: P, MU, and SIGMA must be of common size or scalars.> ...
 logiinv (ones (2), ones (3), 1)
***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (i, 2, 3)
***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, i, 3)
***** error<logiinv: P, MU, and SIGMA must not be complex.> logiinv (1, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncx2cdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/ncx2cdf.m
***** demo
 ## Plot various CDFs from the noncentral chi-squared distribution
 x = 0:0.1:10;
 p1 = ncx2cdf (x, 2, 1);
 p2 = ncx2cdf (x, 2, 2);
 p3 = ncx2cdf (x, 2, 3);
 p4 = ncx2cdf (x, 4, 1);
 p5 = ncx2cdf (x, 4, 2);
 p6 = ncx2cdf (x, 4, 3);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", ...
       x, p4, "-m", x, p5, "-c", x, p6, "-y")
 grid on
 xlim ([0, 10])
 legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
          "df = 2, λ = 3", "df = 4, λ = 1", ...
          "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "southeast")
 title ("Noncentral chi-squared CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Compare the noncentral chi-squared CDF with LAMBDA = 2 to the
 ## chi-squared CDF with the same number of degrees of freedom (4).

 x = 0:0.1:10;
 p1 = ncx2cdf (x, 4, 2);
 p2 = chi2cdf (x, 4);
 plot (x, p1, "-", x, p2, "-")
 grid on
 xlim ([0, 10])
 legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
 title ("Noncentral chi-squared vs chi-squared CDFs")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = -2:0.1:2;
 p = ncx2cdf (x, 10, 1);
 assert (p([1:21]), zeros (1, 21), 3e-84);
 assert (p(22), 1.521400636466575e-09, 1e-14);
 assert (p(30), 6.665480510026046e-05, 1e-14);
 assert (p(41), 0.002406447308399836, 1e-14);
***** test
 p = ncx2cdf (12, 10, 3);
 assert (p, 0.4845555602398649, 1e-14);
***** test
 p = ncx2cdf (2, 3, 2);
 assert (p, 0.2207330870741212, 1e-14);
***** test
 p = ncx2cdf (2, 3, 2, "upper");
 assert (p, 0.7792669129258789, 1e-14);
***** test
 p = ncx2cdf ([3, 6], 3, 2, "upper");
 assert (p, [0.6423318186400054, 0.3152299878943012], 1e-14);
***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf ()
***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1)
***** error<ncx2cdf: function called with too few input arguments.> ncx2cdf (1, 2)
***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, "tail")
***** error<ncx2cdf: invalid argument for upper tail.> ncx2cdf (1, 2, 3, 4)
***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2cdf (ones (3), ones (2), ones (2))
***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2cdf (ones (2), ones (3), ones (2))
***** error<ncx2cdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2cdf (ones (2), ones (2), ones (3))
***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (i, 2, 2)
***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, i, 2)
***** error<ncx2cdf: X, DF, and LAMBDA must not be complex.> ncx2cdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/chi2inv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/chi2inv.m
***** demo
 ## Plot various iCDFs from the chi-squared distribution
 p = 0.001:0.001:0.999;
 x1 = chi2inv (p, 1);
 x2 = chi2inv (p, 2);
 x3 = chi2inv (p, 3);
 x4 = chi2inv (p, 4);
 x5 = chi2inv (p, 6);
 x6 = chi2inv (p, 9);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
       p, x4, "-c", p, x5, "-m", p, x6, "-y")
 grid on
 ylim ([0, 8])
 legend ({"df = 1", "df = 2", "df = 3", ...
          "df = 4", "df = 6", "df = 9"}, "location", "northwest")
 title ("Chi-squared iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.3934693402873666 1 2];
***** assert (chi2inv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], 5*eps)
***** assert (chi2inv (p, 2), [NaN 0 1 Inf NaN], 5*eps)
***** assert (chi2inv (p, 2*[0 1 NaN 1 1]), [NaN 0 NaN Inf NaN], 5*eps)
***** assert (chi2inv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], 5*eps)
***** assert (chi2inv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], 5*eps)
***** assert (chi2inv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
***** assert (chi2inv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), 5*eps ("single"))
***** error<chi2inv: function called with too few input arguments.> chi2inv ()
***** error<chi2inv: function called with too few input arguments.> chi2inv (1)
***** error<chi2inv: function called with too many inputs> chi2inv (1,2,3)
***** error<chi2inv: P and DF must be of common size or scalars.> ...
 chi2inv (ones (3), ones (2))
***** error<chi2inv: P and DF must be of common size or scalars.> ...
 chi2inv (ones (2), ones (3))
***** error<chi2inv: P and DF must not be complex.> chi2inv (i, 2)
***** error<chi2inv: P and DF must not be complex.> chi2inv (2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/finv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/finv.m
***** demo
 ## Plot various iCDFs from the F distribution
 p = 0.001:0.001:0.999;
 x1 = finv (p, 1, 1);
 x2 = finv (p, 2, 1);
 x3 = finv (p, 5, 2);
 x4 = finv (p, 10, 1);
 x5 = finv (p, 100, 100);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 ylim ([0, 4])
 legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
          "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
          "df1 = 100, df2 = 100"}, "location", "northwest")
 title ("F iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (finv (p, 2*ones (1,5), 2*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (finv (p, 2, 2*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (finv (p, 2*ones (1,5), 2), [NaN 0 1 Inf NaN])
***** assert (finv (p, [2 -Inf NaN Inf 2], 2), [NaN NaN NaN Inf NaN])
***** assert (finv (p, 2, [2 -Inf NaN Inf 2]), [NaN NaN NaN Inf NaN])
***** assert (finv ([p(1:2) NaN p(4:5)], 2, 2), [NaN 0 NaN Inf NaN])
***** assert (finv (0.025, 10, 1e6), 0.3247, 1e-4)
***** assert (finv (0.025, 10, 1e7), 0.3247, 1e-4)
***** assert (finv (0.025, 10, 1e10), 0.3247, 1e-4)
***** assert (finv (0.025, 10, 1e255), 0.3247, 1e-4)
***** assert (finv (0.025, 10, Inf), 0.3247, 1e-4)
***** test
 x = finv (0.35, Inf, 4);
 assert (x, 0.9014, 1e-4)
***** test
 x = finv (0, Inf, 4);
 assert (x, 0)
***** test
 x = finv (1, Inf, 4);
 assert (x, Inf)
***** test
 x = finv (0.35, 4, Inf);
 assert (x, 0.6175, 1e-4)
***** test
 x = finv (0, 4, Inf);
 assert (x, 0)
***** test
 x = finv (1, 4, Inf);
 assert (x, Inf)
***** test
 x = finv ([0, 0.000001, 0.35, 1, 1.2], Inf, Inf);
 assert (x, [0, 1, 1, 1, NaN]);
***** assert (finv ([p, NaN], 2, 2), [NaN 0 1 Inf NaN NaN])
***** assert (finv (single ([p, NaN]), 2, 2), single ([NaN 0 1 Inf NaN NaN]))
***** assert (finv ([p, NaN], single (2), 2), single ([NaN 0 1 Inf NaN NaN]))
***** assert (finv ([p, NaN], 2, single (2)), single ([NaN 0 1 Inf NaN NaN]))
***** error<finv: function called with too few input arguments.> finv ()
***** error<finv: function called with too few input arguments.> finv (1)
***** error<finv: function called with too few input arguments.> finv (1,2)
***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
 finv (ones (3), ones (2), ones (2))
***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
 finv (ones (2), ones (3), ones (2))
***** error<finv: P, DF1, and DF2 must be of common size or scalars.> ...
 finv (ones (2), ones (2), ones (3))
***** error<finv: P, DF1, and DF2 must not be complex.> finv (i, 2, 2)
***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, i, 2)
***** error<finv: P, DF1, and DF2 must not be complex.> finv (2, 2, i)
31 tests, 31 passed, 0 known failure, 0 skipped
[inst/dist_fun/burrrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/burrrnd.m
***** assert (size (burrrnd (1, 1, 1)), [1 1])
***** assert (size (burrrnd (ones (2,1), 1, 1)), [2, 1])
***** assert (size (burrrnd (ones (2,2), 1, 1)), [2, 2])
***** assert (size (burrrnd (1, ones (2,1), 1)), [2, 1])
***** assert (size (burrrnd (1, ones (2,2), 1)), [2, 2])
***** assert (size (burrrnd (1, 1, ones (2,1))), [2, 1])
***** assert (size (burrrnd (1, 1, ones (2,2))), [2, 2])
***** assert (size (burrrnd (1, 1, 1, 3)), [3, 3])
***** assert (size (burrrnd (1, 1, 1, [4 1])), [4, 1])
***** assert (size (burrrnd (1, 1, 1, 4, 1)), [4, 1])
***** assert (size (burrrnd (1, 1, 1, [])), [0, 0])
***** assert (size (burrrnd (1, 1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (burrrnd (1,1,1)), "double")
***** assert (class (burrrnd (single (1),1,1)), "single")
***** assert (class (burrrnd (single ([1 1]),1,1)), "single")
***** assert (class (burrrnd (1,single (1),1)), "single")
***** assert (class (burrrnd (1,single ([1 1]),1)), "single")
***** assert (class (burrrnd (1,1,single (1))), "single")
***** assert (class (burrrnd (1,1,single ([1 1]))), "single")
***** error<burrrnd: function called with too few input arguments.> burrrnd ()
***** error<burrrnd: function called with too few input arguments.> burrrnd (1)
***** error<burrrnd: function called with too few input arguments.> burrrnd (1, 2)
***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
 burrrnd (ones (3), ones (2), ones (2))
***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
 burrrnd (ones (2), ones (3), ones (2))
***** error<burrrnd: LAMBDA, C, and K must be of common size or scalars.> ...
 burrrnd (ones (2), ones (2), ones (3))
***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (i, 2, 3)
***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, i, 3)
***** error<burrrnd: LAMBDA, C, and K must not be complex.> burrrnd (1, 2, i)
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, -1)
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, 1.2)
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, ones (2))
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, [2 -1 2])
***** error<burrrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 burrrnd (1, 2, 3, [2 0 2.5])
***** error<burrrnd: dimensions must be non-negative integers.> ...
 burrrnd (1, 2, 3, 2, -1, 5)
***** error<burrrnd: dimensions must be non-negative integers.> ...
 burrrnd (1, 2, 3, 2, 1.5, 5)
***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
 burrrnd (2, ones (2), 2, 3)
***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
 burrrnd (2, ones (2), 2, [3, 2])
***** error<burrrnd: LAMBDA, C, and K must be scalars or of size SZ.> ...
 burrrnd (2, ones (2), 2, 3, 2)
38 tests, 38 passed, 0 known failure, 0 skipped
[inst/dist_fun/nctpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/nctpdf.m
***** demo
 ## Plot various PDFs from the noncentral T distribution
 x = -5:0.01:10;
 y1 = nctpdf (x, 1, 0);
 y2 = nctpdf (x, 4, 0);
 y3 = nctpdf (x, 1, 2);
 y4 = nctpdf (x, 4, 2);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", x, y4, "-m")
 grid on
 xlim ([-5, 10])
 ylim ([0, 0.4])
 legend ({"df = 1, μ = 0", "df = 4, μ = 0", ...
          "df = 1, μ = 2", "df = 4, μ = 2"}, "location", "northeast")
 title ("Noncentral T PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Compare the noncentral T PDF with MU = 1 to the T PDF
 ## with the same number of degrees of freedom (10).

 x = -5:0.1:5;
 y1 = nctpdf (x, 10, 1);
 y2 = tpdf (x, 10);
 plot (x, y1, "-", x, y2, "-");
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.4])
 legend ({"Noncentral χ^2(4,2)", "χ^2(4)"}, "location", "northwest")
 title ("Noncentral T vs T PDFs")
 xlabel ("values in x")
 ylabel ("density")
***** shared x1, df, mu
 x1 = [-Inf, 2, NaN, 4, Inf];
 df = [2, 0, -1, 1, 4];
 mu = [1, NaN, 3, -1, 2];
***** assert (nctpdf (x1, df, mu), [0, NaN, NaN, 0.00401787561306999, 0], 1e-14);
***** assert (nctpdf (x1, df, 1), [0, NaN, NaN, 0.0482312135423008, 0], 1e-14);
***** assert (nctpdf (x1, df, 3), [0, NaN, NaN, 0.1048493126401585, 0], 1e-14);
***** assert (nctpdf (x1, df, 2), [0, NaN, NaN, 0.08137377919890307, 0], 1e-14);
***** assert (nctpdf (x1, 3, mu), [0, NaN, NaN, 0.001185305171654381, 0], 1e-14);
***** assert (nctpdf (2, df, mu), [0.1791097459405861, NaN, NaN, ...
                             0.0146500727180389, 0.3082302682110299], 1e-14);
***** assert (nctpdf (4, df, mu), [0.04467929612254971, NaN, NaN, ...
                             0.00401787561306999, 0.0972086534042828], 1e-14);
***** error<nctpdf: function called with too few input arguments.> nctpdf ()
***** error<nctpdf: function called with too few input arguments.> nctpdf (1)
***** error<nctpdf: function called with too few input arguments.> nctpdf (1, 2)
***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
 nctpdf (ones (3), ones (2), ones (2))
***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
 nctpdf (ones (2), ones (3), ones (2))
***** error<nctpdf: X, DF, and MU must be of common size or scalars.> ...
 nctpdf (ones (2), ones (2), ones (3))
***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (i, 2, 2)
***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, i, 2)
***** error<nctpdf: X, DF, and MU must not be complex.> nctpdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/mnpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/mnpdf.m
***** test
 x = [1, 4, 2];
 pk = [0.2, 0.5, 0.3];
 y = mnpdf (x, pk);
 assert (y, 0.11812, 0.001);
***** test
 x = [1, 4, 2; 1, 0, 9];
 pk = [0.2, 0.5, 0.3; 0.1, 0.1, 0.8];
 y = mnpdf (x, pk);
 assert (y, [0.11812; 0.13422], 0.001);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/dist_fun/tinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/tinv.m
***** demo
 ## Plot various iCDFs from the Student's T distribution
 p = 0.001:0.001:0.999;
 x1 = tinv (p, 1);
 x2 = tinv (p, 2);
 x3 = tinv (p, 5);
 x4 = tinv (p, Inf);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-m")
 grid on
 xlim ([0, 1])
 ylim ([-5, 5])
 legend ({"df = 1", "df = 2", ...
          "df = 5", 'df = \infty'}, "location", "northwest")
 title ("Student's T iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (tinv (p, ones (1,5)), [NaN -Inf 0 Inf NaN])
***** assert (tinv (p, 1), [NaN -Inf 0 Inf NaN], eps)
***** assert (tinv (p, [1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
***** assert (tinv ([p(1:2) NaN p(4:5)], 1), [NaN -Inf NaN Inf NaN])
***** assert (tinv ([p, NaN], 1), [NaN -Inf 0 Inf NaN NaN], eps)
***** assert (tinv (single ([p, NaN]), 1), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
***** assert (tinv ([p, NaN], single (1)), single ([NaN -Inf 0 Inf NaN NaN]), eps ("single"))
***** error<tinv: function called with too few input arguments.> tinv ()
***** error<tinv: function called with too few input arguments.> tinv (1)
***** error<tinv: P and DF must be of common size or scalars.> ...
 tinv (ones (3), ones (2))
***** error<tinv: P and DF must be of common size or scalars.> ...
 tinv (ones (2), ones (3))
***** error<tinv: P and DF must not be complex.> tinv (i, 2)
***** error<tinv: P and DF must not be complex.> tinv (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/logipdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/logipdf.m
***** demo
 ## Plot various PDFs from the logistic distribution
 x = -5:0.01:20;
 y1 = logipdf (x, 5, 2);
 y2 = logipdf (x, 9, 3);
 y3 = logipdf (x, 9, 4);
 y4 = logipdf (x, 6, 2);
 y5 = logipdf (x, 2, 1);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 0.3])
 legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
          "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "northeast")
 title ("Logistic PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-Inf -log(4) 0 log(4) Inf];
 y = [0, 0.16, 1/4, 0.16, 0];
***** assert (logipdf ([x, NaN], 0, 1), [y, NaN], eps)
***** assert (logipdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), y([4:5])], eps)
***** assert (logipdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
***** assert (logipdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
***** assert (logipdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
***** error<logipdf: function called with too few input arguments.> logipdf ()
***** error<logipdf: function called with too few input arguments.> logipdf (1)
***** error<logipdf: function called with too few input arguments.> ...
 logipdf (1, 2)
***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logipdf (1, ones (2), ones (3))
***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logipdf (ones (2), 1, ones (3))
***** error<logipdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logipdf (ones (2), ones (3), 1)
***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (i, 2, 3)
***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, i, 3)
***** error<logipdf: X, MU, and SIGMA must not be complex.> logipdf (1, 2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/copulapdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/copulapdf.m
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 theta = [1; 2];
 y = copulapdf ("Clayton", x, theta);
 expected_p = [0.9872; 0.7295];
 assert (y, expected_p, 0.001);
***** test
 x = [0.2:0.2:0.6; 0.2:0.2:0.6];
 y = copulapdf ("Gumbel", x, 2);
 expected_p = [0.9468; 0.9468];
 assert (y, expected_p, 0.001);
***** test
 x = [0.2, 0.6; 0.2, 0.6];
 theta = [1; 2];
 y = copulapdf ("Frank", x, theta);
 expected_p = [0.9378; 0.8678];
 assert (y, expected_p, 0.001);
***** test
 x = [0.2, 0.6; 0.2, 0.6];
 theta = [0.3; 0.7];
 y = copulapdf ("AMH", x, theta);
 expected_p = [0.9540; 0.8577];
 assert (y, expected_p, 0.001);
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/dist_fun/chi2pdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/chi2pdf.m
***** demo
 ## Plot various PDFs from the chi-squared distribution
 x = 0:0.01:8;
 y1 = chi2pdf (x, 1);
 y2 = chi2pdf (x, 2);
 y3 = chi2pdf (x, 3);
 y4 = chi2pdf (x, 4);
 y5 = chi2pdf (x, 6);
 y6 = chi2pdf (x, 9);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
       x, y4, "-c", x, y5, "-m", x, y6, "-y")
 grid on
 xlim ([0, 8])
 ylim ([0, 0.5])
 legend ({"df = 1", "df = 2", "df = 3", ...
          "df = 4", "df = 6", "df = 9"}, "location", "northeast")
 title ("Chi-squared PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 Inf];
 y = [0, 1/2 * exp(-x(2:5)/2)];
***** assert (chi2pdf (x, 2*ones (1,5)), y)
***** assert (chi2pdf (x, 2), y)
***** assert (chi2pdf (x, 2*[1 0 NaN 1 1]), [y(1) NaN NaN y(4:5)])
***** assert (chi2pdf ([x, NaN], 2), [y, NaN])
***** assert (chi2pdf (2, Inf), 0)
***** assert (chi2pdf (single ([x, NaN]), 2), single ([y, NaN]))
***** assert (chi2pdf ([x, NaN], single (2)), single ([y, NaN]))
***** error<chi2pdf: function called with too few input arguments.> chi2pdf ()
***** error<chi2pdf: function called with too few input arguments.> chi2pdf (1)
***** error<chi2pdf: function called with too many inputs> chi2pdf (1,2,3)
***** error<chi2pdf: X and DF must be of common size or scalars.> ...
 chi2pdf (ones (3), ones (2))
***** error<chi2pdf: X and DF must be of common size or scalars.> ...
 chi2pdf (ones (2), ones (3))
***** error<chi2pdf: X and DF must not be complex.> chi2pdf (i, 2)
***** error<chi2pdf: X and DF must not be complex.> chi2pdf (2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/betainv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/betainv.m
***** demo
 ## Plot various iCDFs from the Beta distribution
 p = 0.001:0.001:0.999;
 x1 = betainv (p, 0.5, 0.5);
 x2 = betainv (p, 5, 1);
 x3 = betainv (p, 1, 3);
 x4 = betainv (p, 2, 2);
 x5 = betainv (p, 2, 5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c", p, x5, "-m")
 grid on
 legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
          "α = 2, β = 2", "α = 2, β = 5"}, "location", "southeast")
 title ("Beta iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.75 1 2];
***** assert (betainv (p, ones (1,5), 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
***** assert (betainv (p, 1, 2*ones (1,5)), [NaN 0 0.5 1 NaN], eps)
***** assert (betainv (p, ones (1,5), 2), [NaN 0 0.5 1 NaN], eps)
***** assert (betainv (p, [1 0 NaN 1 1], 2), [NaN NaN NaN 1 NaN])
***** assert (betainv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 1 NaN])
***** assert (betainv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN 1 NaN])
***** assert (betainv ([p, NaN], 1, 2), [NaN 0 0.5 1 NaN NaN], eps)
***** assert (betainv (single ([p, NaN]), 1, 2), single ([NaN 0 0.5 1 NaN NaN]))
***** assert (betainv ([p, NaN], single (1), 2), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
***** assert (betainv ([p, NaN], 1, single (2)), single ([NaN 0 0.5 1 NaN NaN]), eps("single"))
***** error<betainv: function called with too few input arguments.> betainv ()
***** error<betainv: function called with too few input arguments.> betainv (1)
***** error<betainv: function called with too few input arguments.> betainv (1,2)
***** error<betainv: function called with too many inputs> betainv (1,2,3,4)
***** error<betainv: P, A, and B must be of common size or scalars.> ...
 betainv (ones (3), ones (2), ones (2))
***** error<betainv: P, A, and B must be of common size or scalars.> ...
 betainv (ones (2), ones (3), ones (2))
***** error<betainv: P, A, and B must be of common size or scalars.> ...
 betainv (ones (2), ones (2), ones (3))
***** error<betainv: P, A, and B must not be complex.> betainv (i, 2, 2)
***** error<betainv: P, A, and B must not be complex.> betainv (2, i, 2)
***** error<betainv: P, A, and B must not be complex.> betainv (2, 2, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/expcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/expcdf.m
***** demo
 ## Plot various CDFs from the exponential distribution
 x = 0:0.01:5;
 p1 = expcdf (x, 2/3);
 p2 = expcdf (x, 1.0);
 p3 = expcdf (x, 2.0);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
 grid on
 legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "southeast")
 title ("Exponential CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p
 x = [-1 0 0.5 1 Inf];
 p = [0, 1 - exp(-x(2:end)/2)];
***** assert (expcdf (x, 2 * ones (1, 5)), p, 1e-16)
***** assert (expcdf (x, 2), p, 1e-16)
***** assert (expcdf (x, 2 * [1, 0, NaN, 1, 1]), [0, NaN, NaN, p(4:5)], 1e-16)
***** assert (expcdf ([x, NaN], 2), [p, NaN], 1e-16)
***** assert (expcdf (single ([x, NaN]), 2), single ([p, NaN]))
***** assert (expcdf ([x, NaN], single (2)), single ([p, NaN]))
***** test
 [p, plo, pup] = expcdf (1, 2, 3);
 assert (p, 0.39346934028737, 1e-14);
 assert (plo, 0.08751307220484, 1e-14);
 assert (pup, 0.93476821257933, 1e-14);
***** test
 [p, plo, pup] = expcdf (1, 2, 2, 0.1);
 assert (p, 0.39346934028737, 1e-14);
 assert (plo, 0.14466318041675, 1e-14);
 assert (pup, 0.79808291849140, 1e-14);
***** test
 [p, plo, pup] = expcdf (1, 2, 2, 0.1, "upper");
 assert (p, 0.60653065971263, 1e-14);
 assert (plo, 0.20191708150860, 1e-14);
 assert (pup, 0.85533681958325, 1e-14);
***** error<expcdf: invalid number of input arguments.> expcdf ()
***** error<expcdf: invalid number of input arguments.> expcdf (1, 2 ,3 ,4 ,5, 6)
***** error<expcdf: invalid argument for upper tail.> expcdf (1, 2, 3, 4, "uper")
***** error<expcdf: X and MU must be of common size or scalars.> ...
 expcdf (ones (3), ones (2))
***** error<expcdf: invalid size of variance, PCOV must be a scalar.> ...
 expcdf (2, 3, [1, 2])
***** error<expcdf: variance, PCOV, is required for confidence bounds.> ...
 [p, plo, pup] = expcdf (1, 2)
***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
 expcdf (1, 2, 3, 0)
***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
 expcdf (1, 2, 3, 1.22)
***** error<expcdf: invalid value for alpha.> [p, plo, pup] = ...
 expcdf (1, 2, 3, "alpha", "upper")
***** error<expcdf: X and MU must not be complex.> expcdf (i, 2)
***** error<expcdf: X and MU must not be complex.> expcdf (2, i)
***** error<expcdf: variance, PCOV, cannot be negative.> ...
 [p, plo, pup] = expcdf (1, 2, -1, 0.04)
21 tests, 21 passed, 0 known failure, 0 skipped
[inst/dist_fun/triinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/triinv.m
***** demo
 ## Plot various iCDFs from the triangular distribution
 p = 0.001:0.001:0.999;
 x1 = triinv (p, 3, 6, 4);
 x2 = triinv (p, 1, 5, 2);
 x3 = triinv (p, 2, 9, 3);
 x4 = triinv (p, 2, 9, 5);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([0, 10])
 legend ({"a = 3, b = 6, c = 4", "a = 1, b = 5, c = 2", ...
          "a = 2, b = 9, c = 3", "a = 2, b = 9, c = 5"}, ...
         "location", "northwest")
 title ("Triangular CDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y
 p = [-1, 0, 0.02, 0.5, 0.98, 1, 2];
 y = [NaN, 0, 0.1, 0.5, 0.9, 1, NaN] + 1;
***** assert (triinv (p, ones (1, 7), 1.5 * ones (1, 7), 2 * ones (1, 7)), y, eps)
***** assert (triinv (p, 1 * ones (1, 7), 1.5, 2), y, eps)
***** assert (triinv (p, 1, 1.5, 2 * ones (1, 7)), y, eps)
***** assert (triinv (p, 1, 1.5*ones (1,7), 2), y, eps)
***** assert (triinv (p, 1, 1.5, 2), y, eps)
***** assert (triinv (p, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], eps)
***** assert (triinv (p, 1, 1.5 * [1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], eps)
***** assert (triinv (p, 1, 1.5, 2 * [1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], eps)
***** assert (triinv ([p, NaN], 1, 1.5, 2), [y, NaN], eps)
***** assert (triinv (single ([p, NaN]), 1, 1.5, 2), single ([y, NaN]), eps('single'))
***** assert (triinv ([p, NaN], single (1), 1.5, 2), single ([y, NaN]), eps('single'))
***** assert (triinv ([p, NaN], 1, single (1.5), 2), single ([y, NaN]), eps('single'))
***** assert (triinv ([p, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps('single'))
***** error<triinv: function called with too few input arguments.> triinv ()
***** error<triinv: function called with too few input arguments.> triinv (1)
***** error<triinv: function called with too few input arguments.> triinv (1, 2)
***** error<triinv: function called with too few input arguments.> triinv (1, 2, 3)
***** error<triinv: function called with too many inputs> ...
 triinv (1, 2, 3, 4, 5)
***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
 triinv (ones (3), ones (2), ones(2), ones(2))
***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
 triinv (ones (2), ones (3), ones(2), ones(2))
***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
 triinv (ones (2), ones (2), ones(3), ones(2))
***** error<triinv: P, A, B, and C must be of common size or scalars.> ...
 triinv (ones (2), ones (2), ones(2), ones(3))
***** error<triinv: P, A, B, and C must not be complex.> triinv (i, 2, 3, 4)
***** error<triinv: P, A, B, and C must not be complex.> triinv (1, i, 3, 4)
***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, i, 4)
***** error<triinv: P, A, B, and C must not be complex.> triinv (1, 2, 3, i)
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/dist_fun/logninv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/logninv.m
***** demo
 ## Plot various iCDFs from the log-normal distribution
 p = 0.001:0.001:0.999;
 x1 = logninv (p, 0, 1);
 x2 = logninv (p, 0, 0.5);
 x3 = logninv (p, 0, 0.25);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 3])
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
         "location", "northwest")
 title ("Log-normal iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (logninv (p, ones (1,5), ones (1,5)), [NaN 0 e Inf NaN], 2*eps)
***** assert (logninv (p, 1, ones (1,5)), [NaN 0 e Inf NaN], 2*eps)
***** assert (logninv (p, ones (1,5), 1), [NaN 0 e Inf NaN], 2*eps)
***** assert (logninv (p, [1 1 NaN 0 1], 1), [NaN 0 NaN Inf NaN])
***** assert (logninv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (logninv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 0 NaN Inf NaN])
***** assert (logninv ([p, NaN], 1, 1), [NaN 0 e Inf NaN NaN], 2*eps)
***** assert (logninv (single ([p, NaN]), 1, 1), single ([NaN 0 e Inf NaN NaN]))
***** assert (logninv ([p, NaN], single (1), 1), single ([NaN 0 e Inf NaN NaN]))
***** assert (logninv ([p, NaN], 1, single (1)), single ([NaN 0 e Inf NaN NaN]))
***** error logninv ()
***** error logninv (1,2,3,4)
***** error logninv (ones (3), ones (2), ones (2))
***** error logninv (ones (2), ones (3), ones (2))
***** error logninv (ones (2), ones (2), ones (3))
***** error logninv (i, 2, 2)
***** error logninv (2, i, 2)
***** error logninv (2, 2, i)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/unifinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/unifinv.m
***** demo
 ## Plot various iCDFs from the continuous uniform distribution
 p = 0.001:0.001:0.999;
 x1 = unifinv (p, 2, 5);
 x2 = unifinv (p, 3, 9);
 plot (p, x1, "-b", p, x2, "-g")
 grid on
 xlim ([0, 1])
 ylim ([0, 10])
 legend ({"a = 2, b = 5", "a = 3, b = 9"}, "location", "northwest")
 title ("Continuous uniform iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (unifinv (p, ones (1,5), 2*ones (1,5)), [NaN 1 1.5 2 NaN])
***** assert (unifinv (p, 0, 1), [NaN 1 1.5 2 NaN] - 1)
***** assert (unifinv (p, 1, 2*ones (1,5)), [NaN 1 1.5 2 NaN])
***** assert (unifinv (p, ones (1,5), 2), [NaN 1 1.5 2 NaN])
***** assert (unifinv (p, [1 2 NaN 1 1], 2), [NaN NaN NaN 2 NaN])
***** assert (unifinv (p, 1, 2*[1 0 NaN 1 1]), [NaN NaN NaN 2 NaN])
***** assert (unifinv ([p(1:2) NaN p(4:5)], 1, 2), [NaN 1 NaN 2 NaN])
***** assert (unifinv ([p, NaN], 1, 2), [NaN 1 1.5 2 NaN NaN])
***** assert (unifinv (single ([p, NaN]), 1, 2), single ([NaN 1 1.5 2 NaN NaN]))
***** assert (unifinv ([p, NaN], single (1), 2), single ([NaN 1 1.5 2 NaN NaN]))
***** assert (unifinv ([p, NaN], 1, single (2)), single ([NaN 1 1.5 2 NaN NaN]))
***** error<unifinv: function called with too few input arguments.> unifinv ()
***** error<unifinv: function called with too few input arguments.> unifinv (1, 2)
***** error<unifinv: P, A, and B must be of common size or scalars.> ...
 unifinv (ones (3), ones (2), ones (2))
***** error<unifinv: P, A, and B must be of common size or scalars.> ...
 unifinv (ones (2), ones (3), ones (2))
***** error<unifinv: P, A, and B must be of common size or scalars.> ...
 unifinv (ones (2), ones (2), ones (3))
***** error<unifinv: P, A, and B must not be complex.> unifinv (i, 2, 2)
***** error<unifinv: P, A, and B must not be complex.> unifinv (2, i, 2)
***** error<unifinv: P, A, and B must not be complex.> unifinv (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/expinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/expinv.m
***** demo
 ## Plot various iCDFs from the exponential distribution
 p = 0.001:0.001:0.999;
 x1 = expinv (p, 2/3);
 x2 = expinv (p, 1.0);
 x3 = expinv (p, 2.0);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r")
 grid on
 ylim ([0, 5])
 legend ({"μ = 2/3", "μ = 1", "μ = 2"}, "location", "northwest")
 title ("Exponential iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.3934693402873666 1 2];
***** assert (expinv (p, 2*ones (1,5)), [NaN 0 1 Inf NaN], eps)
***** assert (expinv (p, 2), [NaN 0 1 Inf NaN], eps)
***** assert (expinv (p, 2*[1 0 NaN 1 1]), [NaN NaN NaN Inf NaN], eps)
***** assert (expinv ([p(1:2) NaN p(4:5)], 2), [NaN 0 NaN Inf NaN], eps)
***** assert (expinv ([p, NaN], 2), [NaN 0 1 Inf NaN NaN], eps)
***** assert (expinv (single ([p, NaN]), 2), single ([NaN 0 1 Inf NaN NaN]), eps)
***** assert (expinv ([p, NaN], single (2)), single ([NaN 0 1 Inf NaN NaN]), eps)
***** error<expinv: invalid number of input arguments.> expinv ()
***** error<expinv: invalid number of input arguments.> expinv (1, 2 ,3 ,4 ,5)
***** error<expinv: P and MU must be of common size or scalars.> ...
 expinv (ones (3), ones (2))
***** error<expinv: invalid size of variance, PCOV must be a scalar.> ...
 expinv (2, 3, [1, 2])
***** error<expinv: variance, PCOV, is required for confidence bounds.> ...
 [x, xlo, xup] = expinv (1, 2)
***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
 expinv (1, 2, 3, 0)
***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
 expinv (1, 2, 3, 1.22)
***** error<expinv: invalid value for alpha.> [x, xlo, xup] = ...
 expinv (1, 2, 3, [0.05, 0.1])
***** error<expinv: P and MU must not be complex.> expinv (i, 2)
***** error<expinv: P and MU must not be complex.> expinv (2, i)
***** error<expinv: variance, PCOV, cannot be negative.> ...
 [x, xlo, xup] = expinv (1, 2, -1, 0.04)
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fun/iwishpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/iwishpdf.m
***** assert(iwishpdf(4, 3, 3.1), 0.04226595, 1E-7);
***** assert(iwishpdf([2 -0.3;-0.3 4], [1 0.3;0.3 1], 4), 1.60166e-05, 1E-10);
***** assert(iwishpdf([6 2 5; 2 10 -5; 5 -5 25], ...
 [9 5 5; 5 10 -8; 5 -8 22], 5.1), 4.946831e-12, 1E-17);
***** error iwishpdf ()
***** error iwishpdf (1, 2)
***** error iwishpdf (1, 2, 0)
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/dist_fun/gevpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/gevpdf.m
***** demo
 ## Plot various PDFs from the generalized extreme value distribution
 x = -1:0.001:10;
 y1 = gevpdf (x, 1, 1, 1);
 y2 = gevpdf (x, 0.5, 1, 1);
 y3 = gevpdf (x, 1, 1, 5);
 y4 = gevpdf (x, 1, 2, 5);
 y5 = gevpdf (x, 1, 5, 5);
 y6 = gevpdf (x, 1, 0.5, 5);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", ...
       x, y4, "-c", x, y5, "-m", x, y6, "-k")
 grid on
 xlim ([-1, 10])
 ylim ([0, 1.1])
 legend ({"k = 1, σ = 1, μ = 1", "k = 0.5, σ = 1, μ = 1", ...
          "k = 1, σ = 1, μ = 5", "k = 1, σ = 2, μ = 5", ...
          "k = 1, σ = 5, μ = 5", "k = 1, σ = 0.5, μ = 5"}, ...
         "location", "northeast")
 title ("Generalized extreme value PDF")
 xlabel ("values in x")
 ylabel ("density")
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 k = 1;
 mu = 0;
 y = gevpdf (x, k, sigma, mu);
 expected_y = [0.367879   0.143785   0.088569   0.063898   0.049953   0.040997];
 assert (y, expected_y, 0.001);
***** test
 x = -0.5:0.5:2.5;
 sigma = 0.5;
 k = 1;
 mu = 0;
 y = gevpdf (x, k, sigma, mu);
 expected_y = [0 0.735759   0.303265   0.159229   0.097350   0.065498   0.047027];
 assert (y, expected_y, 0.001);
***** test # check for continuity for k near 0
 x = 1;
 sigma = 0.5;
 k = -0.03:0.01:0.03;
 mu = 0;
 y = gevpdf (x, k, sigma, mu);
 expected_y = [0.23820   0.23764   0.23704   0.23641   0.23576   0.23508   0.23438];
 assert (y, expected_y, 0.001);
***** error<gevpdf: function called with too few input arguments.> gevpdf ()
***** error<gevpdf: function called with too few input arguments.> gevpdf (1)
***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2)
***** error<gevpdf: function called with too few input arguments.> gevpdf (1, 2, 3)
***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevpdf (ones (3), ones (2), ones(2), ones(2))
***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevpdf (ones (2), ones (3), ones(2), ones(2))
***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevpdf (ones (2), ones (2), ones(3), ones(2))
***** error<gevpdf: X, K, SIGMA, and MU must be of common size or scalars.> ...
 gevpdf (ones (2), ones (2), ones(2), ones(3))
***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (i, 2, 3, 4)
***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, i, 3, 4)
***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, i, 4)
***** error<gevpdf: X, K, SIGMA, and MU must not be complex.> gevpdf (1, 2, 3, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/tcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/tcdf.m
***** demo
 ## Plot various CDFs from the Student's T distribution
 x = -5:0.01:5;
 p1 = tcdf (x, 1);
 p2 = tcdf (x, 2);
 p3 = tcdf (x, 5);
 p4 = tcdf (x, Inf);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-m")
 grid on
 xlim ([-5, 5])
 ylim ([0, 1])
 legend ({"df = 1", "df = 2", ...
          "df = 5", 'df = \infty'}, "location", "southeast")
 title ("Student's T CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x,y
 x = [-Inf 0 1 Inf];
 y = [0 1/2 3/4 1];
***** assert (tcdf (x, ones (1,4)), y, eps)
***** assert (tcdf (x, 1), y, eps)
***** assert (tcdf (x, [0 1 NaN 1]), [NaN 1/2 NaN 1], eps)
***** assert (tcdf ([x(1:2) NaN x(4)], 1), [y(1:2) NaN y(4)], eps)
***** assert (tcdf (2, 3, "upper"), 0.0697, 1e-4)
***** assert (tcdf (205, 5, "upper"), 2.6206e-11, 1e-14)
***** assert (tcdf ([x, NaN], 1), [y, NaN], eps)
***** assert (tcdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
***** assert (tcdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
***** error<tcdf: function called with too few input arguments.> tcdf ()
***** error<tcdf: function called with too few input arguments.> tcdf (1)
***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, "uper")
***** error<tcdf: invalid argument for upper tail.> tcdf (1, 2, 3)
***** error<tcdf: X and DF must be of common size or scalars.> ...
 tcdf (ones (3), ones (2))
***** error<tcdf: X and DF must be of common size or scalars.> ...
 tcdf (ones (3), ones (2))
***** error<tcdf: X and DF must be of common size or scalars.> ...
 tcdf (ones (3), ones (2), "upper")
***** error<tcdf: X and DF must not be complex.> tcdf (i, 2)
***** error<tcdf: X and DF must not be complex.> tcdf (2, i)
***** shared tol_rel
 tol_rel = 10 * eps;
***** assert (tcdf (10^(-10), 2.5), 0.50000000003618087, -tol_rel)
***** assert (tcdf (10^(-11), 2.5), 0.50000000000361809, -tol_rel)
***** assert (tcdf (10^(-12), 2.5), 0.50000000000036181, -tol_rel)
***** assert (tcdf (10^(-13), 2.5), 0.50000000000003618, -tol_rel)
***** assert (tcdf (10^(-14), 2.5), 0.50000000000000362, -tol_rel)
***** assert (tcdf (10^(-15), 2.5), 0.50000000000000036, -tol_rel)
***** assert (tcdf (10^(-16), 2.5), 0.50000000000000004, -tol_rel)
***** assert (tcdf (-10^1, 2.5), 2.2207478836537124e-03, -tol_rel)
***** assert (tcdf (-10^2, 2.5), 7.1916492116661878e-06, -tol_rel)
***** assert (tcdf (-10^3, 2.5), 2.2747463948307452e-08, -tol_rel)
***** assert (tcdf (-10^4, 2.5), 7.1933970159922115e-11, -tol_rel)
***** assert (tcdf (-10^5, 2.5), 2.2747519231756221e-13, -tol_rel)
30 tests, 30 passed, 0 known failure, 0 skipped
[inst/dist_fun/nbininv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/nbininv.m
***** demo
 ## Plot various iCDFs from the negative binomial distribution
 p = 0.001:0.001:0.999;
 x1 = nbininv (p, 2, 0.15);
 x2 = nbininv (p, 5, 0.2);
 x3 = nbininv (p, 4, 0.4);
 x4 = nbininv (p, 10, 0.3);
 plot (p, x1, "-r", p, x2, "-g", p, x3, "-k", p, x4, "-m")
 grid on
 ylim ([0, 40])
 legend ({"r = 2, ps = 0.15", "r = 5, ps = 0.2", "r = 4, p = 0.4", ...
          "r = 10, ps = 0.3"}, "location", "northwest")
 title ("Negative binomial iCDF")
 xlabel ("probability")
 ylabel ("values in x (number of failures)")
***** shared p
 p = [-1 0 3/4 1 2];
***** assert (nbininv (p, ones (1,5), 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (nbininv (p, 1, 0.5*ones (1,5)), [NaN 0 1 Inf NaN])
***** assert (nbininv (p, ones (1,5), 0.5), [NaN 0 1 Inf NaN])
***** assert (nbininv (p, [1 0 NaN Inf 1], 0.5), [NaN NaN NaN NaN NaN])
***** assert (nbininv (p, [1 0 1.5 Inf 1], 0.5), [NaN NaN 2 NaN NaN])
***** assert (nbininv (p, 1, 0.5*[1 -Inf NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (nbininv ([p(1:2) NaN p(4:5)], 1, 0.5), [NaN 0 NaN Inf NaN])
***** assert (nbininv ([p, NaN], 1, 0.5), [NaN 0 1 Inf NaN NaN])
***** assert (nbininv (single ([p, NaN]), 1, 0.5), single ([NaN 0 1 Inf NaN NaN]))
***** assert (nbininv ([p, NaN], single (1), 0.5), single ([NaN 0 1 Inf NaN NaN]))
***** assert (nbininv ([p, NaN], 1, single (0.5)), single ([NaN 0 1 Inf NaN NaN]))
***** shared y, tol
 y = magic (3) + 1;
 tol = 1;
***** assert (nbininv (nbincdf (1:10, 3, 0.1), 3, 0.1), 1:10, tol)
***** assert (nbininv (nbincdf (1:10, 3./(1:10), 0.1), 3./(1:10), 0.1), 1:10, tol)
***** assert (nbininv (nbincdf (y, 3./y, 1./y), 3./y, 1./y), y, tol)
***** error<nbininv: function called with too few input arguments.> nbininv ()
***** error<nbininv: function called with too few input arguments.> nbininv (1)
***** error<nbininv: function called with too few input arguments.> nbininv (1, 2)
***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
 nbininv (ones (3), ones (2), ones (2))
***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
 nbininv (ones (2), ones (3), ones (2))
***** error<nbininv: P, R, and PS must be of common size or scalars.> ...
 nbininv (ones (2), ones (2), ones (3))
***** error<nbininv: P, R, and PS must not be complex.> nbininv (i, 2, 2)
***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, i, 2)
***** error<nbininv: P, R, and PS must not be complex.> nbininv (2, 2, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/unifrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/unifrnd.m
***** assert (size (unifrnd (1, 1)), [1 1])
***** assert (size (unifrnd (1, ones (2,1))), [2, 1])
***** assert (size (unifrnd (1, ones (2,2))), [2, 2])
***** assert (size (unifrnd (ones (2,1), 1)), [2, 1])
***** assert (size (unifrnd (ones (2,2), 1)), [2, 2])
***** assert (size (unifrnd (1, 1, 3)), [3, 3])
***** assert (size (unifrnd (1, 1, [4, 1])), [4, 1])
***** assert (size (unifrnd (1, 1, 4, 1)), [4, 1])
***** assert (size (unifrnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (unifrnd (1, 1, 0, 1)), [0, 1])
***** assert (size (unifrnd (1, 1, 1, 0)), [1, 0])
***** assert (size (unifrnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (unifrnd (1, 1, [])), [0, 0])
***** assert (size (unifrnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (unifrnd (1, 1)), "double")
***** assert (class (unifrnd (1, single (1))), "single")
***** assert (class (unifrnd (1, single ([1, 1]))), "single")
***** assert (class (unifrnd (single (1), 1)), "single")
***** assert (class (unifrnd (single ([1, 1]), 1)), "single")
***** error<unifrnd: function called with too few input arguments.> unifrnd ()
***** error<unifrnd: function called with too few input arguments.> unifrnd (1)
***** error<unifrnd: A and B must be of common size or scalars.> ...
 unifrnd (ones (3), ones (2))
***** error<unifrnd: A and B must be of common size or scalars.> ...
 unifrnd (ones (2), ones (3))
***** error<unifrnd: A and B must not be complex.> unifrnd (i, 2, 3)
***** error<unifrnd: A and B must not be complex.> unifrnd (1, i, 3)
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, -1)
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, 1.2)
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, ones (2))
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, [2 -1 2])
***** error<unifrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 unifrnd (1, 2, [2 0 2.5])
***** error<unifrnd: dimensions must be non-negative integers.> ...
 unifrnd (1, 2, 2, -1, 5)
***** error<unifrnd: dimensions must be non-negative integers.> ...
 unifrnd (1, 2, 2, 1.5, 5)
***** error<unifrnd: A and B must be scalars or of size SZ.> ...
 unifrnd (2, ones (2), 3)
***** error<unifrnd: A and B must be scalars or of size SZ.> ...
 unifrnd (2, ones (2), [3, 2])
***** error<unifrnd: A and B must be scalars or of size SZ.> ...
 unifrnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/logncdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/logncdf.m
***** demo
 ## Plot various CDFs from the log-normal distribution
 x = 0:0.01:3;
 p1 = logncdf (x, 0, 1);
 p2 = logncdf (x, 0, 0.5);
 p3 = logncdf (x, 0, 0.25);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r")
 grid on
 legend ({"μ = 0, σ = 1", "μ = 0, σ = 0.5", "μ = 0, σ = 0.25"}, ...
         "location", "southeast")
 title ("Log-normal CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1, 0, 1, e, Inf];
 y = [0, 0, 0.5, 1/2+1/2*erf(1/2), 1];
***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5)), y, eps)
***** assert (logncdf (x, zeros (1,5), sqrt(2)*ones (1,5), []), y, eps)
***** assert (logncdf (x, 0, sqrt(2)*ones (1,5)), y, eps)
***** assert (logncdf (x, zeros (1,5), sqrt(2)), y, eps)
***** assert (logncdf (x, [0 1 NaN 0 1], sqrt(2)), [0 0 NaN y(4:5)], eps)
***** assert (logncdf (x, 0, sqrt(2)*[0 NaN Inf 1 1]), [NaN NaN y(3:5)], eps)
***** assert (logncdf ([x(1:3) NaN x(5)], 0, sqrt(2)), [y(1:3) NaN y(5)], eps)
***** assert (logncdf ([x, NaN], 0, sqrt(2)), [y, NaN], eps)
***** assert (logncdf (single ([x, NaN]), 0, sqrt(2)), single ([y, NaN]), eps ("single"))
***** assert (logncdf ([x, NaN], single (0), sqrt(2)), single ([y, NaN]), eps ("single"))
***** assert (logncdf ([x, NaN], 0, single (sqrt(2))), single ([y, NaN]), eps ("single"))
***** error<logncdf: invalid number of input arguments.> logncdf ()
***** error<logncdf: invalid number of input arguments.> logncdf (1,2,3,4,5,6,7)
***** error<logncdf: invalid argument for upper tail.> logncdf (1, 2, 3, 4, "uper")
***** error<logncdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logncdf (ones (3), ones (2), ones (2))
***** error<logncdf: invalid size of covariance matrix.> logncdf (2, 3, 4, [1, 2])
***** error<logncdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = logncdf (1, 2, 3)
***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
 logncdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
 logncdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<logncdf: invalid value for alpha.> [p, plo, pup] = ...
 logncdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (i, 2, 2)
***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, i, 2)
***** error<logncdf: X, MU, and SIGMA must not be complex.> logncdf (2, 2, i)
***** error<logncdf: bad covariance matrix.> ...
 [p, plo, pup] =logncdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/unidpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/unidpdf.m
***** demo
 ## Plot various PDFs from the discrete uniform distribution
 x = 0:10;
 y1 = unidpdf (x, 5);
 y2 = unidpdf (x, 9);
 plot (x, y1, "*b", x, y2, "*g")
 grid on
 xlim ([0, 10])
 ylim ([0, 0.25])
 legend ({"N = 5", "N = 9"}, "location", "northeast")
 title ("Discrete uniform PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 10 11];
 y = [0 0 0.1 0.1 0.1 0];
***** assert (unidpdf (x, 10*ones (1,6)), y)
***** assert (unidpdf (x, 10), y)
***** assert (unidpdf (x, 10*[0 NaN 1 1 1 1]), [NaN NaN y(3:6)])
***** assert (unidpdf ([x, NaN], 10), [y, NaN])
***** assert (unidpdf (single ([x, NaN]), 10), single ([y, NaN]))
***** assert (unidpdf ([x, NaN], single (10)), single ([y, NaN]))
***** error<unidpdf: function called with too few input arguments.> unidpdf ()
***** error<unidpdf: function called with too few input arguments.> unidpdf (1)
***** error<unidpdf: X and N must be of common size or scalars.> ...
 unidpdf (ones (3), ones (2))
***** error<unidpdf: X and N must be of common size or scalars.> ...
 unidpdf (ones (2), ones (3))
***** error<unidpdf: X and N must not be complex.> unidpdf (i, 2)
***** error<unidpdf: X and N must not be complex.> unidpdf (2, i)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/vmcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/vmcdf.m
***** demo
 ## Plot various CDFs from the von Mises distribution
 x1 = [-pi:0.1:pi];
 p1 = vmcdf (x1, 0, 0.5);
 p2 = vmcdf (x1, 0, 1);
 p3 = vmcdf (x1, 0, 2);
 p4 = vmcdf (x1, 0, 4);
 plot (x1, p1, "-r", x1, p2, "-g", x1, p3, "-b", x1, p4, "-c")
 grid on
 xlim ([-pi, pi])
 legend ({"μ = 0, k = 0.5", "μ = 0, k = 1", ...
          "μ = 0, k = 2", "μ = 0, k = 4"}, "location", "northwest")
 title ("Von Mises CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, p0, p1
 x = [-pi:pi/2:pi];
 p0 = [0, 0.10975, 0.5, 0.89025, 1];
 p1 = [0, 0.03752, 0.5, 0.99622, 1];
***** assert (vmcdf (x, 0, 1), p0, 1e-5)
***** assert (vmcdf (x, 0, 1, "upper"), 1 - p0, 1e-5)
***** assert (vmcdf (x, zeros (1,5), ones (1,5)), p0, 1e-5)
***** assert (vmcdf (x, zeros (1,5), ones (1,5), "upper"), 1 - p0, 1e-5)
***** assert (vmcdf (x, 0, [1 2 3 4 5]), p1, 1e-5)
***** assert (vmcdf (x, 0, [1 2 3 4 5], "upper"), 1 - p1, 1e-5)
***** assert (isa (vmcdf (single (pi), 0, 1), "single"), true)
***** assert (isa (vmcdf (pi, single (0), 1), "single"), true)
***** assert (isa (vmcdf (pi, 0, single (1)), "single"), true)
***** error<vmcdf: function called with too few input arguments.> vmcdf ()
***** error<vmcdf: function called with too few input arguments.> vmcdf (1)
***** error<vmcdf: function called with too few input arguments.> vmcdf (1, 2)
***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, "tail")
***** error<vmcdf: invalid argument for upper tail.> vmcdf (1, 2, 3, 4)
***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
 vmcdf (ones (3), ones (2), ones (2))
***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
 vmcdf (ones (2), ones (3), ones (2))
***** error<vmcdf: X, MU, and K must be of common size or scalars.> ...
 vmcdf (ones (2), ones (2), ones (3))
***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (i, 2, 2)
***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, i, 2)
***** error<vmcdf: X, MU, and K must not be complex.> vmcdf (2, 2, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/poisscdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/poisscdf.m
***** demo
 ## Plot various CDFs from the Poisson distribution
 x = 0:20;
 p1 = poisscdf (x, 1);
 p2 = poisscdf (x, 4);
 p3 = poisscdf (x, 10);
 plot (x, p1, "*b", x, p2, "*g", x, p3, "*r")
 grid on
 ylim ([0, 1])
 legend ({"λ = 1", "λ = 4", "λ = 10"}, "location", "southeast")
 title ("Poisson CDF")
 xlabel ("values in x (number of occurences)")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 1 2 Inf];
 y = [0, gammainc(1, (x(2:4) +1), "upper"), 1];
***** assert (poisscdf (x, ones (1,5)), y)
***** assert (poisscdf (x, 1), y)
***** assert (poisscdf (x, [1 0 NaN 1 1]), [y(1) 1 NaN y(4:5)])
***** assert (poisscdf ([x(1:2) NaN Inf x(5)], 1), [y(1:2) NaN 1 y(5)])
***** assert (poisscdf ([x, NaN], 1), [y, NaN])
***** assert (poisscdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
***** assert (poisscdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
***** error<poisscdf: function called with too few input arguments.> poisscdf ()
***** error<poisscdf: function called with too few input arguments.> poisscdf (1)
***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, 3)
***** error<poisscdf: invalid argument for upper tail.> poisscdf (1, 2, "tail")
***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
 poisscdf (ones (3), ones (2))
***** error<poisscdf: X and LAMBDA must be of common size or scalars.> ...
 poisscdf (ones (2), ones (3))
***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (i, 2)
***** error<poisscdf: X and LAMBDA must not be complex.> poisscdf (2, i)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/bvtcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/bvtcdf.m
***** test
 x = [1, 2];
 rho = [1, 0.5; 0.5, 1];
 df = 4;
 assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
***** test
 x = [3, 2;2, 4;1, 5];
 rho = [1, 0.5; 0.5, 1];
 df = 4;
 assert (bvtcdf(x, rho(2), df), mvtcdf(x, rho, df), 1e-14);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/dist_fun/unidinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/unidinv.m
***** demo
 ## Plot various iCDFs from the discrete uniform distribution
 p = 0.001:0.001:0.999;
 x1 = unidinv (p, 5);
 x2 = unidinv (p, 9);
 plot (p, x1, "-b", p, x2, "-g")
 grid on
 xlim ([0, 1])
 ylim ([0, 10])
 legend ({"N = 5", "N = 9"}, "location", "northwest")
 title ("Discrete uniform iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (unidinv (p, 10*ones (1,5)), [NaN NaN 5 10 NaN], eps)
***** assert (unidinv (p, 10), [NaN NaN 5 10 NaN], eps)
***** assert (unidinv (p, 10*[0 1 NaN 1 1]), [NaN NaN NaN 10 NaN], eps)
***** assert (unidinv ([p(1:2) NaN p(4:5)], 10), [NaN NaN NaN 10 NaN], eps)
***** assert (unidinv ([p, NaN], 10), [NaN NaN 5 10 NaN NaN], eps)
***** assert (unidinv (single ([p, NaN]), 10), single ([NaN NaN 5 10 NaN NaN]), eps)
***** assert (unidinv ([p, NaN], single (10)), single ([NaN NaN 5 10 NaN NaN]), eps)
***** error<unidinv: function called with too few input arguments.> unidinv ()
***** error<unidinv: function called with too few input arguments.> unidinv (1)
***** error<unidinv: P and N must be of common size or scalars.> ...
 unidinv (ones (3), ones (2))
***** error<unidinv: P and N must be of common size or scalars.> ...
 unidinv (ones (2), ones (3))
***** error<unidinv: P and N must not be complex.> unidinv (i, 2)
***** error<unidinv: P and N must not be complex.> unidinv (2, i)
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fun/unidcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/unidcdf.m
***** demo
 ## Plot various CDFs from the discrete uniform distribution
 x = 0:10;
 p1 = unidcdf (x, 5);
 p2 = unidcdf (x, 9);
 plot (x, p1, "*b", x, p2, "*g")
 grid on
 xlim ([0, 10])
 ylim ([0, 1])
 legend ({"N = 5", "N = 9"}, "location", "southeast")
 title ("Discrete uniform CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [0 1 2.5 10 11];
 y = [0, 0.1 0.2 1.0 1.0];
***** assert (unidcdf (x, 10*ones (1,5)), y)
***** assert (unidcdf (x, 10*ones (1,5), "upper"), 1 - y)
***** assert (unidcdf (x, 10), y)
***** assert (unidcdf (x, 10, "upper"), 1 - y)
***** assert (unidcdf (x, 10*[0 1 NaN 1 1]), [NaN 0.1 NaN y(4:5)])
***** assert (unidcdf ([x(1:2) NaN Inf x(5)], 10), [y(1:2) NaN 1 y(5)])
***** assert (unidcdf ([x, NaN], 10), [y, NaN])
***** assert (unidcdf (single ([x, NaN]), 10), single ([y, NaN]))
***** assert (unidcdf ([x, NaN], single (10)), single ([y, NaN]))
***** error<unidcdf: function called with too few input arguments.> unidcdf ()
***** error<unidcdf: function called with too few input arguments.> unidcdf (1)
***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, 3)
***** error<unidcdf: invalid argument for upper tail.> unidcdf (1, 2, "tail")
***** error<unidcdf: X and N must be of common size or scalars.> ...
 unidcdf (ones (3), ones (2))
***** error<unidcdf: X and N must be of common size or scalars.> ...
 unidcdf (ones (2), ones (3))
***** error<unidcdf: X and N must not be complex.> unidcdf (i, 2)
***** error<unidcdf: X and N must not be complex.> unidcdf (2, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dist_fun/logicdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/logicdf.m
***** demo
 ## Plot various CDFs from the logistic distribution
 x = -5:0.01:20;
 p1 = logicdf (x, 5, 2);
 p2 = logicdf (x, 9, 3);
 p3 = logicdf (x, 9, 4);
 p4 = logicdf (x, 6, 2);
 p5 = logicdf (x, 2, 1);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"μ = 5, σ = 2", "μ = 9, σ = 3", "μ = 9, σ = 4", ...
          "μ = 6, σ = 2", "μ = 2, σ = 1"}, "location", "southeast")
 title ("Logistic CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf -log(3) 0 log(3) Inf];
 y = [0, 1/4, 1/2, 3/4, 1];
***** assert (logicdf ([x, NaN], 0, 1), [y, NaN], eps)
***** assert (logicdf (x, 0, [-2, -1, 0, 1, 2]), [nan(1, 3), 0.75, 1], eps)
***** assert (logicdf (single ([x, NaN]), 0, 1), single ([y, NaN]), eps ("single"))
***** assert (logicdf ([x, NaN], single (0), 1), single ([y, NaN]), eps ("single"))
***** assert (logicdf ([x, NaN], 0, single (1)), single ([y, NaN]), eps ("single"))
***** error<logicdf: function called with too few input arguments.> logicdf ()
***** error<logicdf: function called with too few input arguments.> logicdf (1)
***** error<logicdf: function called with too few input arguments.> ...
 logicdf (1, 2)
***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, "tail")
***** error<logicdf: invalid argument for upper tail.> logicdf (1, 2, 3, 4)
***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logicdf (1, ones (2), ones (3))
***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logicdf (ones (2), 1, ones (3))
***** error<logicdf: X, MU, and SIGMA must be of common size or scalars.> ...
 logicdf (ones (2), ones (3), 1)
***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (i, 2, 3)
***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, i, 3)
***** error<logicdf: X, MU, and SIGMA must not be complex.> logicdf (1, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/hygepdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/hygepdf.m
***** demo
 ## Plot various PDFs from the hypergeometric distribution
 x = 0:60;
 y1 = hygepdf (x, 500, 50, 100);
 y2 = hygepdf (x, 500, 60, 200);
 y3 = hygepdf (x, 500, 70, 300);
 plot (x, y1, "*b", x, y2, "*g", x, y3, "*r")
 grid on
 xlim ([0, 60])
 ylim ([0, 0.18])
 legend ({"m = 500, k = 50, μ = 100", "m = 500, k = 60, μ = 200", ...
          "m = 500, k = 70, μ = 300"}, "location", "northeast")
 title ("Hypergeometric PDF")
 xlabel ("values in x (number of successes)")
 ylabel ("density")
***** shared x, y
 x = [-1 0 1 2 3];
 y = [0 1/6 4/6 1/6 0];
***** assert (hygepdf (x, 4 * ones (1, 5), 2, 2), y, 3 * eps)
***** assert (hygepdf (x, 4, 2 * ones (1, 5), 2), y, 3 * eps)
***** assert (hygepdf (x, 4, 2, 2 * ones (1, 5)), y, 3 * eps)
***** assert (hygepdf (x, 4 * [1, -1, NaN, 1.1, 1], 2, 2), [0, NaN, NaN, NaN, 0])
***** assert (hygepdf (x, 4, 2 * [1, -1, NaN, 1.1, 1], 2), [0, NaN, NaN, NaN, 0])
***** assert (hygepdf (x, 4, 5, 2), [NaN, NaN, NaN, NaN, NaN], 3 * eps)
***** assert (hygepdf (x, 4, 2, 2 * [1, -1, NaN, 1.1, 1]), [0, NaN, NaN, NaN, 0])
***** assert (hygepdf (x, 4, 2, 5), [NaN, NaN, NaN, NaN, NaN], 3 * eps)
***** assert (hygepdf ([x, NaN], 4, 2, 2), [y, NaN], 3 * eps)
***** assert (hygepdf (single ([x, NaN]), 4, 2, 2), single ([y, NaN]), eps ("single"))
***** assert (hygepdf ([x, NaN], single (4), 2, 2), single ([y, NaN]), eps ("single"))
***** assert (hygepdf ([x, NaN], 4, single (2), 2), single ([y, NaN]), eps ("single"))
***** assert (hygepdf ([x, NaN], 4, 2, single (2)), single ([y, NaN]), eps ("single"))
***** test
 z = zeros(3,5);
 z([4,5,6,8,9,12]) = [1, 0.5, 1/6, 0.5, 2/3, 1/6];
 assert (hygepdf (x, 4, [0, 1, 2], 2, "vectorexpand"), z, 3 * eps);
 assert (hygepdf (x, 4, [0, 1, 2]', 2, "vectorexpand"), z, 3 * eps);
 assert (hygepdf (x', 4, [0, 1, 2], 2, "vectorexpand"), z, 3 * eps);
 assert (hygepdf (2, 4, [0 ,1, 2], 2, "vectorexpand"), z(:,4), 3 * eps);
 assert (hygepdf (x, 4, 1, 2, "vectorexpand"), z(2,:), 3 *eps);
 assert (hygepdf ([NaN, x], 4, [0 1 2]', 2, "vectorexpand"), [NaN(3, 1), z], 3 * eps);
***** error<hygepdf: function called with too few input arguments.> hygepdf ()
***** error<hygepdf: function called with too few input arguments.> hygepdf (1)
***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2)
***** error<hygepdf: function called with too few input arguments.> hygepdf (1,2,3)
***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
 hygepdf (1, ones (3), ones (2), ones (2))
***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
 hygepdf (1, ones (2), ones (3), ones (2))
***** error<hygepdf: X, T, M, and N must be of common size or scalars.> ...
 hygepdf (1, ones (2), ones (2), ones (3))
***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (i, 2, 2, 2)
***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, i, 2, 2)
***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, i, 2)
***** error<hygepdf: X, T, M, and N must not be complex.> hygepdf (2, 2, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/tpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/tpdf.m
***** demo
 ## Plot various PDFs from the Student's T distribution
 x = -5:0.01:5;
 y1 = tpdf (x, 1);
 y2 = tpdf (x, 2);
 y3 = tpdf (x, 5);
 y4 = tpdf (x, Inf);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-m")
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.41])
 legend ({"df = 1", "df = 2", ...
          "df = 5", 'df = \infty'}, "location", "northeast")
 title ("Student's T PDF")
 xlabel ("values in x")
 ylabel ("density")
***** test
 x = rand (10,1);
 y = 1./(pi * (1 + x.^2));
 assert (tpdf (x, 1), y, 5*eps);
***** shared x, y
 x = [-Inf 0 0.5 1 Inf];
 y = 1./(pi * (1 + x.^2));
***** assert (tpdf (x, ones (1,5)), y, eps)
***** assert (tpdf (x, 1), y, eps)
***** assert (tpdf (x, [0 NaN 1 1 1]), [NaN NaN y(3:5)], eps)
***** assert (tpdf (x, Inf), normpdf (x))
***** assert (tpdf ([x, NaN], 1), [y, NaN], eps)
***** assert (tpdf (single ([x, NaN]), 1), single ([y, NaN]), eps ("single"))
***** assert (tpdf ([x, NaN], single (1)), single ([y, NaN]), eps ("single"))
***** error<tpdf: function called with too few input arguments.> tpdf ()
***** error<tpdf: function called with too few input arguments.> tpdf (1)
***** error<tpdf: X and DF must be of common size or scalars.> ...
 tpdf (ones (3), ones (2))
***** error<tpdf: X and DF must be of common size or scalars.> ...
 tpdf (ones (2), ones (3))
***** error<tpdf: X and DF must not be complex.> tpdf (i, 2)
***** error<tpdf: X and DF must not be complex.> tpdf (2, i)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_fun/exprnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/exprnd.m
***** assert (size (exprnd (2)), [1, 1])
***** assert (size (exprnd (ones (2,1))), [2, 1])
***** assert (size (exprnd (ones (2,2))), [2, 2])
***** assert (size (exprnd (1, 3)), [3, 3])
***** assert (size (exprnd (1, [4 1])), [4, 1])
***** assert (size (exprnd (1, 4, 1)), [4, 1])
***** assert (size (exprnd (1, 4, 1)), [4, 1])
***** assert (size (exprnd (1, 4, 1, 5)), [4, 1, 5])
***** assert (size (exprnd (1, 0, 1)), [0, 1])
***** assert (size (exprnd (1, 1, 0)), [1, 0])
***** assert (size (exprnd (1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (exprnd (1, [])), [0, 0])
***** assert (size (exprnd (1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (exprnd (2)), "double")
***** assert (class (exprnd (single (2))), "single")
***** assert (class (exprnd (single ([2 2]))), "single")
***** error<exprnd: function called with too few input arguments.> exprnd ()
***** error<exprnd: MU must not be complex.> exprnd (i)
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, -1)
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, 1.2)
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, ones (2))
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, [2 -1 2])
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (1, [2 0 2.5])
***** error<exprnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 exprnd (ones (2), ones (2))
***** error<exprnd: dimensions must be non-negative integers.> ...
 exprnd (1, 2, -1, 5)
***** error<exprnd: dimensions must be non-negative integers.> ...
 exprnd (1, 2, 1.5, 5)
***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 3)
***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), [3, 2])
***** error<exprnd: MU must be scalar or of size SZ.> exprnd (ones (2,2), 2, 3)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/dist_fun/geornd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/geornd.m
***** assert (size (geornd (0.5)), [1, 1])
***** assert (size (geornd (0.5*ones (2,1))), [2, 1])
***** assert (size (geornd (0.5*ones (2,2))), [2, 2])
***** assert (size (geornd (0.5, 3)), [3, 3])
***** assert (size (geornd (0.5, [4 1])), [4, 1])
***** assert (size (geornd (0.5, 4, 1)), [4, 1])
***** assert (size (geornd (0.5, [])), [0, 0])
***** assert (size (geornd (0.5, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (geornd (0.5)), "double")
***** assert (class (geornd (single (0.5))), "single")
***** assert (class (geornd (single ([0.5 0.5]))), "single")
***** assert (class (geornd (single (0))), "single")
***** assert (class (geornd (single (1))), "single")
***** error<geornd: function called with too few input arguments.> geornd ()
***** error<geornd: PS must not be complex.> geornd (i)
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, -1)
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, 1.2)
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, ones (2))
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, [2 -1 2])
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (1, [2 0 2.5])
***** error<geornd: SZ must be a scalar or a row vector of non-negative integers.> ...
 geornd (ones (2), ones (2))
***** error<geornd: dimensions must be non-negative integers.> ...
 geornd (1, 2, -1, 5)
***** error<geornd: dimensions must be non-negative integers.> ...
 geornd (1, 2, 1.5, 5)
***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 3)
***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), [3, 2])
***** error<geornd: PS must be scalar or of size SZ.> geornd (ones (2,2), 2, 3)
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/dist_fun/tripdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/tripdf.m
***** demo
 ## Plot various CDFs from the triangular distribution
 x = 0.001:0.001:10;
 y1 = tripdf (x, 3, 4, 6);
 y2 = tripdf (x, 1, 2, 5);
 y3 = tripdf (x, 2, 3, 9);
 y4 = tripdf (x, 2, 5, 9);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([0, 10])
 legend ({"a = 3, b = 4, c = 6", "a = 1, b = 2, c = 5", ...
          "a = 2, b = 3, c = 9", "a = 2, b = 5, c = 9"}, ...
         "location", "northeast")
 title ("Triangular CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y, deps
 x = [-1, 0, 0.1, 0.5, 0.9, 1, 2] + 1;
 y = [0, 0, 0.4, 2, 0.4, 0, 0];
 deps = 2*eps;
***** assert (tripdf (x, ones (1,7), 1.5*ones (1,7), 2*ones (1,7)), y, deps)
***** assert (tripdf (x, 1*ones (1,7), 1.5, 2), y, deps)
***** assert (tripdf (x, 1, 1.5, 2*ones (1,7)), y, deps)
***** assert (tripdf (x, 1, 1.5*ones (1,7), 2), y, deps)
***** assert (tripdf (x, 1, 1.5, 2), y, deps)
***** assert (tripdf (x, [1, 1, NaN, 1, 1, 1, 1], 1.5, 2), [y(1:2), NaN, y(4:7)], deps)
***** assert (tripdf (x, 1, 1.5, 2*[1, 1, NaN, 1, 1, 1, 1]), [y(1:2), NaN, y(4:7)], deps)
***** assert (tripdf (x, 1, 1.5*[1, 1, NaN, 1, 1, 1, 1], 2), [y(1:2), NaN, y(4:7)], deps)
***** assert (tripdf ([x, NaN], 1, 1.5, 2), [y, NaN], deps)
***** assert (tripdf (single ([x, NaN]), 1, 1.5, 2), single ([y, NaN]), eps("single"))
***** assert (tripdf ([x, NaN], single (1), 1.5, 2), single ([y, NaN]), eps("single"))
***** assert (tripdf ([x, NaN], 1, 1.5, single (2)), single ([y, NaN]), eps("single"))
***** assert (tripdf ([x, NaN], 1, single (1.5), 2), single ([y, NaN]), eps("single"))
***** error<tripdf: function called with too few input arguments.> tripdf ()
***** error<tripdf: function called with too few input arguments.> tripdf (1)
***** error<tripdf: function called with too few input arguments.> tripdf (1, 2)
***** error<tripdf: function called with too few input arguments.> tripdf (1, 2, 3)
***** error<tripdf: function called with too many inputs> ...
 tripdf (1, 2, 3, 4, 5)
***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
 tripdf (ones (3), ones (2), ones(2), ones(2))
***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
 tripdf (ones (2), ones (3), ones(2), ones(2))
***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
 tripdf (ones (2), ones (2), ones(3), ones(2))
***** error<tripdf: X, A, B, and C must be of common size or scalars.> ...
 tripdf (ones (2), ones (2), ones(2), ones(3))
***** error<tripdf: X, A, B, and C must not be complex.> tripdf (i, 2, 3, 4)
***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, i, 3, 4)
***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, i, 4)
***** error<tripdf: X, A, B, and C must not be complex.> tripdf (1, 2, 3, i)
26 tests, 26 passed, 0 known failure, 0 skipped
[inst/dist_fun/evcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/evcdf.m
***** demo
 ## Plot various CDFs from the extreme value distribution
 x = -10:0.01:10;
 p1 = evcdf (x, 0.5, 2);
 p2 = evcdf (x, 1.0, 2);
 p3 = evcdf (x, 1.5, 3);
 p4 = evcdf (x, 3.0, 4);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 legend ({"μ = 0.5, σ = 2", "μ = 1.0, σ = 2", ...
          "μ = 1.5, σ = 3", "μ = 3.0, σ = 4"}, "location", "southeast")
 title ("Extreme value CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf, 1, 2, Inf];
 y = [0, 0.6321, 0.9340, 1];
***** assert (evcdf (x, ones (1,4), ones (1,4)), y, 1e-4)
***** assert (evcdf (x, 1, ones (1,4)), y, 1e-4)
***** assert (evcdf (x, ones (1,4), 1), y, 1e-4)
***** assert (evcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN], 1e-4)
***** assert (evcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, NaN], 1e-4)
***** assert (evcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)], 1e-4)
***** assert (evcdf (x, "upper"), [1, 0.0660, 0.0006, 0], 1e-4)
***** assert (evcdf ([x, NaN], 1, 1), [y, NaN], 1e-4)
***** assert (evcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), 1e-4)
***** assert (evcdf ([x, NaN], single (1), 1), single ([y, NaN]), 1e-4)
***** assert (evcdf ([x, NaN], 1, single (1)), single ([y, NaN]), 1e-4)
***** error<evcdf: invalid number of input arguments.> evcdf ()
***** error<evcdf: invalid number of input arguments.> evcdf (1,2,3,4,5,6,7)
***** error<evcdf: invalid argument for upper tail.> evcdf (1, 2, 3, 4, "uper")
***** error<evcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 evcdf (ones (3), ones (2), ones (2))
***** error<evcdf: invalid size of covariance matrix.> evcdf (2, 3, 4, [1, 2])
***** error<evcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = evcdf (1, 2, 3)
***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
 evcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
 evcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<evcdf: invalid value for alpha.> [p, plo, pup] = ...
 evcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (i, 2, 2)
***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, i, 2)
***** error<evcdf: X, MU, and SIGMA must not be complex.> evcdf (2, 2, i)
***** error<evcdf: bad covariance matrix.> ...
 [p, plo, pup] = evcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/burrinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/burrinv.m
***** demo
 ## Plot various iCDFs from the Burr type XII distribution
 p = 0.001:0.001:0.999;
 x1 = burrinv (p, 1, 1, 1);
 x2 = burrinv (p, 1, 1, 2);
 x3 = burrinv (p, 1, 1, 3);
 x4 = burrinv (p, 1, 2, 1);
 x5 = burrinv (p, 1, 3, 1);
 x6 = burrinv (p, 1, 0.5, 2);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", ...
       p, x4, "-c", p, x5, "-m", p, x6, "-k")
 grid on
 ylim ([0, 5])
 legend ({"λ = 1, c = 1, k = 1", "λ = 1, c = 1, k = 2", ...
          "λ = 1, c = 1, k = 3", "λ = 1, c = 2, k = 1", ...
          "λ = 1, c = 3, k = 1", "λ = 1, c = 0.5, k = 2"}, ...
         "location", "northwest")
 title ("Burr type XII iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p, y
 p = [-Inf, -1, 0, 1/2, 1, 2, Inf];
 y = [NaN, NaN, 0, 1 , Inf, NaN, NaN];
***** assert (burrinv (p, ones (1,7), ones (1,7), ones(1,7)), y, eps)
***** assert (burrinv (p, 1, 1, 1), y, eps)
***** assert (burrinv (p, [1, 1, 1, NaN, 1, 1, 1], 1, 1), [y(1:3), NaN, y(5:7)], eps)
***** assert (burrinv (p, 1, [1, 1, 1, NaN, 1, 1, 1], 1), [y(1:3), NaN, y(5:7)], eps)
***** assert (burrinv (p, 1, 1, [1, 1, 1, NaN, 1, 1, 1]), [y(1:3), NaN, y(5:7)], eps)
***** assert (burrinv ([p, NaN], 1, 1, 1), [y, NaN], eps)
***** assert (burrinv (single ([p, NaN]), 1, 1, 1), single ([y, NaN]), eps("single"))
***** assert (burrinv ([p, NaN], single (1), 1, 1), single ([y, NaN]), eps("single"))
***** assert (burrinv ([p, NaN], 1, single (1), 1), single ([y, NaN]), eps("single"))
***** assert (burrinv ([p, NaN], 1, 1, single (1)), single ([y, NaN]), eps("single"))
***** error<burrinv: function called with too few input arguments.> burrinv ()
***** error<burrinv: function called with too few input arguments.> burrinv (1)
***** error<burrinv: function called with too few input arguments.> burrinv (1, 2)
***** error<burrinv: function called with too few input arguments.> burrinv (1, 2, 3)
***** error<burrinv: function called with too many inputs> ...
 burrinv (1, 2, 3, 4, 5)
***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
 burrinv (ones (3), ones (2), ones(2), ones(2))
***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
 burrinv (ones (2), ones (3), ones(2), ones(2))
***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
 burrinv (ones (2), ones (2), ones(3), ones(2))
***** error<burrinv: P, LAMBDA, C, and K must be of common size or scalars.> ...
 burrinv (ones (2), ones (2), ones(2), ones(3))
***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (i, 2, 3, 4)
***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, i, 3, 4)
***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, i, 4)
***** error<burrinv: P, LAMBDA, C, and K must not be complex.> burrinv (1, 2, 3, i)
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fun/plrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/plrnd.m
***** shared x, Fx
 x = [0, 1, 3, 4, 7, 10];
 Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
***** assert (size (plrnd (x, Fx)), [1, 1])
***** assert (size (plrnd (x, Fx, 3)), [3, 3])
***** assert (size (plrnd (x, Fx, [4, 1])), [4, 1])
***** assert (size (plrnd (x, Fx, 4, 1)), [4, 1])
***** assert (size (plrnd (x, Fx, 4, 1, 5)), [4, 1, 5])
***** assert (size (plrnd (x, Fx, 0, 1)), [0, 1])
***** assert (size (plrnd (x, Fx, 1, 0)), [1, 0])
***** assert (size (plrnd (x, Fx, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (plrnd (x, Fx, [])), [0, 0])
***** assert (size (plrnd (x, Fx, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (plrnd (x, Fx)), "double")
***** assert (class (plrnd (x, single (Fx))), "single")
***** assert (class (plrnd (single (x), Fx)), "single")
***** error<plrnd: function called with too few input arguments.> plrnd ()
***** error<plrnd: function called with too few input arguments.> plrnd (1)
***** error<plrnd: X and FX must be vectors of equal size.> ...
 plrnd ([0, 1, 2], [0, 1])
***** error<plrnd: X and FX must be at least two-elements long.> ...
 plrnd ([0], [1])
***** error<plrnd: FX must be bounded in the range> ...
 plrnd ([0, 1, 2], [0, 1, 1.5])
***** error<plrnd: FX must be bounded in the range> ...
 plrnd ([0, 1, 2], [0, i, 1])
***** error<plrnd: X and FX must not be complex.> ...
 plrnd ([0, i, 2], [0, 0.5, 1])
***** error<plrnd: X and FX must not be complex.> ...
 plrnd ([0, i, 2], [0, 0.5i, 1])
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, -1)
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, 1.2)
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, ones (2))
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, [2 -1 2])
***** error<plrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 plrnd (x, Fx, [2 0 2.5])
***** error<plrnd: dimensions must be non-negative integers.> ...
 plrnd (x, Fx, 2, -1, 5)
***** error<plrnd: dimensions must be non-negative integers.> ...
 plrnd (x, Fx, 2, 1.5, 5)
28 tests, 28 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncx2pdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/ncx2pdf.m
***** demo
 ## Plot various PDFs from the noncentral chi-squared distribution
 x = 0:0.1:10;
 y1 = ncx2pdf (x, 2, 1);
 y2 = ncx2pdf (x, 2, 2);
 y3 = ncx2pdf (x, 2, 3);
 y4 = ncx2pdf (x, 4, 1);
 y5 = ncx2pdf (x, 4, 2);
 y6 = ncx2pdf (x, 4, 3);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-k", ...
       x, y4, "-m", x, y5, "-c", x, y6, "-y")
 grid on
 xlim ([0, 10])
 ylim ([0, 0.32])
 legend ({"df = 2, λ = 1", "df = 2, λ = 2", ...
          "df = 2, λ = 3", "df = 4, λ = 1", ...
          "df = 4, λ = 2", "df = 4, λ = 3"}, "location", "northeast")
 title ("Noncentral chi-squared PDF")
 xlabel ("values in x")
 ylabel ("density")
***** demo
 ## Compare the noncentral chi-squared PDF with LAMBDA = 2 to the
 ## chi-squared PDF with the same number of degrees of freedom (4).

 x = 0:0.1:10;
 y1 = ncx2pdf (x, 4, 2);
 y2 = chi2pdf (x, 4);
 plot (x, y1, "-", x, y2, "-");
 grid on
 xlim ([0, 10])
 ylim ([0, 0.32])
 legend ({"Noncentral T(10,1)", "T(10)"}, "location", "northwest")
 title ("Noncentral chi-squared vs chi-squared PDFs")
 xlabel ("values in x")
 ylabel ("density")
***** shared x1, df, d1
 x1 = [-Inf, 2, NaN, 4, Inf];
 df = [2, 0, -1, 1, 4];
 d1 = [1, NaN, 3, -1, 2];
***** assert (ncx2pdf (x1, df, d1), [0, NaN, NaN, NaN, 0]);
***** assert (ncx2pdf (x1, df, 1), [0, 0.07093996461786045, NaN, ...
                              0.06160064323277038, 0], 1e-14);
***** assert (ncx2pdf (x1, df, 3), [0, 0.1208364909271113, NaN, ...
                              0.09631299762429098, 0], 1e-14);
***** assert (ncx2pdf (x1, df, 2), [0, 0.1076346446244688, NaN, ...
                              0.08430464047296625, 0], 1e-14);
***** assert (ncx2pdf (x1, 2, d1), [0, NaN, NaN, NaN, 0]);
***** assert (ncx2pdf (2, df, d1), [0.1747201674611283, NaN, NaN, ...
                              NaN, 0.1076346446244688], 1e-14);
***** assert (ncx2pdf (4, df, d1), [0.09355987820265799, NaN, NaN, ...
                              NaN, 0.1192317192431485], 1e-14);
***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf ()
***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1)
***** error<ncx2pdf: function called with too few input arguments.> ncx2pdf (1, 2)
***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2pdf (ones (3), ones (2), ones (2))
***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2pdf (ones (2), ones (3), ones (2))
***** error<ncx2pdf: X, DF, and LAMBDA must be of common size or scalars.> ...
 ncx2pdf (ones (2), ones (2), ones (3))
***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (i, 2, 2)
***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, i, 2)
***** error<ncx2pdf: X, DF, and LAMBDA must not be complex.> ncx2pdf (2, 2, i)
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fun/copularnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/copularnd.m
***** test
 theta = 0.5;
 r = copularnd ("Gaussian", theta);
 assert (size (r), [1, 2]);
 assert (all ((r >= 0) & (r <= 1)));
***** test
 theta = 0.5;
 df = 2;
 r = copularnd ("t", theta, df);
 assert (size (r), [1, 2]);
 assert (all ((r >= 0) & (r <= 1)));
***** test
 theta = 0.5;
 r = copularnd ("Clayton", theta);
 assert (size (r), [1, 2]);
 assert (all ((r >= 0) & (r <= 1)));
***** test
 theta = 0.5;
 n = 2;
 r = copularnd ("Clayton", theta, n);
 assert (size (r), [n, 2]);
 assert (all ((r >= 0) & (r <= 1)));
***** test
 theta = [1; 2];
 n = 2;
 d = 3;
 r = copularnd ("Clayton", theta, n, d);
 assert (size (r), [n, d]);
 assert (all ((r >= 0) & (r <= 1)));
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_fun/cauchycdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/cauchycdf.m
***** demo
 ## Plot various CDFs from the Cauchy distribution
 x = -5:0.01:5;
 p1 = cauchycdf (x, 0, 0.5);
 p2 = cauchycdf (x, 0, 1);
 p3 = cauchycdf (x, 0, 2);
 p4 = cauchycdf (x, -2, 1);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([-5, 5])
 legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
          "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "southeast")
 title ("Cauchy CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-1 0 0.5 1 2];
 y = 1/pi * atan ((x-1) / 2) + 1/2;
***** assert (cauchycdf (x, ones (1,5), 2*ones (1,5)), y)
***** assert (cauchycdf (x, 1, 2*ones (1,5)), y)
***** assert (cauchycdf (x, ones (1,5), 2), y)
***** assert (cauchycdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
***** assert (cauchycdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
***** assert (cauchycdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
***** assert (cauchycdf ([x, NaN], 1, 2), [y, NaN])
***** assert (cauchycdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
***** assert (cauchycdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
***** assert (cauchycdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
***** error<cauchycdf: function called with too few input arguments.> cauchycdf ()
***** error<cauchycdf: function called with too few input arguments.> cauchycdf (1)
***** error<cauchycdf: function called with too few input arguments.> ...
 cauchycdf (1, 2)
***** error<cauchycdf: function called with too many inputs> ...
 cauchycdf (1, 2, 3, 4, 5)
***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, "tail")
***** error<cauchycdf: invalid argument for upper tail.> cauchycdf (1, 2, 3, 4)
***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchycdf (ones (3), ones (2), ones (2))
***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchycdf (ones (2), ones (3), ones (2))
***** error<cauchycdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchycdf (ones (2), ones (2), ones (3))
***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (i, 2, 2)
***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, i, 2)
***** error<cauchycdf: X, X0, and GAMMA must not be complex.> cauchycdf (2, 2, i)
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/dist_fun/cauchypdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/cauchypdf.m
***** demo
 ## Plot various PDFs from the Cauchy distribution
 x = -5:0.01:5;
 y1 = cauchypdf (x, 0, 0.5);
 y2 = cauchypdf (x, 0, 1);
 y3 = cauchypdf (x, 0, 2);
 y4 = cauchypdf (x, -2, 1);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c")
 grid on
 xlim ([-5, 5])
 ylim ([0, 0.7])
 legend ({"x0 = 0, γ = 0.5", "x0 = 0, γ = 1", ...
          "x0 = 0, γ = 2", "x0 = -2, γ = 1"}, "location", "northeast")
 title ("Cauchy PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1 0 0.5 1 2];
 y = 1/pi * ( 2 ./ ((x-1).^2 + 2^2) );
***** assert (cauchypdf (x, ones (1,5), 2*ones (1,5)), y)
***** assert (cauchypdf (x, 1, 2*ones (1,5)), y)
***** assert (cauchypdf (x, ones (1,5), 2), y)
***** assert (cauchypdf (x, [-Inf 1 NaN 1 Inf], 2), [NaN y(2) NaN y(4) NaN])
***** assert (cauchypdf (x, 1, 2*[0 1 NaN 1 Inf]), [NaN y(2) NaN y(4) NaN])
***** assert (cauchypdf ([x, NaN], 1, 2), [y, NaN])
***** assert (cauchypdf (single ([x, NaN]), 1, 2), single ([y, NaN]), eps ("single"))
***** assert (cauchypdf ([x, NaN], single (1), 2), single ([y, NaN]), eps ("single"))
***** assert (cauchypdf ([x, NaN], 1, single (2)), single ([y, NaN]), eps ("single"))
***** test
 x = rand (10, 1);
 assert (cauchypdf (x, 0, 1), tpdf (x, 1), eps);
***** error<cauchypdf: function called with too few input arguments.> cauchypdf ()
***** error<cauchypdf: function called with too few input arguments.> cauchypdf (1)
***** error<cauchypdf: function called with too few input arguments.> ...
 cauchypdf (1, 2)
***** error<cauchypdf: function called with too many inputs> cauchypdf (1, 2, 3, 4)
***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchypdf (ones (3), ones (2), ones(2))
***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchypdf (ones (2), ones (3), ones(2))
***** error<cauchypdf: X, X0, and GAMMA must be of common size or scalars.> ...
 cauchypdf (ones (2), ones (2), ones(3))
***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (i, 4, 3)
***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, i, 3)
***** error<cauchypdf: X, X0, and GAMMA must not be complex.> cauchypdf (1, 4, i)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fun/wienrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/wienrnd.m
***** error wienrnd (0)
***** error wienrnd (1, 3, -50)
***** error wienrnd (5, 0)
***** error wienrnd (0.4, 3, 5)
***** error wienrnd ([1 4], 3, 5)
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_fun/betacdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/betacdf.m
***** demo
 ## Plot various CDFs from the Beta distribution
 x = 0:0.005:1;
 p1 = betacdf (x, 0.5, 0.5);
 p2 = betacdf (x, 5, 1);
 p3 = betacdf (x, 1, 3);
 p4 = betacdf (x, 2, 2);
 p5 = betacdf (x, 2, 5);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c", x, p5, "-m")
 grid on
 legend ({"α = β = 0.5", "α = 5, β = 1", "α = 1, β = 3", ...
          "α = 2, β = 2", "α = 2, β = 5"}, "location", "northwest")
 title ("Beta CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y, x1, x2
 x = [-1 0 0.5 1 2];
 y = [0 0 0.75 1 1];
***** assert (betacdf (x, ones (1, 5), 2 * ones (1, 5)), y)
***** assert (betacdf (x, 1, 2 * ones (1, 5)), y)
***** assert (betacdf (x, ones (1, 5), 2), y)
***** assert (betacdf (x, [0 1 NaN 1 1], 2), [NaN 0 NaN 1 1])
***** assert (betacdf (x, 1, 2 * [0 1 NaN 1 1]), [NaN 0 NaN 1 1])
***** assert (betacdf ([x(1:2) NaN x(4:5)], 1, 2), [y(1:2) NaN y(4:5)])
 x1 = [0.1:0.2:0.9];
***** assert (betacdf (x1, 2, 2), [0.028, 0.216, 0.5, 0.784, 0.972], 1e-14);
***** assert (betacdf (x1, 2, 2, "upper"), 1 - [0.028, 0.216, 0.5, 0.784, 0.972],...
        1e-14);
 x2 = [1, 2, 3];
***** assert (betacdf (0.5, x2, x2), [0.5, 0.5, 0.5], 1e-14);
***** assert (betacdf ([x, NaN], 1, 2), [y, NaN])
***** assert (betacdf (single ([x, NaN]), 1, 2), single ([y, NaN]))
***** assert (betacdf ([x, NaN], single (1), 2), single ([y, NaN]))
***** assert (betacdf ([x, NaN], 1, single (2)), single ([y, NaN]))
***** error<betacdf: function called with too few input arguments.> betacdf ()
***** error<betacdf: function called with too few input arguments.> betacdf (1)
***** error<betacdf: function called with too few input arguments.> betacdf (1, 2)
***** error<betacdf: function called with too many inputs> betacdf (1, 2, 3, 4, 5)
***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, "tail")
***** error<betacdf: invalid argument for upper tail.> betacdf (1, 2, 3, 4)
***** error<betacdf: X, A, and B must be of common size or scalars.> ...
 betacdf (ones (3), ones (2), ones (2))
***** error<betacdf: X, A, and B must be of common size or scalars.> ...
 betacdf (ones (2), ones (3), ones (2))
***** error<betacdf: X, A, and B must be of common size or scalars.> ...
 betacdf (ones (2), ones (2), ones (3))
***** error<betacdf: X, A, and B must not be complex.> betacdf (i, 2, 2)
***** error<betacdf: X, A, and B must not be complex.> betacdf (2, i, 2)
***** error<betacdf: X, A, and B must not be complex.> betacdf (2, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/jsupdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/jsupdf.m
***** error jsupdf ()
***** error jsupdf (1, 2, 3, 4)
***** error<jsupdf: X, ALPHA1, and ALPHA2 must be of common size or scalars.> ...
 jsupdf (1, ones (2), ones (3))
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/dist_fun/raylinv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/raylinv.m
***** demo
 ## Plot various iCDFs from the Rayleigh distribution
 p = 0.001:0.001:0.999;
 x1 = raylinv (p, 0.5);
 x2 = raylinv (p, 1);
 x3 = raylinv (p, 2);
 x4 = raylinv (p, 3);
 x5 = raylinv (p, 4);
 plot (p, x1, "-b", p, x2, "g", p, x3, "-r", p, x4, "-m", p, x5, "-k")
 grid on
 ylim ([0, 10])
 legend ({"σ = 0,5", "σ = 1", "σ = 2", ...
          "σ = 3", "σ = 4"}, "location", "northwest")
 title ("Rayleigh iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** test
 p = 0:0.1:0.5;
 sigma = 1:6;
 x = raylinv (p, sigma);
 expected_x = [0.0000, 0.9181, 2.0041, 3.3784, 5.0538, 7.0645];
 assert (x, expected_x, 0.001);
***** test
 p = 0:0.1:0.5;
 x = raylinv (p, 0.5);
 expected_x = [0.0000, 0.2295, 0.3340, 0.4223, 0.5054, 0.5887];
 assert (x, expected_x, 0.001);
***** error<raylinv: function called with too few input arguments.> raylinv ()
***** error<raylinv: function called with too few input arguments.> raylinv (1)
***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
 raylinv (ones (3), ones (2))
***** error<raylinv: P and SIGMA must be of common size or scalars.> ...
 raylinv (ones (2), ones (3))
***** error<raylinv: P and SIGMA must not be complex.> raylinv (i, 2)
***** error<raylinv: P and SIGMA must not be complex.> raylinv (2, i)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fun/plpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/plpdf.m
***** demo
 ## Plot various PDFs from the Piecewise linear distribution
 data = 0:0.01:10;
 x1 = [0, 1, 3, 4, 7, 10];
 Fx1 = [0, 0.2, 0.5, 0.6, 0.7, 1];
 x2 = [0, 2, 5, 6, 7, 8];
 Fx2 = [0, 0.1, 0.3, 0.6, 0.9, 1];
 y1 = plpdf (data, x1, Fx1);
 y2 = plpdf (data, x2, Fx2);
 plot (data, y1, "-b", data, y2, "g")
 grid on
 ylim ([0, 0.6])
 xlim ([0, 10])
 legend ({"x1, Fx1", "x2, Fx2"}, "location", "northeast")
 title ("Piecewise linear CDF")
 xlabel ("values in data")
 ylabel ("density")
***** shared x, Fx
 x = [0, 1, 3, 4, 7, 10];
 Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
***** assert (plpdf (0.5, x, Fx), 0.2, eps);
***** assert (plpdf (1.5, x, Fx), 0.15, eps);
***** assert (plpdf (3.5, x, Fx), 0.1, eps);
***** assert (plpdf (5, x, Fx), 0.1/3, eps);
***** assert (plpdf (8, x, Fx), 0.1, eps);
***** error<plpdf: function called with too few input arguments.> plpdf ()
***** error<plpdf: function called with too few input arguments.> plpdf (1)
***** error<plpdf: function called with too few input arguments.> plpdf (1, 2)
***** error<plpdf: X and FX must be vectors of equal size.> ...
 plpdf (1, [0, 1, 2], [0, 1])
***** error<plpdf: X and FX must be at least two-elements long.> ...
 plpdf (1, [0], [1])
***** error<plpdf: FX must be bounded in the range> ...
 plpdf (1, [0, 1, 2], [0, 1, 1.5])
***** error<plpdf: FX must be bounded in the range> ...
 plpdf (1, [0, 1, 2], [0, i, 1])
***** error<plpdf: DATA, X, and FX must not be complex.> ...
 plpdf (i, [0, 1, 2], [0, 0.5, 1])
***** error<plpdf: DATA, X, and FX must not be complex.> ...
 plpdf (1, [0, i, 2], [0, 0.5, 1])
***** error<plpdf: DATA, X, and FX must not be complex.> ...
 plpdf (1, [0, 1, 2], [0, 0.5i, 1])
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fun/ncfcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/ncfcdf.m
***** demo
 ## Plot various CDFs from the noncentral F distribution
 x = 0:0.01:5;
 p1 = ncfcdf (x, 2, 5, 1);
 p2 = ncfcdf (x, 2, 5, 2);
 p3 = ncfcdf (x, 5, 10, 1);
 p4 = ncfcdf (x, 10, 20, 10);
 plot (x, p1, "-r", x, p2, "-g", x, p3, "-k", x, p4, "-m")
 grid on
 xlim ([0, 5])
 legend ({"df1 = 2, df2 = 5, λ = 1", "df1 = 2, df2 = 5, λ = 2", ...
          "df1 = 5, df2 = 10, λ = 1", "df1 = 10, df2 = 20, λ = 10"}, ...
         "location", "southeast")
 title ("Noncentral F CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** demo
 ## Compare the noncentral F CDF with LAMBDA = 10 to the F CDF with the
 ## same number of numerator and denominator degrees of freedom (5, 20)

 x = 0.01:0.1:10.01;
 p1 = ncfcdf (x, 5, 20, 10);
 p2 = fcdf (x, 5, 20);
 plot (x, p1, "-", x, p2, "-");
 grid on
 xlim ([0, 10])
 legend ({"Noncentral F(5,20,10)", "F(5,20)"}, "location", "southeast")
 title ("Noncentral F vs F CDFs")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = -2:0.1:2;
 p = ncfcdf (x, 10, 1, 3);
 assert (p([1:21]), zeros (1, 21), 1e-76);
 assert (p(22), 0.004530737275319753, 1e-14);
 assert (p(30), 0.255842099135669, 1e-14);
 assert (p(41), 0.4379890998457305, 1e-14);
***** test
 p = ncfcdf (12, 10, 3, 2);
 assert (p, 0.9582287900447416, 1e-14);
***** test
 p = ncfcdf (2, 3, 2, 1);
 assert (p, 0.5731985522994989, 1e-14);
***** test
 p = ncfcdf (2, 3, 2, 1, "upper");
 assert (p, 0.4268014477004823, 1e-14);
***** test
 p = ncfcdf ([3, 6], 3, 2, 5, "upper");
 assert (p, [0.530248523596927, 0.3350482341323044], 1e-14);
***** error<ncfcdf: function called with too few input arguments.> ncfcdf ()
***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1)
***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2)
***** error<ncfcdf: function called with too few input arguments.> ncfcdf (1, 2, 3)
***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, "tail")
***** error<ncfcdf: invalid argument for upper tail.> ncfcdf (1, 2, 3, 4, 5)
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfcdf (ones (3), ones (2), ones (2), ones (2))
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfcdf (ones (2), ones (3), ones (2), ones (2))
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfcdf (ones (2), ones (2), ones (3), ones (2))
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfcdf (ones (2), ones (2), ones (2), ones (3))
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (i, 2, 2, 2)
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, i, 2, 2)
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, i, 2)
***** error<ncfcdf: X, DF1, DF2, and LAMBDA must not be complex.> ncfcdf (2, 2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/nakarnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/nakarnd.m
***** assert (size (nakarnd (1, 1)), [1, 1])
***** assert (size (nakarnd (1, ones (2, 1))), [2, 1])
***** assert (size (nakarnd (1, ones (2, 2))), [2, 2])
***** assert (size (nakarnd (ones (2, 1), 1)), [2, 1])
***** assert (size (nakarnd (ones (2, 2), 1)), [2, 2])
***** assert (size (nakarnd (1, 1, 3)), [3, 3])
***** assert (size (nakarnd (1, 1, [4, 1])), [4, 1])
***** assert (size (nakarnd (1, 1, 4, 1)), [4, 1])
***** assert (size (nakarnd (1, 1, 4, 1, 5)), [4, 1, 5])
***** assert (size (nakarnd (1, 1, 0, 1)), [0, 1])
***** assert (size (nakarnd (1, 1, 1, 0)), [1, 0])
***** assert (size (nakarnd (1, 1, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (nakarnd (1, 1, [])), [0, 0])
***** assert (size (nakarnd (1, 1, [2, 0, 2, 1])), [2, 0, 2])
***** assert (class (nakarnd (1, 1)), "double")
***** assert (class (nakarnd (1, single (1))), "single")
***** assert (class (nakarnd (1, single ([1, 1]))), "single")
***** assert (class (nakarnd (single (1), 1)), "single")
***** assert (class (nakarnd (single ([1, 1]), 1)), "single")
***** error<nakarnd: function called with too few input arguments.> nakarnd ()
***** error<nakarnd: function called with too few input arguments.> nakarnd (1)
***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
 nakarnd (ones (3), ones (2))
***** error<nakarnd: MU and OMEGA must be of common size or scalars.> ...
 nakarnd (ones (2), ones (3))
***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (i, 2, 3)
***** error<nakarnd: MU and OMEGA must not be complex.> nakarnd (1, i, 3)
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, -1)
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, 1.2)
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, ones (2))
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, [2 -1 2])
***** error<nakarnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 nakarnd (1, 2, [2 0 2.5])
***** error<nakarnd: dimensions must be non-negative integers.> ...
 nakarnd (1, 2, 2, -1, 5)
***** error<nakarnd: dimensions must be non-negative integers.> ...
 nakarnd (1, 2, 2, 1.5, 5)
***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
 nakarnd (2, ones (2), 3)
***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
 nakarnd (2, ones (2), [3, 2])
***** error<nakarnd: MU and OMEGA must be scalars or of size SZ.> ...
 nakarnd (2, ones (2), 3, 2)
35 tests, 35 passed, 0 known failure, 0 skipped
[inst/dist_fun/raylcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/raylcdf.m
***** demo
 ## Plot various CDFs from the Rayleigh distribution
 x = 0:0.01:10;
 p1 = raylcdf (x, 0.5);
 p2 = raylcdf (x, 1);
 p3 = raylcdf (x, 2);
 p4 = raylcdf (x, 3);
 p5 = raylcdf (x, 4);
 plot (x, p1, "-b", x, p2, "g", x, p3, "-r", x, p4, "-m", x, p5, "-k")
 grid on
 ylim ([0, 1])
 legend ({"σ = 0.5", "σ = 1", "σ = 2", ...
          "σ = 3", "σ = 4"}, "location", "southeast")
 title ("Rayleigh CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** test
 x = 0:0.5:2.5;
 sigma = 1:6;
 p = raylcdf (x, sigma);
 expected_p = [0.0000, 0.0308, 0.0540, 0.0679, 0.0769, 0.0831];
 assert (p, expected_p, 0.001);
***** test
 x = 0:0.5:2.5;
 p = raylcdf (x, 0.5);
 expected_p = [0.0000, 0.3935, 0.8647, 0.9889, 0.9997, 1.0000];
 assert (p, expected_p, 0.001);
***** shared x, p
 x = [-1, 0, 1, 2, Inf];
 p = [0, 0, 0.39346934028737, 0.86466471676338, 1];
***** assert (raylcdf (x, 1), p, 1e-14)
***** assert (raylcdf (x, 1, "upper"), 1 - p, 1e-14)
***** error<raylcdf: function called with too few input arguments.> raylcdf ()
***** error<raylcdf: function called with too few input arguments.> raylcdf (1)
***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, "uper")
***** error<raylcdf: invalid argument for upper tail.> raylcdf (1, 2, 3)
***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
 raylcdf (ones (3), ones (2))
***** error<raylcdf: X and SIGMA must be of common size or scalars.> ...
 raylcdf (ones (2), ones (3))
***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (i, 2)
***** error<raylcdf: X and SIGMA must not be complex.> raylcdf (2, i)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fun/norminv.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/norminv.m
***** demo
 ## Plot various iCDFs from the normal distribution
 p = 0.001:0.001:0.999;
 x1 = norminv (p, 0, 0.5);
 x2 = norminv (p, 0, 1);
 x3 = norminv (p, 0, 2);
 x4 = norminv (p, -2, 0.8);
 plot (p, x1, "-b", p, x2, "-g", p, x3, "-r", p, x4, "-c")
 grid on
 ylim ([-5, 5])
 legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
          "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "northwest")
 title ("Normal iCDF")
 xlabel ("probability")
 ylabel ("values in x")
***** shared p
 p = [-1 0 0.5 1 2];
***** assert (norminv (p, ones (1,5), ones (1,5)), [NaN -Inf 1 Inf NaN])
***** assert (norminv (p, 1, ones (1,5)), [NaN -Inf 1 Inf NaN])
***** assert (norminv (p, ones (1,5), 1), [NaN -Inf 1 Inf NaN])
***** assert (norminv (p, [1 -Inf NaN Inf 1], 1), [NaN NaN NaN NaN NaN])
***** assert (norminv (p, 1, [1 0 NaN Inf 1]), [NaN NaN NaN NaN NaN])
***** assert (norminv ([p(1:2) NaN p(4:5)], 1, 1), [NaN -Inf NaN Inf NaN])
***** assert (norminv (p), probit (p))
***** assert (norminv (0.31254), probit (0.31254))
***** assert (norminv ([p, NaN], 1, 1), [NaN -Inf 1 Inf NaN NaN])
***** assert (norminv (single ([p, NaN]), 1, 1), single ([NaN -Inf 1 Inf NaN NaN]))
***** assert (norminv ([p, NaN], single (1), 1), single ([NaN -Inf 1 Inf NaN NaN]))
***** assert (norminv ([p, NaN], 1, single (1)), single ([NaN -Inf 1 Inf NaN NaN]))
***** error<norminv: function called with too few input arguments.> norminv ()
***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
 norminv (ones (3), ones (2), ones (2))
***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
 norminv (ones (2), ones (3), ones (2))
***** error<norminv: P, MU, and SIGMA must be of common size or scalars.> ...
 norminv (ones (2), ones (2), ones (3))
***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (i, 2, 2)
***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, i, 2)
***** error<norminv: P, MU, and SIGMA must not be complex.> norminv (2, 2, i)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fun/tlsrnd.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/tlsrnd.m
***** assert (size (tlsrnd (1, 2, 3)), [1, 1])
***** assert (size (tlsrnd (ones (2, 1), 2, 3)), [2, 1])
***** assert (size (tlsrnd (ones (2, 2), 2, 3)), [2, 2])
***** assert (size (tlsrnd (1, 2, 3, 3)), [3, 3])
***** assert (size (tlsrnd (1, 2, 3, [4, 1])), [4, 1])
***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
***** assert (size (tlsrnd (1, 2, 3, 4, 1)), [4, 1])
***** assert (size (tlsrnd (1, 2, 3, 4, 1, 5)), [4, 1, 5])
***** assert (size (tlsrnd (1, 2, 3, 0, 1)), [0, 1])
***** assert (size (tlsrnd (1, 2, 3, 1, 0)), [1, 0])
***** assert (size (tlsrnd (1, 2, 3, 1, 2, 0, 5)), [1, 2, 0, 5])
***** assert (size (tlsrnd (1, 2, 3, [])), [0, 0])
***** assert (size (tlsrnd (1, 2, 3, [2, 0, 2, 1])), [2, 0, 2])
***** assert (tlsrnd (1, 2, 0, 1, 1), NaN)
***** assert (tlsrnd (1, 2, [0, 0, 0], [1, 3]), [NaN, NaN, NaN])
***** assert (class (tlsrnd (1, 2, 3)), "double")
***** assert (class (tlsrnd (single (1), 2, 3)), "single")
***** assert (class (tlsrnd (single ([1, 1]), 2, 3)), "single")
***** assert (class (tlsrnd (1, single (2), 3)), "single")
***** assert (class (tlsrnd (1, single ([2, 2]), 3)), "single")
***** assert (class (tlsrnd (1, 2, single (3))), "single")
***** assert (class (tlsrnd (1, 2, single ([3, 3]))), "single")
***** error<tlsrnd: function called with too few input arguments.> tlsrnd ()
***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1)
***** error<tlsrnd: function called with too few input arguments.> tlsrnd (1, 2)
***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsrnd (ones (3), ones (2), 1)
***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsrnd (ones (2), 1, ones (3))
***** error<tlsrnd: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsrnd (1, ones (2), ones (3))
***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (i, 2, 3)
***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, i, 3)
***** error<tlsrnd: MU, SIGMA, and NU must not be complex.> tlsrnd (1, 2, i)
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, -1)
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, 1.2)
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, ones (2))
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, [2 -1 2])
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (1, 2, 3, [2 0 2.5])
***** error<tlsrnd: SZ must be a scalar or a row vector of non-negative integers.> ...
 tlsrnd (ones (2), 2, 3, ones (2))
***** error<tlsrnd: dimensions must be non-negative integers.> ...
 tlsrnd (1, 2, 3, 2, -1, 5)
***** error<tlsrnd: dimensions must be non-negative integers.> ...
 tlsrnd (1, 2, 3, 2, 1.5, 5)
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (ones (2,2), 2, 3, 3)
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (1, ones (2,2), 3, 3)
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (1, 2, ones (2,2), 3)
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (1, 2, ones (2,2), [3, 3])
***** error<tlsrnd: MU, SIGMA, and NU must be scalar or of size SZ.> ...
 tlsrnd (1, 2, ones (2,2), 2, 3)
44 tests, 44 passed, 0 known failure, 0 skipped
[inst/dist_fun/fpdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/fpdf.m
***** demo
 ## Plot various PDFs from the F distribution
 x = 0.01:0.01:4;
 y1 = fpdf (x, 1, 1);
 y2 = fpdf (x, 2, 1);
 y3 = fpdf (x, 5, 2);
 y4 = fpdf (x, 10, 1);
 y5 = fpdf (x, 100, 100);
 plot (x, y1, "-b", x, y2, "-g", x, y3, "-r", x, y4, "-c", x, y5, "-m")
 grid on
 ylim ([0, 2.5])
 legend ({"df1 = 1, df2 = 2", "df1 = 2, df2 = 1", ...
          "df1 = 5, df2 = 2", "df1 = 10, df2 = 1", ...
          "df1 = 100, df2 = 100"}, "location", "northeast")
 title ("F PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1, 0, 0.5, 1, 2];
 y = [0, 0, 4/9, 1/4, 1/9];
***** assert (fpdf (x, 2*ones (1,5), 2*ones (1,5)), y, eps)
***** assert (fpdf (x, 2, 2*ones (1,5)), y, eps)
***** assert (fpdf (x, 2*ones (1,5), 2), y, eps)
***** assert (fpdf (x, [0, NaN, Inf, 2, 2], 2), [NaN, NaN, 0.5413, y(4:5)], 1e-4)
***** assert (fpdf (x, 2, [0, NaN, Inf, 2, 2]), [NaN, NaN, 0.6065, y(4:5)], 1e-4)
***** assert (fpdf ([x, NaN], 2, 2), [y, NaN], eps)
***** test #F (x, 1, df1) == T distribution (sqrt (x), df1) / sqrt (x)
 rand ("seed", 1234);    # for reproducibility
 xr = rand (10,1);
 xr = xr(x > 0.1 & x < 0.9);
 yr = tpdf (sqrt (xr), 2) ./ sqrt (xr);
 assert (fpdf (xr, 1, 2), yr, 5*eps);
***** test
 yy = fpdf (2, 4, Inf);
 assert (yy, 0.1465, 1e-4)
***** test
 yy = fpdf (2, 4, 1000000000000000);
 assert (yy, 0.1465, 1e-4)
***** test
 yy = fpdf (2, Inf, 4);
 assert (yy, 0.1839, 1e-4)
***** test
 yy = fpdf (2, 10000000000000000, 4);
 assert (yy, 0.1839, 1e-4)
***** test
 yy = fpdf (2, Inf, Inf);
 assert (yy, 0)
***** test
 yy = fpdf (NaN, Inf, Inf);
 assert (yy, NaN)
***** assert (fpdf (single ([x, NaN]), 2, 2), single ([y, NaN]), eps ("single"))
***** assert (fpdf ([x, NaN], single (2), 2), single ([y, NaN]), eps ("single"))
***** assert (fpdf ([x, NaN], 2, single (2)), single ([y, NaN]), eps ("single"))
***** error<fpdf: function called with too few input arguments.> fpdf ()
***** error<fpdf: function called with too few input arguments.> fpdf (1)
***** error<fpdf: function called with too few input arguments.> fpdf (1,2)
***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fpdf (ones (3), ones (2), ones (2))
***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fpdf (ones (2), ones (3), ones (2))
***** error<fpdf: X, DF1, and DF2 must be of common size or scalars.> ...
 fpdf (ones (2), ones (2), ones (3))
***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (i, 2, 2)
***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, i, 2)
***** error<fpdf: X, DF1, and DF2 must not be complex.> fpdf (2, 2, i)
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/dist_fun/normcdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/normcdf.m
***** demo
 ## Plot various CDFs from the normal distribution
 x = -5:0.01:5;
 p1 = normcdf (x, 0, 0.5);
 p2 = normcdf (x, 0, 1);
 p3 = normcdf (x, 0, 2);
 p4 = normcdf (x, -2, 0.8);
 plot (x, p1, "-b", x, p2, "-g", x, p3, "-r", x, p4, "-c")
 grid on
 xlim ([-5, 5])
 legend ({"μ = 0, σ = 0.5", "μ = 0, σ = 1", ...
          "μ = 0, σ = 2", "μ = -2, σ = 0.8"}, "location", "southeast")
 title ("Normal CDF")
 xlabel ("values in x")
 ylabel ("probability")
***** shared x, y
 x = [-Inf 1 2 Inf];
 y = [0, 0.5, 1/2*(1+erf(1/sqrt(2))), 1];
***** assert (normcdf (x, ones (1,4), ones (1,4)), y)
***** assert (normcdf (x, 1, ones (1,4)), y)
***** assert (normcdf (x, ones (1,4), 1), y)
***** assert (normcdf (x, [0, -Inf, NaN, Inf], 1), [0, 1, NaN, NaN])
***** assert (normcdf (x, 1, [Inf, NaN, -1, 0]), [NaN, NaN, NaN, 1])
***** assert (normcdf ([x(1:2), NaN, x(4)], 1, 1), [y(1:2), NaN, y(4)])
***** assert (normcdf (x, "upper"), [1, 0.1587, 0.0228, 0], 1e-4)
***** assert (normcdf ([x, NaN], 1, 1), [y, NaN])
***** assert (normcdf (single ([x, NaN]), 1, 1), single ([y, NaN]), eps ("single"))
***** assert (normcdf ([x, NaN], single (1), 1), single ([y, NaN]), eps ("single"))
***** assert (normcdf ([x, NaN], 1, single (1)), single ([y, NaN]), eps ("single"))
***** error<normcdf: invalid number of input arguments.> normcdf ()
***** error<normcdf: invalid number of input arguments.> normcdf (1,2,3,4,5,6,7)
***** error<normcdf: invalid argument for upper tail.> normcdf (1, 2, 3, 4, "uper")
***** error<normcdf: X, MU, and SIGMA must be of common size or scalars.> ...
 normcdf (ones (3), ones (2), ones (2))
***** error<normcdf: invalid size of covariance matrix.> normcdf (2, 3, 4, [1, 2])
***** error<normcdf: covariance matrix is required for confidence bounds.> ...
 [p, plo, pup] = normcdf (1, 2, 3)
***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
 normcdf (1, 2, 3, [1, 0; 0, 1], 0)
***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
 normcdf (1, 2, 3, [1, 0; 0, 1], 1.22)
***** error<normcdf: invalid value for alpha.> [p, plo, pup] = ...
 normcdf (1, 2, 3, [1, 0; 0, 1], "alpha", "upper")
***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (i, 2, 2)
***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, i, 2)
***** error<normcdf: X, MU, and SIGMA must not be complex.> normcdf (2, 2, i)
***** error<normcdf: bad covariance matrix.> ...
 [p, plo, pup] =normcdf (1, 2, 3, [1, 0; 0, -inf], 0.04)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/dist_fun/nakapdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fun/nakapdf.m
***** demo
 ## Plot various PDFs from the Nakagami distribution
 x = 0:0.01:3;
 y1 = nakapdf (x, 0.5, 1);
 y2 = nakapdf (x, 1, 1);
 y3 = nakapdf (x, 1, 2);
 y4 = nakapdf (x, 1, 3);
 y5 = nakapdf (x, 2, 1);
 y6 = nakapdf (x, 2, 2);
 y7 = nakapdf (x, 5, 1);
 plot (x, y1, "-r", x, y2, "-g", x, y3, "-y", x, y4, "-m", ...
       x, y5, "-k", x, y6, "-b", x, y7, "-c")
 grid on
 xlim ([0, 3])
 ylim ([0, 2])
 legend ({"μ = 0.5, ω = 1", "μ = 1, ω = 1", "μ = 1, ω = 2", ...
          "μ = 1, ω = 3", "μ = 2, ω = 1", "μ = 2, ω = 2", ...
          "μ = 5, ω = 1"}, "location", "northeast")
 title ("Nakagami PDF")
 xlabel ("values in x")
 ylabel ("density")
***** shared x, y
 x = [-1, 0, 1, 2, Inf];
 y = [0, 0, 0.73575888234288467, 0.073262555554936715, 0];
***** assert (nakapdf (x, ones (1,5), ones (1,5)), y, eps)
***** assert (nakapdf (x, 1, 1), y, eps)
***** assert (nakapdf (x, [1, 1, NaN, 1, 1], 1), [y(1:2), NaN, y(4:5)], eps)
***** assert (nakapdf (x, 1, [1, 1, NaN, 1, 1]), [y(1:2), NaN, y(4:5)], eps)
***** assert (nakapdf ([x, NaN], 1, 1), [y, NaN], eps)
***** assert (nakapdf (single ([x, NaN]), 1, 1), single ([y, NaN]))
***** assert (nakapdf ([x, NaN], single (1), 1), single ([y, NaN]))
***** assert (nakapdf ([x, NaN], 1, single (1)), single ([y, NaN]))
***** error<nakapdf: function called with too few input arguments.> nakapdf ()
***** error<nakapdf: function called with too few input arguments.> nakapdf (1)
***** error<nakapdf: function called with too few input arguments.> nakapdf (1, 2)
***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakapdf (ones (3), ones (2), ones(2))
***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakapdf (ones (2), ones (3), ones(2))
***** error<nakapdf: X, MU, and OMEGA must be of common size or scalars.> ...
 nakapdf (ones (2), ones (2), ones(3))
***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (i, 4, 3)
***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, i, 3)
***** error<nakapdf: X, MU, and OMEGA must not be complex.> nakapdf (1, 4, i)
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/cmdscale.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/cmdscale.m
***** shared m, n, X, D
 m = randi(100) + 1; n = randi(100) + 1; X = rand(m, n); D = pdist(X);
***** assert(norm(pdist(cmdscale(D))), norm(D), sqrt(eps))
***** assert(norm(pdist(cmdscale(squareform(D)))), norm(D), sqrt(eps))
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/createns.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/createns.m
***** test
 ## Default ExhaustiveSearcher
 X = [1, 2; 3, 4; 5, 6];
 obj = createns (X);
 assert (isa (obj, "ExhaustiveSearcher"));
 assert (obj.X, X);
 assert (obj.Distance, "euclidean");
***** test
 ## KDTreeSearcher with default parameters
 X = [1, 2; 3, 4; 5, 6];
 obj = createns (X, "NSMethod", "kdtree");
 assert (isa (obj, "KDTreeSearcher"));
 assert (obj.X, X);
 assert (obj.Distance, "euclidean");
***** test
 ## hnswSearcher with custom parameters
 X = [1, 2; 3, 4; 5, 6];
 obj = createns (X, "NSMethod", "hnsw", "MaxNumLinksPerNode", 2, "TrainSetSize", 3);
 assert (isa (obj, "hnswSearcher"));
 assert (obj.X, X);
 assert (obj.MaxNumLinksPerNode, 2);
 assert (obj.TrainSetSize, 3);
***** test
 ## ExhaustiveSearcher with custom distance
 X = [1, 2; 3, 4];
 obj = createns (X, "NSMethod", "exhaustive", "Distance", "cityblock");
 assert (isa (obj, "ExhaustiveSearcher"));
 assert (obj.Distance, "cityblock");
***** error<createns: too few input arguments.>
 createns ()
***** error<createns: Name-Value arguments must be in pairs.>
 X = [1, 2; 3, 4]; createns (X, "NSMethod")
***** error<createns: X must be a finite numeric matrix.>
 createns ([1; Inf; 3])
***** error<createns: 'NSMethod' must be a string.>
 X = [1, 2; 3, 4]; createns (X, "NSMethod", 1)
***** error<createns: invalid 'NSMethod' value: 'invalid'.>
 X = [1, 2; 3, 4]; createns (X, "NSMethod", "invalid")
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_wrap/mle.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_wrap/mle.m
***** error <mle: X must be a numeric vector of real values.> mle (ones (2))
***** error <mle: X must be a numeric vector of real values.> mle ("text")
***** error <mle: X must be a numeric vector of real values.> mle ([1, 2, 3, i, 5])
***** error <mle: optional arguments must be in NAME-VALUE pairs.> ...
 mle ([1:50], "distribution")
***** error <mle: 'censoring' argument must have the same size as the input data in X.> ...
 mle ([1:50], "censoring", logical ([1,0,1,0]))
***** error <mle: 'frequency' argument must have the same size as the input data in X.> ...
 mle ([1:50], "frequency", [1,0,1,0])
***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
 mle ([1 0 1 0], "frequency", [-1 1 0 0])
***** error <mle: 'frequency' argument must contain non-negative integer values.> ...
 mle ([1 0 1 0], "distribution", "nbin", "frequency", [-1 1 0 0])
***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", [0.05, 0.01])
***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", 1)
***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", -1)
***** error <mle: invalid value for 'alpha' argument.> mle ([1:50], "alpha", i)
***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
 mle ([1:50], "ntrials", -1)
***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
 mle ([1:50], "ntrials", [20, 50])
***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
 mle ([1:50], "ntrials", [20.3])
***** error <mle: 'ntrials' argument must be a positive integer scalar value.> ...
 mle ([1:50], "ntrials", 3i)
***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
 mle ([1:50], "options", 4)
***** error <mle: 'options' argument must be a structure compatible for 'fminsearch'.> ...
 mle ([1:50], "options", struct ("x", 3))
***** error <mle: unknown parameter name.> mle ([1:50], "NAME", "value")
***** error <mle: censoring is not supported for the Bernoulli distribution.> ...
 mle ([1 0 1 0], "distribution", "bernoulli", "censoring", [1 1 0 0])
***** error <mle: invalid data for the Bernoulli distribution.> ...
 mle ([1 2 1 0], "distribution", "bernoulli")
***** error <mle: censoring is not supported for the Beta distribution.> ...
 mle ([1 0 1 0], "distribution", "beta", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Binomial distribution.> ...
 mle ([1 0 1 0], "distribution", "bino", "censoring", [1 1 0 0])
***** error <mle: 'Ntrials' parameter is required for the Binomial distribution.> ...
 mle ([1 0 1 0], "distribution", "bino")
***** error <mle: censoring is not supported for the Geometric distribution.> ...
 mle ([1 0 1 0], "distribution", "geo", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Generalized Extreme Value distribution.> ...
 mle ([1 0 1 0], "distribution", "gev", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Generalized Pareto distribution.> ...
 mle ([1 0 1 0], "distribution", "gp", "censoring", [1 1 0 0])
***** error <mle: invalid 'theta' location parameter for the Generalized Pareto distribution.> ...
 mle ([1 0 -1 0], "distribution", "gp")
***** error <mle: censoring is not supported for the Half Normal distribution.> ...
 mle ([1 0 1 0], "distribution", "hn", "censoring", [1 1 0 0])
***** error <mle: invalid 'mu' location parameter for the Half Normal distribution.> ...
 mle ([1 0 -1 0], "distribution", "hn")
***** error <mle: censoring is not supported for the Negative Binomial distribution.> ...
 mle ([1 0 1 0], "distribution", "nbin", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Poisson distribution.> ...
 mle ([1 0 1 0], "distribution", "poisson", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Discrete Uniform distribution.> ...
 mle ([1 0 1 0], "distribution", "unid", "censoring", [1 1 0 0])
***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
 mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
***** error <mle: unrecognized distribution name.> mle ([1:50], "distribution", "value")
***** error <mle: censoring is not supported for the Continuous Uniform distribution.> ...
 mle ([1 0 1 0], "distribution", "unif", "censoring", [1 1 0 0])
36 tests, 36 passed, 0 known failure, 0 skipped
[inst/dist_wrap/fitdist.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_wrap/fitdist.m
***** test
 x = betarnd (1, 1, 100, 1);
 pd = fitdist (x, "Beta");
 [phat, pci] = betafit (x);
 assert ([pd.a, pd.b], phat);
 assert (paramci (pd), pci);
***** test
 x1 = betarnd (1, 1, 100, 1);
 x2 = betarnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "Beta", "By", [ones(100, 1); 2*ones(100, 1)]);
 [phat, pci] = betafit (x1);
 assert ([pd{1}.a, pd{1}.b], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = betafit (x2);
 assert ([pd{2}.a, pd{2}.b], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'Beta' distribution.> ...
 fitdist ([betarnd(1, 1, 100, 1); nan(100, 1)], "Beta", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 N = 1;
 x = binornd (N, 0.5, 100, 1);
 pd = fitdist (x, "binomial");
 [phat, pci] = binofit (sum (x), numel (x));
 assert ([pd.N, pd.p], [N, phat]);
 assert (paramci (pd), pci);
***** test
 N = 3;
 x = binornd (N, 0.4, 100, 1);
 pd = fitdist (x, "binomial", "ntrials", N);
 [phat, pci] = binofit (sum (x), numel (x) * N);
 assert ([pd.N, pd.p], [N, phat]);
 assert (paramci (pd), pci);
***** test
 N = 1;
 x1 = binornd (N, 0.5, 100, 1);
 x2 = binornd (N, 0.7, 100, 1);
 pd = fitdist ([x1; x2], "binomial", "By", [ones(100, 1); 2*ones(100, 1)]);
 [phat, pci] = binofit (sum (x1), numel (x1));
 assert ([pd{1}.N, pd{1}.p], [N, phat]);
 assert (paramci (pd{1}), pci);
 [phat, pci] = binofit (sum (x2), numel (x2));
 assert ([pd{2}.N, pd{2}.p], [N, phat]);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'binomial' distribution.> ...
 fitdist ([binornd(1, 0.5, 100, 1); nan(100, 1)], "binomial", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 N = 5;
 x1 = binornd (N, 0.5, 100, 1);
 x2 = binornd (N, 0.8, 100, 1);
 pd = fitdist ([x1; x2], "binomial", "ntrials", N, ...
               "By", [ones(100, 1); 2*ones(100, 1)]);
 [phat, pci] = binofit (sum (x1), numel (x1) * N);
 assert ([pd{1}.N, pd{1}.p], [N, phat]);
 assert (paramci (pd{1}), pci);
 [phat, pci] = binofit (sum (x2), numel (x2) * N);
 assert ([pd{2}.N, pd{2}.p], [N, phat]);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'binomial' distribution.> ...
 fitdist ([binornd(5, 0.5, 100, 1); nan(100, 1)], "binomial", "ntrials", 5, ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = bisarnd (1, 1, 100, 1);
 pd = fitdist (x, "BirnbaumSaunders");
 [phat, pci] = bisafit (x);
 assert ([pd.beta, pd.gamma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = bisarnd (1, 1, 100, 1);
 x2 = bisarnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "bisa", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = bisafit (x1);
 assert ([pd{1}.beta, pd{1}.gamma], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = bisafit (x2);
 assert ([pd{2}.beta, pd{2}.gamma], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'bisa' distribution.> ...
 fitdist ([bisarnd(1, 1, 100, 1); nan(100, 1)], "bisa", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = burrrnd (1, 2, 1, 100, 1);
 pd = fitdist (x, "Burr");
 [phat, pci] = burrfit (x);
 assert ([pd.alpha, pd.c, pd.k], phat);
 assert (paramci (pd), pci);
***** test
 rand ("seed", 4);   # for reproducibility
 x1 = burrrnd (1, 2, 1, 100, 1);
 rand ("seed", 3);   # for reproducibility
 x2 = burrrnd (1, 0.5, 2, 100, 1);
 pd = fitdist ([x1; x2], "burr", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = burrfit (x1);
 assert ([pd{1}.alpha, pd{1}.c, pd{1}.k], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = burrfit (x2);
 assert ([pd{2}.alpha, pd{2}.c, pd{2}.k], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'burr' distribution.> ...
 fitdist ([burrrnd(1, 2, 1, 100, 1); nan(100, 1)], "burr", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = exprnd (1, 100, 1);
 pd = fitdist (x, "exponential");
 [muhat, muci] = expfit (x);
 assert ([pd.mu], muhat);
 assert (paramci (pd), muci);
***** test
 x1 = exprnd (1, 100, 1);
 x2 = exprnd (5, 100, 1);
 pd = fitdist ([x1; x2], "exponential", "By", [ones(100,1); 2*ones(100,1)]);
 [muhat, muci] = expfit (x1);
 assert ([pd{1}.mu], muhat);
 assert (paramci (pd{1}), muci);
 [muhat, muci] = expfit (x2);
 assert ([pd{2}.mu], muhat);
 assert (paramci (pd{2}), muci);
***** warning <fitdist: no data in group '2' to fit a 'exponential' distribution.> ...
 fitdist ([exprnd(1, 100, 1); nan(100, 1)], "exponential", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = evrnd (1, 1, 100, 1);
 pd = fitdist (x, "ev");
 [phat, pci] = evfit (x);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = evrnd (1, 1, 100, 1);
 x2 = evrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "extremevalue", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = evfit (x1);
 assert ([pd{1}.mu, pd{1}.sigma], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = evfit (x2);
 assert ([pd{2}.mu, pd{2}.sigma], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'extremevalue' distribution.> ...
 fitdist ([evrnd(1, 1, 100, 1); nan(100, 1)], "extremevalue", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = gamrnd (1, 1, 100, 1);
 pd = fitdist (x, "Gamma");
 [phat, pci] = gamfit (x);
 assert ([pd.a, pd.b], phat);
 assert (paramci (pd), pci);
***** test
 x1 = gamrnd (1, 1, 100, 1);
 x2 = gamrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "Gamma", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = gamfit (x1);
 assert ([pd{1}.a, pd{1}.b], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = gamfit (x2);
 assert ([pd{2}.a, pd{2}.b], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'Gamma' distribution.> ...
 fitdist ([gamrnd(1, 1, 100, 1); nan(100, 1)], "Gamma", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 rand ("seed", 4);   # for reproducibility
 x = gevrnd (-0.5, 1, 2, 1000, 1);
 pd = fitdist (x, "generalizedextremevalue");
 [phat, pci] = gevfit (x);
 assert ([pd.k, pd.sigma, pd.mu], phat);
 assert (paramci (pd), pci);
***** test
 rand ("seed", 5);   # for reproducibility
 x1 = gevrnd (-0.5, 1, 2, 1000, 1);
 rand ("seed", 9);   # for reproducibility
 x2 = gevrnd (0, 1, -4, 1000, 1);
 pd = fitdist ([x1; x2], "gev", "By", [ones(1000,1); 2*ones(1000,1)]);
 [phat, pci] = gevfit (x1);
 assert ([pd{1}.k, pd{1}.sigma, pd{1}.mu], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = gevfit (x2);
 assert ([pd{2}.k, pd{2}.sigma, pd{2}.mu], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'gev' distribution.> ...
 fitdist ([gevrnd(-0.5, 1, 2, 1000, 1); nan(1000, 1)], "gev", ...
          "By", [ones(1000, 1); 2*ones(1000, 1)]);
***** test
 x = gprnd (1, 1, 1, 100, 1);
 pd = fitdist (x, "GeneralizedPareto");
 [phat, pci] = gpfit (x, 1);
 assert ([pd.k, pd.sigma, pd.theta], phat);
 assert (paramci (pd), pci);
***** test
 x = gprnd (1, 1, 2, 100, 1);
 pd = fitdist (x, "GeneralizedPareto", "theta", 2);
 [phat, pci] = gpfit (x, 2);
 assert ([pd.k, pd.sigma, pd.theta], phat);
 assert (paramci (pd), pci);
***** test
 x1 = gprnd (1, 1, 1, 100, 1);
 x2 = gprnd (0, 2, 1, 100, 1);
 pd = fitdist ([x1; x2], "gp", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = gpfit (x1, 1);
 assert ([pd{1}.k, pd{1}.sigma, pd{1}.theta], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = gpfit (x2, 1);
 assert ([pd{2}.k, pd{2}.sigma, pd{2}.theta], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'gp' distribution.> ...
 fitdist ([gprnd(1, 1, 1, 100, 1); nan(100, 1)], "gp", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x1 = gprnd (3, 2, 2, 100, 1);
 x2 = gprnd (2, 3, 2, 100, 1);
 pd = fitdist ([x1; x2], "GeneralizedPareto", "theta", 2, ...
               "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = gpfit (x1, 2);
 assert ([pd{1}.k, pd{1}.sigma, pd{1}.theta], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = gpfit (x2, 2);
 assert ([pd{2}.k, pd{2}.sigma, pd{2}.theta], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'gp' distribution.> ...
 fitdist ([gprnd(3, 2, 2, 100, 1); nan(100, 1)], "gp", "theta", 2, ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = hnrnd (0, 1, 100, 1);
 pd = fitdist (x, "HalfNormal");
 [phat, pci] = hnfit (x, 0);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x = hnrnd (1, 1, 100, 1);
 pd = fitdist (x, "HalfNormal", "mu", 1);
 [phat, pci] = hnfit (x, 1);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = hnrnd (0, 1, 100, 1);
 x2 = hnrnd (0, 2, 100, 1);
 pd = fitdist ([x1; x2], "HalfNormal", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = hnfit (x1, 0);
 assert ([pd{1}.mu, pd{1}.sigma], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = hnfit (x2, 0);
 assert ([pd{2}.mu, pd{2}.sigma], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'HalfNormal' distribution.> ...
 fitdist ([hnrnd(0, 1, 100, 1); nan(100, 1)], "HalfNormal", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x1 = hnrnd (2, 1, 100, 1);
 x2 = hnrnd (2, 2, 100, 1);
 pd = fitdist ([x1; x2], "HalfNormal", "mu", 2, ...
               "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = hnfit (x1, 2);
 assert ([pd{1}.mu, pd{1}.sigma], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = hnfit (x2, 2);
 assert ([pd{2}.mu, pd{2}.sigma], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'HalfNormal' distribution.> ...
 fitdist ([hnrnd(2, 1, 100, 1); nan(100, 1)], "HalfNormal", "mu", 2, ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = invgrnd (1, 1, 100, 1);
 pd = fitdist (x, "InverseGaussian");
 [phat, pci] = invgfit (x);
 assert ([pd.mu, pd.lambda], phat);
 assert (paramci (pd), pci);
***** test
 x1 = invgrnd (1, 1, 100, 1);
 x2 = invgrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "InverseGaussian", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = invgfit (x1);
 assert ([pd{1}.mu, pd{1}.lambda], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = invgfit (x2);
 assert ([pd{2}.mu, pd{2}.lambda], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'InverseGaussian' distribution.> ...
 fitdist ([invgrnd(1, 1, 100, 1); nan(100, 1)], "InverseGaussian", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = logirnd (1, 1, 100, 1);
 pd = fitdist (x, "logistic");
 [phat, pci] = logifit (x);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = logirnd (1, 1, 100, 1);
 x2 = logirnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "logistic", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = logifit (x1);
 assert ([pd{1}.mu, pd{1}.sigma], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = logifit (x2);
 assert ([pd{2}.mu, pd{2}.sigma], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'logistic' distribution.> ...
 fitdist ([logirnd(1, 1, 100, 1); nan(100, 1)], "logistic", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = loglrnd (1, 1, 100, 1);
 pd = fitdist (x, "loglogistic");
 [phat, pci] = loglfit (x);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = loglrnd (1, 1, 100, 1);
 x2 = loglrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "loglogistic", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = loglfit (x1);
 assert ([pd{1}.mu, pd{1}.sigma], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = loglfit (x2);
 assert ([pd{2}.mu, pd{2}.sigma], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'loglogistic' distribution.> ...
 fitdist ([loglrnd(1, 1, 100, 1); nan(100, 1)], "loglogistic", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = lognrnd (1, 1, 100, 1);
 pd = fitdist (x, "lognormal");
 [phat, pci] = lognfit (x);
 assert ([pd.mu, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = lognrnd (1, 1, 100, 1);
 x2 = lognrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "lognormal", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = lognfit (x1);
 assert ([pd{1}.mu, pd{1}.sigma], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = lognfit (x2);
 assert ([pd{2}.mu, pd{2}.sigma], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'lognormal' distribution.> ...
 fitdist ([lognrnd(1, 1, 100, 1); nan(100, 1)], "lognormal", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = nakarnd (2, 0.5, 100, 1);
 pd = fitdist (x, "Nakagami");
 [phat, pci] = nakafit (x);
 assert ([pd.mu, pd.omega], phat);
 assert (paramci (pd), pci);
***** test
 x1 = nakarnd (2, 0.5, 100, 1);
 x2 = nakarnd (5, 0.8, 100, 1);
 pd = fitdist ([x1; x2], "Nakagami", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = nakafit (x1);
 assert ([pd{1}.mu, pd{1}.omega], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = nakafit (x2);
 assert ([pd{2}.mu, pd{2}.omega], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'Nakagami' distribution.> ...
 fitdist ([nakarnd(2, 0.5, 100, 1); nan(100, 1)], "Nakagami", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 randp ("seed", 123);
 randg ("seed", 321);
 x = nbinrnd (2, 0.5, 100, 1);
 pd = fitdist (x, "negativebinomial");
 [phat, pci] = nbinfit (x);
 assert ([pd.R, pd.P], phat);
 assert (paramci (pd), pci);
***** test
 randp ("seed", 345);
 randg ("seed", 543);
 x1 = nbinrnd (2, 0.5, 100, 1);
 randp ("seed", 432);
 randg ("seed", 234);
 x2 = nbinrnd (5, 0.8, 100, 1);
 pd = fitdist ([x1; x2], "nbin", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = nbinfit (x1);
 assert ([pd{1}.R, pd{1}.P], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = nbinfit (x2);
 assert ([pd{2}.R, pd{2}.P], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'nbin' distribution.> ...
 fitdist ([nbinrnd(2, 0.5, 100, 1); nan(100, 1)], "nbin", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = normrnd (1, 1, 100, 1);
 pd = fitdist (x, "normal");
 [muhat, sigmahat, muci, sigmaci] = normfit (x);
 assert ([pd.mu, pd.sigma], [muhat, sigmahat]);
 assert (paramci (pd), [muci, sigmaci]);
***** test
 x1 = normrnd (1, 1, 100, 1);
 x2 = normrnd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "normal", "By", [ones(100,1); 2*ones(100,1)]);
 [muhat, sigmahat, muci, sigmaci] = normfit (x1);
 assert ([pd{1}.mu, pd{1}.sigma], [muhat, sigmahat]);
 assert (paramci (pd{1}), [muci, sigmaci]);
 [muhat, sigmahat, muci, sigmaci] = normfit (x2);
 assert ([pd{2}.mu, pd{2}.sigma], [muhat, sigmahat]);
 assert (paramci (pd{2}), [muci, sigmaci]);
***** warning <fitdist: no data in group '2' to fit a 'normal' distribution.> ...
 fitdist ([normrnd(1, 1, 100, 1); nan(100, 1)], "normal", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = poissrnd (1, 100, 1);
 pd = fitdist (x, "poisson");
 [phat, pci] = poissfit (x);
 assert (pd.lambda, phat);
 assert (paramci (pd), pci);
***** test
 x1 = poissrnd (1, 100, 1);
 x2 = poissrnd (5, 100, 1);
 pd = fitdist ([x1; x2], "poisson", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = poissfit (x1);
 assert (pd{1}.lambda, phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = poissfit (x2);
 assert (pd{2}.lambda, phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'poisson' distribution.> ...
 fitdist ([poissrnd(1, 100, 1); nan(100, 1)], "poisson", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = raylrnd (1, 100, 1);
 pd = fitdist (x, "rayleigh");
 [phat, pci] = raylfit (x);
 assert (pd.sigma, phat);
 assert (paramci (pd), pci);
***** test
 x1 = raylrnd (1, 100, 1);
 x2 = raylrnd (5, 100, 1);
 pd = fitdist ([x1; x2], "rayleigh", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = raylfit (x1);
 assert (pd{1}.sigma, phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = raylfit (x2);
 assert (pd{2}.sigma, phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'rayleigh' distribution.> ...
 fitdist ([raylrnd(1, 100, 1); nan(100, 1)], "rayleigh", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = ricernd (1, 1, 100, 1);
 pd = fitdist (x, "rician");
 [phat, pci] = ricefit (x);
 assert ([pd.s, pd.sigma], phat);
 assert (paramci (pd), pci);
***** test
 x1 = ricernd (1, 1, 100, 1);
 x2 = ricernd (5, 2, 100, 1);
 pd = fitdist ([x1; x2], "rician", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = ricefit (x1);
 assert ([pd{1}.s, pd{1}.sigma], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = ricefit (x2);
 assert ([pd{2}.s, pd{2}.sigma], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'rician' distribution.> ...
 fitdist ([ricernd(1, 1, 100, 1); nan(100, 1)], "rician", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** warning <fitdist: 'Stable' distribution not supported yet.> ...
 fitdist ([1 2 3 4 5], "Stable");
***** test
 x = tlsrnd (0, 1, 1, 100, 1);
 pd = fitdist (x, "tlocationscale");
 [phat, pci] = tlsfit (x);
 assert ([pd.mu, pd.sigma, pd.nu], phat);
 assert (paramci (pd), pci);
***** test
 x1 = tlsrnd (0, 1, 1, 100, 1);
 x2 = tlsrnd (5, 2, 1, 100, 1);
 pd = fitdist ([x1; x2], "tlocationscale", "By", [ones(100,1); 2*ones(100,1)]);
 [phat, pci] = tlsfit (x1);
 assert ([pd{1}.mu, pd{1}.sigma, pd{1}.nu], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = tlsfit (x2);
 assert ([pd{2}.mu, pd{2}.sigma, pd{2}.nu], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'tlocationscale' distribution.> ...
 fitdist ([tlsrnd(0, 1, 1, 100, 1); nan(100, 1)], "tlocationscale", ...
          "By", [ones(100, 1); 2*ones(100, 1)]);
***** test
 x = [1 2 3 4 5];
 pd = fitdist (x, "weibull");
 [phat, pci] = wblfit (x);
 assert ([pd.lambda, pd.k], phat);
 assert (paramci (pd), pci);
***** test
 x = [1 2 3 4 5 6 7 8 9 10];
 pd = fitdist (x, "weibull", "By", [1 1 1 1 1 2 2 2 2 2]);
 [phat, pci] = wblfit (x(1:5));
 assert ([pd{1}.lambda, pd{1}.k], phat);
 assert (paramci (pd{1}), pci);
 [phat, pci] = wblfit (x(6:10));
 assert ([pd{2}.lambda, pd{2}.k], phat);
 assert (paramci (pd{2}), pci);
***** warning <fitdist: no data in group '2' to fit a 'weibull' distribution.> ...
 fitdist ([1 2 3 4 5 NaN NaN NaN NaN NaN], "weibull", "By", [1 1 1 1 1 2 2 2 2 2]);
***** error <fitdist: DISTNAME is required.> fitdist (1)
***** error <fitdist: DISTNAME must be a character vector.> fitdist (1, ["as";"sd"])
***** error <fitdist: unrecognized distribution name.> fitdist (1, "some")
***** error <fitdist: X must be a numeric vector of real values.> ...
 fitdist (ones (2), "normal")
***** error <fitdist: X must be a numeric vector of real values.> ...
 fitdist ([i, 2, 3], "normal")
***** error <fitdist: X must be a numeric vector of real values.> ...
 fitdist (["a", "s", "d"], "normal")
***** error <fitdist: optional arguments must be in NAME-VALUE pairs.> ...
 fitdist ([1, 2, 3], "normal", "By")
***** error <fitdist: GROUPVAR argument must have the same size as the input data in X.> ...
 fitdist ([1, 2, 3], "normal", "By", [1, 2])
***** error <fitdist: 'censoring' argument must have the same size as the input data in X.> ...
 fitdist ([1, 2, 3], "normal", "Censoring", [1, 2])
***** error <fitdist: 'frequency' argument must have the same size as the input data in X.> ...
 fitdist ([1, 2, 3], "normal", "frequency", [1, 2])
***** error <fitdist: 'frequency' argument must contain non-negative integer values.> ...
 fitdist ([1, 2, 3], "negativebinomial", "frequency", [1, -2, 3])
***** error <fitdist: invalid value for 'alpha' argument.> ...
 fitdist ([1, 2, 3], "normal", "alpha", [1, 2])
***** error <fitdist: invalid value for 'alpha' argument.> ...
 fitdist ([1, 2, 3], "normal", "alpha", i)
***** error <fitdist: invalid value for 'alpha' argument.> ...
 fitdist ([1, 2, 3], "normal", "alpha", -0.5)
***** error <fitdist: invalid value for 'alpha' argument.> ...
 fitdist ([1, 2, 3], "normal", "alpha", 1.5)
***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
 fitdist ([1, 2, 3], "normal", "ntrials", [1, 2])
***** error <fitdist: 'ntrials' argument must be a positive integer scalar value.> ...
 fitdist ([1, 2, 3], "normal", "ntrials", 0)
***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
 fitdist ([1, 2, 3], "normal", "options", 0)
***** error <fitdist: 'options' argument must be a structure compatible for 'fminsearch'.> ...
 fitdist ([1, 2, 3], "normal", "options", struct ("options", 1))
***** warning fitdist ([1, 2, 3], "kernel", "kernel", "normal");
***** warning fitdist ([1, 2, 3], "kernel", "support", "positive");
***** warning fitdist ([1, 2, 3], "kernel", "width", 1);
***** error <fitdist: unknown parameter name.> ...
 fitdist ([1, 2, 3], "normal", "param", struct ("options", 1))
***** error <fitdist: no data in X to fit a 'normal' distribution.> ...
 fitdist (nan (100,1), "normal");
***** error <fitdist: must define GROUPVAR for more than one output arguments.> ...
 [pdca, gn, gl] = fitdist ([1, 2, 3], "normal");
***** error <fitdist: invalid THETA value for generalized Pareto distribution.> ...
 fitdist ([1, 2, 3], "generalizedpareto", "theta", 2);
***** error <fitdist: invalid MU value for half-normal distribution.> ...
 fitdist ([1, 2, 3], "halfnormal", "mu", 2);
103 tests, 103 passed, 0 known failure, 0 skipped
[inst/dist_wrap/icdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_wrap/icdf.m
***** shared p
 p = [0.05:0.05:0.5];
***** assert (icdf ("Beta", p, 5, 2), betainv (p, 5, 2))
***** assert (icdf ("beta", p, 5, 2), betainv (p, 5, 2))
***** assert (icdf ("Binomial", p, 5, 2), binoinv (p, 5, 2))
***** assert (icdf ("bino", p, 5, 2), binoinv (p, 5, 2))
***** assert (icdf ("Birnbaum-Saunders", p, 5, 2), bisainv (p, 5, 2))
***** assert (icdf ("bisa", p, 5, 2), bisainv (p, 5, 2))
***** assert (icdf ("Burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
***** assert (icdf ("burr", p, 5, 2, 2), burrinv (p, 5, 2, 2))
***** assert (icdf ("Cauchy", p, 5, 2), cauchyinv (p, 5, 2))
***** assert (icdf ("cauchy", p, 5, 2), cauchyinv (p, 5, 2))
***** assert (icdf ("Chi-squared", p, 5), chi2inv (p, 5))
***** assert (icdf ("chi2", p, 5), chi2inv (p, 5))
***** assert (icdf ("Extreme Value", p, 5, 2), evinv (p, 5, 2))
***** assert (icdf ("ev", p, 5, 2), evinv (p, 5, 2))
***** assert (icdf ("Exponential", p, 5), expinv (p, 5))
***** assert (icdf ("exp", p, 5), expinv (p, 5))
***** assert (icdf ("F-Distribution", p, 5, 2), finv (p, 5, 2))
***** assert (icdf ("f", p, 5, 2), finv (p, 5, 2))
***** assert (icdf ("Gamma", p, 5, 2), gaminv (p, 5, 2))
***** assert (icdf ("gam", p, 5, 2), gaminv (p, 5, 2))
***** assert (icdf ("Geometric", p, 5), geoinv (p, 5))
***** assert (icdf ("geo", p, 5), geoinv (p, 5))
***** assert (icdf ("Generalized Extreme Value", p, 5, 2, 2), gevinv (p, 5, 2, 2))
***** assert (icdf ("gev", p, 5, 2, 2), gevinv (p, 5, 2, 2))
***** assert (icdf ("Generalized Pareto", p, 5, 2, 2), gpinv (p, 5, 2, 2))
***** assert (icdf ("gp", p, 5, 2, 2), gpinv (p, 5, 2, 2))
***** assert (icdf ("Gumbel", p, 5, 2), gumbelinv (p, 5, 2))
***** assert (icdf ("gumbel", p, 5, 2), gumbelinv (p, 5, 2))
***** assert (icdf ("Half-normal", p, 5, 2), hninv (p, 5, 2))
***** assert (icdf ("hn", p, 5, 2), hninv (p, 5, 2))
***** assert (icdf ("Hypergeometric", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
***** assert (icdf ("hyge", p, 5, 2, 2), hygeinv (p, 5, 2, 2))
***** assert (icdf ("Inverse Gaussian", p, 5, 2), invginv (p, 5, 2))
***** assert (icdf ("invg", p, 5, 2), invginv (p, 5, 2))
***** assert (icdf ("Laplace", p, 5, 2), laplaceinv (p, 5, 2))
***** assert (icdf ("laplace", p, 5, 2), laplaceinv (p, 5, 2))
***** assert (icdf ("Logistic", p, 5, 2), logiinv (p, 5, 2))
***** assert (icdf ("logi", p, 5, 2), logiinv (p, 5, 2))
***** assert (icdf ("Log-Logistic", p, 5, 2), loglinv (p, 5, 2))
***** assert (icdf ("logl", p, 5, 2), loglinv (p, 5, 2))
***** assert (icdf ("Lognormal", p, 5, 2), logninv (p, 5, 2))
***** assert (icdf ("logn", p, 5, 2), logninv (p, 5, 2))
***** assert (icdf ("Nakagami", p, 5, 2), nakainv (p, 5, 2))
***** assert (icdf ("naka", p, 5, 2), nakainv (p, 5, 2))
***** assert (icdf ("Negative Binomial", p, 5, 2), nbininv (p, 5, 2))
***** assert (icdf ("nbin", p, 5, 2), nbininv (p, 5, 2))
***** assert (icdf ("Noncentral F-Distribution", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
***** assert (icdf ("ncf", p, 5, 2, 2), ncfinv (p, 5, 2, 2))
***** assert (icdf ("Noncentral Student T", p, 5, 2), nctinv (p, 5, 2))
***** assert (icdf ("nct", p, 5, 2), nctinv (p, 5, 2))
***** assert (icdf ("Noncentral Chi-Squared", p, 5, 2), ncx2inv (p, 5, 2))
***** assert (icdf ("ncx2", p, 5, 2), ncx2inv (p, 5, 2))
***** assert (icdf ("Normal", p, 5, 2), norminv (p, 5, 2))
***** assert (icdf ("norm", p, 5, 2), norminv (p, 5, 2))
***** assert (icdf ("Poisson", p, 5), poissinv (p, 5))
***** assert (icdf ("poiss", p, 5), poissinv (p, 5))
***** assert (icdf ("Rayleigh", p, 5), raylinv (p, 5))
***** assert (icdf ("rayl", p, 5), raylinv (p, 5))
***** assert (icdf ("Rician", p, 5, 1), riceinv (p, 5, 1))
***** assert (icdf ("rice", p, 5, 1), riceinv (p, 5, 1))
***** assert (icdf ("Student T", p, 5), tinv (p, 5))
***** assert (icdf ("t", p, 5), tinv (p, 5))
***** assert (icdf ("location-scale T", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
***** assert (icdf ("tls", p, 5, 1, 2), tlsinv (p, 5, 1, 2))
***** assert (icdf ("Triangular", p, 5, 2, 2), triinv (p, 5, 2, 2))
***** assert (icdf ("tri", p, 5, 2, 2), triinv (p, 5, 2, 2))
***** assert (icdf ("Discrete Uniform", p, 5), unidinv (p, 5))
***** assert (icdf ("unid", p, 5), unidinv (p, 5))
***** assert (icdf ("Uniform", p, 5, 2), unifinv (p, 5, 2))
***** assert (icdf ("unif", p, 5, 2), unifinv (p, 5, 2))
***** assert (icdf ("Von Mises", p, 5, 2), vminv (p, 5, 2))
***** assert (icdf ("vm", p, 5, 2), vminv (p, 5, 2))
***** assert (icdf ("Weibull", p, 5, 2), wblinv (p, 5, 2))
***** assert (icdf ("wbl", p, 5, 2), wblinv (p, 5, 2))
***** error<icdf: distribution NAME must be a char string.> icdf (1)
***** error<icdf: distribution NAME must be a char string.> icdf ({"beta"})
***** error<icdf: P must be numeric.> icdf ("beta", {[1 2 3 4 5]})
***** error<icdf: P must be numeric.> icdf ("beta", "text")
***** error<icdf: values in P must be real.> icdf ("beta", 1+i)
***** error<icdf: distribution parameters must be numeric.> ...
 icdf ("Beta", p, "a", 2)
***** error<icdf: distribution parameters must be numeric.> ...
 icdf ("Beta", p, 5, "")
***** error<icdf: distribution parameters must be numeric.> ...
 icdf ("Beta", p, 5, {2})
***** error<icdf: chi2 distribution requires 1 parameter.> icdf ("chi2", p)
***** error<icdf: Beta distribution requires 2 parameters.> icdf ("Beta", p, 5)
***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5)
***** error<icdf: Burr distribution requires 3 parameters.> icdf ("Burr", p, 5, 2)
86 tests, 86 passed, 0 known failure, 0 skipped
[inst/dist_wrap/random.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_wrap/random.m
***** assert (size (random ("Beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
***** assert (size (random ("beta", 5, 2, 2, 10)), size (betarnd (5, 2, 2, 10)))
***** assert (size (random ("Binomial", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
***** assert (size (random ("bino", 5, 2, [10, 20])), size (binornd (5, 2, 10, 20)))
***** assert (size (random ("Birnbaum-Saunders", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
***** assert (size (random ("bisa", 5, 2, [10, 20])), size (bisarnd (5, 2, 10, 20)))
***** assert (size (random ("Burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("burr", 5, 2, 2, [10, 20])), size (burrrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
***** assert (size (random ("cauchy", 5, 2, [10, 20])), size (cauchyrnd (5, 2, 10, 20)))
***** assert (size (random ("Chi-squared", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
***** assert (size (random ("chi2", 5, [10, 20])), size (chi2rnd (5, 10, 20)))
***** assert (size (random ("Extreme Value", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
***** assert (size (random ("ev", 5, 2, [10, 20])), size (evrnd (5, 2, 10, 20)))
***** assert (size (random ("Exponential", 5, [10, 20])), size (exprnd (5, 10, 20)))
***** assert (size (random ("exp", 5, [10, 20])), size (exprnd (5, 10, 20)))
***** assert (size (random ("F-Distribution", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
***** assert (size (random ("f", 5, 2, [10, 20])), size (frnd (5, 2, 10, 20)))
***** assert (size (random ("Gamma", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
***** assert (size (random ("gam", 5, 2, [10, 20])), size (gamrnd (5, 2, 10, 20)))
***** assert (size (random ("Geometric", 5, [10, 20])), size (geornd (5, 10, 20)))
***** assert (size (random ("geo", 5, [10, 20])), size (geornd (5, 10, 20)))
***** assert (size (random ("Generalized Extreme Value", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("gev", 5, 2, 2, [10, 20])), size (gevrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Generalized Pareto", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
***** assert (size (random ("gp", 5, 2, 2, [10, 20])), size (gprnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
***** assert (size (random ("gumbel", 5, 2, [10, 20])), size (gumbelrnd (5, 2, 10, 20)))
***** assert (size (random ("Half-normal", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
***** assert (size (random ("hn", 5, 2, [10, 20])), size (hnrnd (5, 2, 10, 20)))
***** assert (size (random ("Hypergeometric", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
***** assert (size (random ("hyge", 5, 2, 2, [10, 20])), size (hygernd (5, 2, 2, 10, 20)))
***** assert (size (random ("Inverse Gaussian", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
***** assert (size (random ("invg", 5, 2, [10, 20])), size (invgrnd (5, 2, 10, 20)))
***** assert (size (random ("Laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
***** assert (size (random ("laplace", 5, 2, [10, 20])), size (laplacernd (5, 2, 10, 20)))
***** assert (size (random ("Logistic", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
***** assert (size (random ("logi", 5, 2, [10, 20])), size (logirnd (5, 2, 10, 20)))
***** assert (size (random ("Log-Logistic", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
***** assert (size (random ("logl", 5, 2, [10, 20])), size (loglrnd (5, 2, 10, 20)))
***** assert (size (random ("Lognormal", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
***** assert (size (random ("logn", 5, 2, [10, 20])), size (lognrnd (5, 2, 10, 20)))
***** assert (size (random ("Nakagami", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
***** assert (size (random ("naka", 5, 2, [10, 20])), size (nakarnd (5, 2, 10, 20)))
***** assert (size (random ("Negative Binomial", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
***** assert (size (random ("nbin", 5, 2, [10, 20])), size (nbinrnd (5, 2, 10, 20)))
***** assert (size (random ("Noncentral F-Distribution", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("ncf", 5, 2, 2, [10, 20])), size (ncfrnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Noncentral Student T", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
***** assert (size (random ("nct", 5, 2, [10, 20])), size (nctrnd (5, 2, 10, 20)))
***** assert (size (random ("Noncentral Chi-Squared", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
***** assert (size (random ("ncx2", 5, 2, [10, 20])), size (ncx2rnd (5, 2, 10, 20)))
***** assert (size (random ("Normal", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
***** assert (size (random ("norm", 5, 2, [10, 20])), size (normrnd (5, 2, 10, 20)))
***** assert (size (random ("Poisson", 5, [10, 20])), size (poissrnd (5, 10, 20)))
***** assert (size (random ("poiss", 5, [10, 20])), size (poissrnd (5, 10, 20)))
***** assert (size (random ("Rayleigh", 5, [10, 20])), size (raylrnd (5, 10, 20)))
***** assert (size (random ("rayl", 5, [10, 20])), size (raylrnd (5, 10, 20)))
***** assert (size (random ("Rician", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
***** assert (size (random ("rice", 5, 1, [10, 20])), size (ricernd (5, 1, 10, 20)))
***** assert (size (random ("Student T", 5, [10, 20])), size (trnd (5, 10, 20)))
***** assert (size (random ("t", 5, [10, 20])), size (trnd (5, 10, 20)))
***** assert (size (random ("location-scale T", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
***** assert (size (random ("tls", 5, 1, 2, [10, 20])), size (tlsrnd (5, 1, 2, 10, 20)))
***** assert (size (random ("Triangular", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
***** assert (size (random ("tri", 5, 2, 2, [10, 20])), size (trirnd (5, 2, 2, 10, 20)))
***** assert (size (random ("Discrete Uniform", 5, [10, 20])), size (unidrnd (5, 10, 20)))
***** assert (size (random ("unid", 5, [10, 20])), size (unidrnd (5, 10, 20)))
***** assert (size (random ("Uniform", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
***** assert (size (random ("unif", 5, 2, [10, 20])), size (unifrnd (5, 2, 10, 20)))
***** assert (size (random ("Von Mises", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
***** assert (size (random ("vm", 5, 2, [10, 20])), size (vmrnd (5, 2, 10, 20)))
***** assert (size (random ("Weibull", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
***** assert (size (random ("wbl", 5, 2, [10, 20])), size (wblrnd (5, 2, 10, 20)))
***** error<random: distribution NAME must be a char string.> random (1)
***** error<random: distribution NAME must be a char string.> random ({"beta"})
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", "a", 2)
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, "")
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, {2})
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", "a", 2, 2, 10)
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, "", 2, 10)
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, {2}, 2, 10)
***** error<random: distribution parameters must be numeric.> ...
 random ("Beta", 5, "", 2, 10)
***** error<random: chi2 distribution requires 1 parameter.> random ("chi2")
***** error<random: Beta distribution requires 2 parameters.> random ("Beta", 5)
***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5)
***** error<random: Burr distribution requires 3 parameters.> random ("Burr", 5, 2)
87 tests, 87 passed, 0 known failure, 0 skipped
[inst/dist_wrap/makedist.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_wrap/makedist.m
***** test
 pd = makedist ("beta");
 assert (class (pd), "BetaDistribution");
 assert (pd.a, 1);
 assert (pd.b, 1);
***** test
 pd = makedist ("beta", "a", 5);
 assert (pd.a, 5);
 assert (pd.b, 1);
***** test
 pd = makedist ("beta", "b", 5);
 assert (pd.a, 1);
 assert (pd.b, 5);
***** test
 pd = makedist ("beta", "a", 3, "b", 5);
 assert (pd.a, 3);
 assert (pd.b, 5);
***** test
 pd = makedist ("binomial");
 assert (class (pd), "BinomialDistribution");
 assert (pd.N, 1);
 assert (pd.p, 0.5);
***** test
 pd = makedist ("binomial", "N", 5);
 assert (pd.N, 5);
 assert (pd.p, 0.5);
***** test
 pd = makedist ("binomial", "p", 0.2);
 assert (pd.N, 1);
 assert (pd.p, 0.2);
***** test
 pd = makedist ("binomial", "N", 3, "p", 0.3);
 assert (pd.N, 3);
 assert (pd.p, 0.3);
***** test
 pd = makedist ("birnbaumsaunders");
 assert (class (pd), "BirnbaumSaundersDistribution");
 assert (pd.beta, 1);
 assert (pd.gamma, 1);
***** test
 pd = makedist ("birnbaumsaunders", "beta", 5);
 assert (pd.beta, 5);
 assert (pd.gamma, 1);
***** test
 pd = makedist ("birnbaumsaunders", "gamma", 5);
 assert (pd.beta, 1);
 assert (pd.gamma, 5);
***** test
 pd = makedist ("birnbaumsaunders", "beta", 3, "gamma", 5);
 assert (pd.beta, 3);
 assert (pd.gamma, 5);
***** test
 pd = makedist ("burr");
 assert (class (pd), "BurrDistribution");
 assert (pd.alpha, 1);
 assert (pd.c, 1);
 assert (pd.k, 1);
***** test
 pd = makedist ("burr", "k", 5);
 assert (pd.alpha, 1);
 assert (pd.c, 1);
 assert (pd.k, 5);
***** test
 pd = makedist ("burr", "c", 5);
 assert (pd.alpha, 1);
 assert (pd.c, 5);
 assert (pd.k, 1);
***** test
 pd = makedist ("burr", "alpha", 3, "c", 5);
 assert (pd.alpha, 3);
 assert (pd.c, 5);
 assert (pd.k, 1);
***** test
 pd = makedist ("burr", "k", 3, "c", 5);
 assert (pd.alpha, 1);
 assert (pd.c, 5);
 assert (pd.k, 3);
***** test
 pd = makedist ("exponential");
 assert (class (pd), "ExponentialDistribution");
 assert (pd.mu, 1);
***** test
 pd = makedist ("exponential", "mu", 5);
 assert (pd.mu, 5);
***** test
 pd = makedist ("extremevalue");
 assert (class (pd), "ExtremeValueDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("extremevalue", "mu", 5);
 assert (class (pd), "ExtremeValueDistribution");
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("ev", "sigma", 5);
 assert (class (pd), "ExtremeValueDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("ev", "mu", -3, "sigma", 5);
 assert (class (pd), "ExtremeValueDistribution");
 assert (pd.mu, -3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("gamma");
 assert (class (pd), "GammaDistribution");
 assert (pd.a, 1);
 assert (pd.b, 1);
***** test
 pd = makedist ("gamma", "a", 5);
 assert (pd.a, 5);
 assert (pd.b, 1);
***** test
 pd = makedist ("gamma", "b", 5);
 assert (pd.a, 1);
 assert (pd.b, 5);
***** test
 pd = makedist ("gamma", "a", 3, "b", 5);
 assert (pd.a, 3);
 assert (pd.b, 5);
***** test
 pd = makedist ("GeneralizedExtremeValue");
 assert (class (pd), "GeneralizedExtremeValueDistribution");
 assert (pd.k, 0);
 assert (pd.sigma, 1);
 assert (pd.mu, 0);
***** test
 pd = makedist ("GeneralizedExtremeValue", "k", 5);
 assert (pd.k, 5);
 assert (pd.sigma, 1);
 assert (pd.mu, 0);
***** test
 pd = makedist ("GeneralizedExtremeValue", "sigma", 5);
 assert (pd.k, 0);
 assert (pd.sigma, 5);
 assert (pd.mu, 0);
***** test
 pd = makedist ("GeneralizedExtremeValue", "k", 3, "sigma", 5);
 assert (pd.k, 3);
 assert (pd.sigma, 5);
 assert (pd.mu, 0);
***** test
 pd = makedist ("GeneralizedExtremeValue", "mu", 3, "sigma", 5);
 assert (pd.k, 0);
 assert (pd.sigma, 5);
 assert (pd.mu, 3);
***** test
 pd = makedist ("GeneralizedPareto");
 assert (class (pd), "GeneralizedParetoDistribution");
 assert (pd.k, 1);
 assert (pd.sigma, 1);
 assert (pd.theta, 1);
***** test
 pd = makedist ("GeneralizedPareto", "k", 5);
 assert (pd.k, 5);
 assert (pd.sigma, 1);
 assert (pd.theta, 1);
***** test
 pd = makedist ("GeneralizedPareto", "sigma", 5);
 assert (pd.k, 1);
 assert (pd.sigma, 5);
 assert (pd.theta, 1);
***** test
 pd = makedist ("GeneralizedPareto", "k", 3, "sigma", 5);
 assert (pd.k, 3);
 assert (pd.sigma, 5);
 assert (pd.theta, 1);
***** test
 pd = makedist ("GeneralizedPareto", "theta", 3, "sigma", 5);
 assert (pd.k, 1);
 assert (pd.sigma, 5);
 assert (pd.theta, 3);
***** test
 pd = makedist ("HalfNormal");
 assert (class (pd), "HalfNormalDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("HalfNormal", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("HalfNormal", "sigma", 5);
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("HalfNormal", "mu", 3, "sigma", 5);
 assert (pd.mu, 3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("InverseGaussian");
 assert (class (pd), "InverseGaussianDistribution");
 assert (pd.mu, 1);
 assert (pd.lambda, 1);
***** test
 pd = makedist ("InverseGaussian", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.lambda, 1);
***** test
 pd = makedist ("InverseGaussian", "lambda", 5);
 assert (pd.mu, 1);
 assert (pd.lambda, 5);
***** test
 pd = makedist ("InverseGaussian", "mu", 3, "lambda", 5);
 assert (pd.mu, 3);
 assert (pd.lambda, 5);
***** test
 pd = makedist ("logistic");
 assert (class (pd), "LogisticDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("logistic", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("logistic", "sigma", 5);
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("logistic", "mu", 3, "sigma", 5);
 assert (pd.mu, 3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("loglogistic");
 assert (class (pd), "LoglogisticDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("loglogistic", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("loglogistic", "sigma", 5);
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("loglogistic", "mu", 3, "sigma", 5);
 assert (pd.mu, 3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Lognormal");
 assert (class (pd), "LognormalDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Lognormal", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Lognormal", "sigma", 5);
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Lognormal", "mu", -3, "sigma", 5);
 assert (pd.mu, -3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Loguniform");
 assert (class (pd), "LoguniformDistribution");
 assert (pd.Lower, 1);
 assert (pd.Upper, 4);
***** test
 pd = makedist ("Loguniform", "Lower", 2);
 assert (pd.Lower, 2);
 assert (pd.Upper, 4);
***** test
 pd = makedist ("Loguniform", "Lower", 1, "Upper", 3);
 assert (pd.Lower, 1);
 assert (pd.Upper, 3);
***** test
 pd = makedist ("Multinomial");
 assert (class (pd), "MultinomialDistribution");
 assert (pd.Probabilities, [0.5, 0.5]);
***** test
 pd = makedist ("Multinomial", "Probabilities", [0.2, 0.3, 0.1, 0.4]);
 assert (class (pd), "MultinomialDistribution");
 assert (pd.Probabilities, [0.2, 0.3, 0.1, 0.4]);
***** test
 pd = makedist ("Nakagami");
 assert (class (pd), "NakagamiDistribution");
 assert (pd.mu, 1);
 assert (pd.omega, 1);
***** test
 pd = makedist ("Nakagami", "mu", 5);
 assert (class (pd), "NakagamiDistribution");
 assert (pd.mu, 5);
 assert (pd.omega, 1);
***** test
 pd = makedist ("Nakagami", "omega", 0.3);
 assert (class (pd), "NakagamiDistribution");
 assert (pd.mu, 1);
 assert (pd.omega, 0.3);
***** test
 pd = makedist ("NegativeBinomial");
 assert (class (pd), "NegativeBinomialDistribution");
 assert (pd.R, 1);
 assert (pd.P, 0.5);
***** test
 pd = makedist ("NegativeBinomial", "R", 5);
 assert (class (pd), "NegativeBinomialDistribution");
 assert (pd.R, 5);
 assert (pd.P, 0.5);
***** test
 pd = makedist ("NegativeBinomial", "p", 0.3);
 assert (class (pd), "NegativeBinomialDistribution");
 assert (pd.R, 1);
 assert (pd.P, 0.3);
***** test
 pd = makedist ("Normal");
 assert (class (pd), "NormalDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Normal", "mu", 5);
 assert (class (pd), "NormalDistribution");
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Normal", "sigma", 5);
 assert (class (pd), "NormalDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Normal", "mu", -3, "sigma", 5);
 assert (class (pd), "NormalDistribution");
 assert (pd.mu, -3);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("PiecewiseLinear");
 assert (class (pd), "PiecewiseLinearDistribution");
 assert (pd.x, [0; 1]);
 assert (pd.Fx, [0; 1]);
***** test
 pd = makedist ("PiecewiseLinear", "x", [0, 1, 2], "Fx", [0, 0.5, 1]);
 assert (pd.x, [0; 1; 2]);
 assert (pd.Fx, [0; 0.5; 1]);
***** test
 pd = makedist ("Poisson");
 assert (class (pd), "PoissonDistribution");
 assert (pd.lambda, 1);
***** test
 pd = makedist ("Poisson", "lambda", 5);
 assert (pd.lambda, 5);
***** test
 pd = makedist ("Rayleigh");
 assert (class (pd), "RayleighDistribution");
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Rayleigh", "sigma", 5);
 assert (pd.sigma, 5);
***** test
 pd = makedist ("Rician");
 assert (class (pd), "RicianDistribution");
 assert (pd.s, 1);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Rician", "s", 3);
 assert (pd.s, 3);
 assert (pd.sigma, 1);
***** test
 pd = makedist ("Rician", "sigma", 3);
 assert (pd.s, 1);
 assert (pd.sigma, 3);
***** test
 pd = makedist ("Rician", "s", 2, "sigma", 3);
 assert (pd.s, 2);
 assert (pd.sigma, 3);
***** warning
 pd = makedist ("stable");
 assert (class (pd), "double");
 assert (isempty (pd), true);
***** test
 pd = makedist ("tlocationscale");
 assert (class (pd), "tLocationScaleDistribution");
 assert (pd.mu, 0);
 assert (pd.sigma, 1);
 assert (pd.nu, 5);
***** test
 pd = makedist ("tlocationscale", "mu", 5);
 assert (pd.mu, 5);
 assert (pd.sigma, 1);
 assert (pd.nu, 5);
***** test
 pd = makedist ("tlocationscale", "sigma", 2);
 assert (pd.mu, 0);
 assert (pd.sigma, 2);
 assert (pd.nu, 5);
***** test
 pd = makedist ("tlocationscale", "mu", 5, "sigma", 2);
 assert (pd.mu, 5);
 assert (pd.sigma, 2);
 assert (pd.nu, 5);
***** test
 pd = makedist ("tlocationscale", "nu", 1, "sigma", 2);
 assert (pd.mu, 0);
 assert (pd.sigma, 2);
 assert (pd.nu, 1);
***** test
 pd = makedist ("tlocationscale", "mu", -2, "sigma", 3, "nu", 1);
 assert (pd.mu, -2);
 assert (pd.sigma, 3);
 assert (pd.nu, 1);
***** test
 pd = makedist ("Triangular");
 assert (class (pd), "TriangularDistribution");
 assert (pd.A, 0);
 assert (pd.B, 0.5);
 assert (pd.C, 1);
***** test
 pd = makedist ("Triangular", "A", -2);
 assert (pd.A, -2);
 assert (pd.B, 0.5);
 assert (pd.C, 1);
***** test
 pd = makedist ("Triangular", "A", 0.5, "B", 0.9);
 assert (pd.A, 0.5);
 assert (pd.B, 0.9);
 assert (pd.C, 1);
***** test
 pd = makedist ("Triangular", "A", 1, "B", 2, "C", 5);
 assert (pd.A, 1);
 assert (pd.B, 2);
 assert (pd.C, 5);
***** test
 pd = makedist ("Uniform");
 assert (class (pd), "UniformDistribution");
 assert (pd.Lower, 0);
 assert (pd.Upper, 1);
***** test
 pd = makedist ("Uniform", "Lower", -2);
 assert (pd.Lower, -2);
 assert (pd.Upper, 1);
***** test
 pd = makedist ("Uniform", "Lower", 1, "Upper", 3);
 assert (pd.Lower, 1);
 assert (pd.Upper, 3);
***** test
 pd = makedist ("Weibull");
 assert (class (pd), "WeibullDistribution");
 assert (pd.lambda, 1);
 assert (pd.k, 1);
***** test
 pd = makedist ("Weibull", "lambda", 3);
 assert (pd.lambda, 3);
 assert (pd.k, 1);
***** test
 pd = makedist ("Weibull", "lambda", 3, "k", 2);
 assert (pd.lambda, 3);
 assert (pd.k, 2);
***** error <makedist: DISTNAME must be a character vector.> makedist (1)
***** error <makedist: DISTNAME must be a character vector.> makedist (["as";"sd"])
***** error <makedist: unrecognized distribution name.> makedist ("some")
***** error <makedist: optional arguments must be in NAME-VALUE pairs.> ...
 makedist ("Beta", "a")
***** error <makedist: unknown parameter for 'Beta' distribution.> ...
 makedist ("Beta", "a", 1, "Q", 23)
***** error <makedist: unknown parameter for 'Binomial' distribution.> ...
 makedist ("Binomial", "N", 1, "Q", 23)
***** error <makedist: unknown parameter for 'BirnbaumSaunders' distribution.> ...
 makedist ("BirnbaumSaunders", "N", 1)
***** error <makedist: unknown parameter for 'Burr' distribution.> ...
 makedist ("Burr", "lambda", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'ExtremeValue' distribution.> ...
 makedist ("extremevalue", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Exponential' distribution.> ...
 makedist ("exponential", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Gamma' distribution.> ...
 makedist ("Gamma", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'GeneralizedExtremeValue' distribution.> ...
 makedist ("GeneralizedExtremeValue", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'GeneralizedPareto' distribution.> ...
 makedist ("GeneralizedPareto", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'HalfNormal' distribution.> ...
 makedist ("HalfNormal", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'InverseGaussian' distribution.> ...
 makedist ("InverseGaussian", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Logistic' distribution.> ...
 makedist ("Logistic", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Loglogistic' distribution.> ...
 makedist ("Loglogistic", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Lognormal' distribution.> ...
 makedist ("Lognormal", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Loguniform' distribution.> ...
 makedist ("Loguniform", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Multinomial' distribution.> ...
 makedist ("Multinomial", "k", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Nakagami' distribution.> ...
 makedist ("Nakagami", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'NegativeBinomial' distribution.> ...
 makedist ("NegativeBinomial", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Normal' distribution.> ...
 makedist ("Normal", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'PiecewiseLinear' distribution.> ...
 makedist ("PiecewiseLinear", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Poisson' distribution.> ...
 makedist ("Poisson", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Rayleigh' distribution.> ...
 makedist ("Rayleigh", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Rician' distribution.> ...
 makedist ("Rician", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Stable' distribution.> ...
 makedist ("Stable", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'tLocationScale' distribution.> ...
 makedist ("tLocationScale", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Triangular' distribution.> ...
 makedist ("Triangular", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Uniform' distribution.> ...
 makedist ("Uniform", "mu", 1, "sdfs", 34)
***** error <makedist: unknown parameter for 'Weibull' distribution.> ...
 makedist ("Weibull", "mu", 1, "sdfs", 34)
131 tests, 131 passed, 0 known failure, 0 skipped
[inst/dist_wrap/pdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_wrap/pdf.m
***** shared x
 x = [1:5];
***** assert (pdf ("Beta", x, 5, 2), betapdf (x, 5, 2))
***** assert (pdf ("beta", x, 5, 2), betapdf (x, 5, 2))
***** assert (pdf ("Binomial", x, 5, 2), binopdf (x, 5, 2))
***** assert (pdf ("bino", x, 5, 2), binopdf (x, 5, 2))
***** assert (pdf ("Birnbaum-Saunders", x, 5, 2), bisapdf (x, 5, 2))
***** assert (pdf ("bisa", x, 5, 2), bisapdf (x, 5, 2))
***** assert (pdf ("Burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
***** assert (pdf ("burr", x, 5, 2, 2), burrpdf (x, 5, 2, 2))
***** assert (pdf ("Cauchy", x, 5, 2), cauchypdf (x, 5, 2))
***** assert (pdf ("cauchy", x, 5, 2), cauchypdf (x, 5, 2))
***** assert (pdf ("Chi-squared", x, 5), chi2pdf (x, 5))
***** assert (pdf ("chi2", x, 5), chi2pdf (x, 5))
***** assert (pdf ("Extreme Value", x, 5, 2), evpdf (x, 5, 2))
***** assert (pdf ("ev", x, 5, 2), evpdf (x, 5, 2))
***** assert (pdf ("Exponential", x, 5), exppdf (x, 5))
***** assert (pdf ("exp", x, 5), exppdf (x, 5))
***** assert (pdf ("F-Distribution", x, 5, 2), fpdf (x, 5, 2))
***** assert (pdf ("f", x, 5, 2), fpdf (x, 5, 2))
***** assert (pdf ("Gamma", x, 5, 2), gampdf (x, 5, 2))
***** assert (pdf ("gam", x, 5, 2), gampdf (x, 5, 2))
***** assert (pdf ("Geometric", x, 5), geopdf (x, 5))
***** assert (pdf ("geo", x, 5), geopdf (x, 5))
***** assert (pdf ("Generalized Extreme Value", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
***** assert (pdf ("gev", x, 5, 2, 2), gevpdf (x, 5, 2, 2))
***** assert (pdf ("Generalized Pareto", x, 5, 2, 2), gppdf (x, 5, 2, 2))
***** assert (pdf ("gp", x, 5, 2, 2), gppdf (x, 5, 2, 2))
***** assert (pdf ("Gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
***** assert (pdf ("gumbel", x, 5, 2), gumbelpdf (x, 5, 2))
***** assert (pdf ("Half-normal", x, 5, 2), hnpdf (x, 5, 2))
***** assert (pdf ("hn", x, 5, 2), hnpdf (x, 5, 2))
***** assert (pdf ("Hypergeometric", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
***** assert (pdf ("hyge", x, 5, 2, 2), hygepdf (x, 5, 2, 2))
***** assert (pdf ("Inverse Gaussian", x, 5, 2), invgpdf (x, 5, 2))
***** assert (pdf ("invg", x, 5, 2), invgpdf (x, 5, 2))
***** assert (pdf ("Laplace", x, 5, 2), laplacepdf (x, 5, 2))
***** assert (pdf ("laplace", x, 5, 2), laplacepdf (x, 5, 2))
***** assert (pdf ("Logistic", x, 5, 2), logipdf (x, 5, 2))
***** assert (pdf ("logi", x, 5, 2), logipdf (x, 5, 2))
***** assert (pdf ("Log-Logistic", x, 5, 2), loglpdf (x, 5, 2))
***** assert (pdf ("logl", x, 5, 2), loglpdf (x, 5, 2))
***** assert (pdf ("Lognormal", x, 5, 2), lognpdf (x, 5, 2))
***** assert (pdf ("logn", x, 5, 2), lognpdf (x, 5, 2))
***** assert (pdf ("Nakagami", x, 5, 2), nakapdf (x, 5, 2))
***** assert (pdf ("naka", x, 5, 2), nakapdf (x, 5, 2))
***** assert (pdf ("Negative Binomial", x, 5, 2), nbinpdf (x, 5, 2))
***** assert (pdf ("nbin", x, 5, 2), nbinpdf (x, 5, 2))
***** assert (pdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
***** assert (pdf ("ncf", x, 5, 2, 2), ncfpdf (x, 5, 2, 2))
***** assert (pdf ("Noncentral Student T", x, 5, 2), nctpdf (x, 5, 2))
***** assert (pdf ("nct", x, 5, 2), nctpdf (x, 5, 2))
***** assert (pdf ("Noncentral Chi-Squared", x, 5, 2), ncx2pdf (x, 5, 2))
***** assert (pdf ("ncx2", x, 5, 2), ncx2pdf (x, 5, 2))
***** assert (pdf ("Normal", x, 5, 2), normpdf (x, 5, 2))
***** assert (pdf ("norm", x, 5, 2), normpdf (x, 5, 2))
***** assert (pdf ("Poisson", x, 5), poisspdf (x, 5))
***** assert (pdf ("poiss", x, 5), poisspdf (x, 5))
***** assert (pdf ("Rayleigh", x, 5), raylpdf (x, 5))
***** assert (pdf ("rayl", x, 5), raylpdf (x, 5))
***** assert (pdf ("Rician", x, 5, 1), ricepdf (x, 5, 1))
***** assert (pdf ("rice", x, 5, 1), ricepdf (x, 5, 1))
***** assert (pdf ("Student T", x, 5), tpdf (x, 5))
***** assert (pdf ("t", x, 5), tpdf (x, 5))
***** assert (pdf ("location-scale T", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
***** assert (pdf ("tls", x, 5, 1, 2), tlspdf (x, 5, 1, 2))
***** assert (pdf ("Triangular", x, 5, 2, 2), tripdf (x, 5, 2, 2))
***** assert (pdf ("tri", x, 5, 2, 2), tripdf (x, 5, 2, 2))
***** assert (pdf ("Discrete Uniform", x, 5), unidpdf (x, 5))
***** assert (pdf ("unid", x, 5), unidpdf (x, 5))
***** assert (pdf ("Uniform", x, 5, 2), unifpdf (x, 5, 2))
***** assert (pdf ("unif", x, 5, 2), unifpdf (x, 5, 2))
***** assert (pdf ("Von Mises", x, 5, 2), vmpdf (x, 5, 2))
***** assert (pdf ("vm", x, 5, 2), vmpdf (x, 5, 2))
***** assert (pdf ("Weibull", x, 5, 2), wblpdf (x, 5, 2))
***** assert (pdf ("wbl", x, 5, 2), wblpdf (x, 5, 2))
***** error<pdf: distribution NAME must a char string.> pdf (1)
***** error<pdf: distribution NAME must a char string.> pdf ({"beta"})
***** error<pdf: X must be numeric.> pdf ("beta", {[1 2 3 4 5]})
***** error<pdf: X must be numeric.> pdf ("beta", "text")
***** error<pdf: values in X must be real.> pdf ("beta", 1+i)
***** error<pdf: distribution parameters must be numeric.> ...
 pdf ("Beta", x, "a", 2)
***** error<pdf: distribution parameters must be numeric.> ...
 pdf ("Beta", x, 5, "")
***** error<pdf: distribution parameters must be numeric.> ...
 pdf ("Beta", x, 5, {2})
***** error<pdf: chi2 distribution requires 1 parameter.> pdf ("chi2", x)
***** error<pdf: Beta distribution requires 2 parameters.> pdf ("Beta", x, 5)
***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5)
***** error<pdf: Burr distribution requires 3 parameters.> pdf ("Burr", x, 5, 2)
86 tests, 86 passed, 0 known failure, 0 skipped
[inst/dist_wrap/cdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_wrap/cdf.m
***** shared x
 x = [1:5];
***** assert (cdf ("Beta", x, 5, 2), betacdf (x, 5, 2))
***** assert (cdf ("beta", x, 5, 2, "upper"), betacdf (x, 5, 2, "upper"))
***** assert (cdf ("Binomial", x, 5, 2), binocdf (x, 5, 2))
***** assert (cdf ("bino", x, 5, 2, "upper"), binocdf (x, 5, 2, "upper"))
***** assert (cdf ("Birnbaum-Saunders", x, 5, 2), bisacdf (x, 5, 2))
***** assert (cdf ("bisa", x, 5, 2, "upper"), bisacdf (x, 5, 2, "upper"))
***** assert (cdf ("Burr", x, 5, 2, 2), burrcdf (x, 5, 2, 2))
***** assert (cdf ("burr", x, 5, 2, 2, "upper"), burrcdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Cauchy", x, 5, 2), cauchycdf (x, 5, 2))
***** assert (cdf ("cauchy", x, 5, 2, "upper"), cauchycdf (x, 5, 2, "upper"))
***** assert (cdf ("Chi-squared", x, 5), chi2cdf (x, 5))
***** assert (cdf ("chi2", x, 5, "upper"), chi2cdf (x, 5, "upper"))
***** assert (cdf ("Extreme Value", x, 5, 2), evcdf (x, 5, 2))
***** assert (cdf ("ev", x, 5, 2, "upper"), evcdf (x, 5, 2, "upper"))
***** assert (cdf ("Exponential", x, 5), expcdf (x, 5))
***** assert (cdf ("exp", x, 5, "upper"), expcdf (x, 5, "upper"))
***** assert (cdf ("F-Distribution", x, 5, 2), fcdf (x, 5, 2))
***** assert (cdf ("f", x, 5, 2, "upper"), fcdf (x, 5, 2, "upper"))
***** assert (cdf ("Gamma", x, 5, 2), gamcdf (x, 5, 2))
***** assert (cdf ("gam", x, 5, 2, "upper"), gamcdf (x, 5, 2, "upper"))
***** assert (cdf ("Geometric", x, 5), geocdf (x, 5))
***** assert (cdf ("geo", x, 5, "upper"), geocdf (x, 5, "upper"))
***** assert (cdf ("Generalized Extreme Value", x, 5, 2, 2), gevcdf (x, 5, 2, 2))
***** assert (cdf ("gev", x, 5, 2, 2, "upper"), gevcdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Generalized Pareto", x, 5, 2, 2), gpcdf (x, 5, 2, 2))
***** assert (cdf ("gp", x, 5, 2, 2, "upper"), gpcdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Gumbel", x, 5, 2), gumbelcdf (x, 5, 2))
***** assert (cdf ("gumbel", x, 5, 2, "upper"), gumbelcdf (x, 5, 2, "upper"))
***** assert (cdf ("Half-normal", x, 5, 2), hncdf (x, 5, 2))
***** assert (cdf ("hn", x, 5, 2, "upper"), hncdf (x, 5, 2, "upper"))
***** assert (cdf ("Hypergeometric", x, 5, 2, 2), hygecdf (x, 5, 2, 2))
***** assert (cdf ("hyge", x, 5, 2, 2, "upper"), hygecdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Inverse Gaussian", x, 5, 2), invgcdf (x, 5, 2))
***** assert (cdf ("invg", x, 5, 2, "upper"), invgcdf (x, 5, 2, "upper"))
***** assert (cdf ("Laplace", x, 5, 2), laplacecdf (x, 5, 2))
***** assert (cdf ("laplace", x, 5, 2, "upper"), laplacecdf (x, 5, 2, "upper"))
***** assert (cdf ("Logistic", x, 5, 2), logicdf (x, 5, 2))
***** assert (cdf ("logi", x, 5, 2, "upper"), logicdf (x, 5, 2, "upper"))
***** assert (cdf ("Log-Logistic", x, 5, 2), loglcdf (x, 5, 2))
***** assert (cdf ("logl", x, 5, 2, "upper"), loglcdf (x, 5, 2, "upper"))
***** assert (cdf ("Lognormal", x, 5, 2), logncdf (x, 5, 2))
***** assert (cdf ("logn", x, 5, 2, "upper"), logncdf (x, 5, 2, "upper"))
***** assert (cdf ("Nakagami", x, 5, 2), nakacdf (x, 5, 2))
***** assert (cdf ("naka", x, 5, 2, "upper"), nakacdf (x, 5, 2, "upper"))
***** assert (cdf ("Negative Binomial", x, 5, 2), nbincdf (x, 5, 2))
***** assert (cdf ("nbin", x, 5, 2, "upper"), nbincdf (x, 5, 2, "upper"))
***** assert (cdf ("Noncentral F-Distribution", x, 5, 2, 2), ncfcdf (x, 5, 2, 2))
***** assert (cdf ("ncf", x, 5, 2, 2, "upper"), ncfcdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Noncentral Student T", x, 5, 2), nctcdf (x, 5, 2))
***** assert (cdf ("nct", x, 5, 2, "upper"), nctcdf (x, 5, 2, "upper"))
***** assert (cdf ("Noncentral Chi-Squared", x, 5, 2), ncx2cdf (x, 5, 2))
***** assert (cdf ("ncx2", x, 5, 2, "upper"), ncx2cdf (x, 5, 2, "upper"))
***** assert (cdf ("Normal", x, 5, 2), normcdf (x, 5, 2))
***** assert (cdf ("norm", x, 5, 2, "upper"), normcdf (x, 5, 2, "upper"))
***** assert (cdf ("Poisson", x, 5), poisscdf (x, 5))
***** assert (cdf ("poiss", x, 5, "upper"), poisscdf (x, 5, "upper"))
***** assert (cdf ("Rayleigh", x, 5), raylcdf (x, 5))
***** assert (cdf ("rayl", x, 5, "upper"), raylcdf (x, 5, "upper"))
***** assert (cdf ("Rician", x, 5, 1), ricecdf (x, 5, 1))
***** assert (cdf ("rice", x, 5, 1, "upper"), ricecdf (x, 5, 1, "upper"))
***** assert (cdf ("Student T", x, 5), tcdf (x, 5))
***** assert (cdf ("t", x, 5, "upper"), tcdf (x, 5, "upper"))
***** assert (cdf ("location-scale T", x, 5, 1, 2), tlscdf (x, 5, 1, 2))
***** assert (cdf ("tls", x, 5, 1, 2, "upper"), tlscdf (x, 5, 1, 2, "upper"))
***** assert (cdf ("Triangular", x, 5, 2, 2), tricdf (x, 5, 2, 2))
***** assert (cdf ("tri", x, 5, 2, 2, "upper"), tricdf (x, 5, 2, 2, "upper"))
***** assert (cdf ("Discrete Uniform", x, 5), unidcdf (x, 5))
***** assert (cdf ("unid", x, 5, "upper"), unidcdf (x, 5, "upper"))
***** assert (cdf ("Uniform", x, 5, 2), unifcdf (x, 5, 2))
***** assert (cdf ("unif", x, 5, 2, "upper"), unifcdf (x, 5, 2, "upper"))
***** assert (cdf ("Von Mises", x, 5, 2), vmcdf (x, 5, 2))
***** assert (cdf ("vm", x, 5, 2, "upper"), vmcdf (x, 5, 2, "upper"))
***** assert (cdf ("Weibull", x, 5, 2), wblcdf (x, 5, 2))
***** assert (cdf ("wbl", x, 5, 2, "upper"), wblcdf (x, 5, 2, "upper"))
***** error<cdf: distribution NAME must be a char string.> cdf (1)
***** error<cdf: distribution NAME must be a char string.> cdf ({"beta"})
***** error<cdf: X must be numeric.> cdf ("beta", {[1 2 3 4 5]})
***** error<cdf: X must be numeric.> cdf ("beta", "text")
***** error<cdf: values in X must be real.> cdf ("beta", 1+i)
***** error<cdf: distribution parameters must be numeric.> ...
 cdf ("Beta", x, "a", 2)
***** error<cdf: distribution parameters must be numeric.> ...
 cdf ("Beta", x, 5, "")
***** error<cdf: distribution parameters must be numeric.> ...
 cdf ("Beta", x, 5, {2})
***** error<cdf: chi2 distribution requires 1 parameter.> cdf ("chi2", x)
***** error<cdf: Beta distribution requires 2 parameters.> cdf ("Beta", x, 5)
***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5)
***** error<cdf: Burr distribution requires 3 parameters.> cdf ("Burr", x, 5, 2)
86 tests, 86 passed, 0 known failure, 0 skipped
[inst/bar3h.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/bar3h.m
***** demo
 ## Plotting 5 bars in the same series.

 y = [50; 40; 30; 20; 10];
 bar3h (y);
***** demo
 ## Plotting 5 bars in different groups.

 y = [50, 40, 30, 20, 10];
 bar3h (y);
***** demo
 ## A 3D bar graph with each series corresponding to a column in y.

 y = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
 bar3h (y);
***** demo
 ## Specify z-axis locations as tick names. z must be a column vector!

 z = [1950, 1960, 1970, 1980, 1990]';
 y = [16, 8, 4, 2, 1]';
 bar3h (z, y);
***** demo
 ## Plot 3 series as a grouped plot without any space between the grouped bars

 y = [70 50 33 10; 75 55 35 15; 80 60 40 20];
 bar3h (y, 1, 'grouped');
***** demo
 ## Plot a stacked style 3D bar graph

 y = [19, 30, 21, 30; 40, 16, 32, 12];
 b = bar3h (y, 0.5, 'stacked');
***** error <bar3h: Z must be numeric.> bar3h ("A")
***** error <bar3h: Z must be numeric.> bar3h ({2,3,4,5})
***** error <bar3h: inconsistent size in Y and Z input arguments.> ...
 bar3h ([1,2,3]', ones (2))
***** error <bar3h: WIDTH must be a scalar in the range> ...
 bar3h ([1:5], 1.2)
***** error <bar3h: WIDTH must be a scalar in the range> ...
 bar3h ([1:5]', ones (5), 1.2)
***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
 bar3h ([1:5]', ones (5), [0.8, 0.7])
***** error <bar3h: missing value for optional argument 'width'.> ...
 bar3h (ones (5), 'width')
***** error <bar3h: invalid value for optional argument 'width'.> ...
 bar3h (ones (5), 'width', 1.2)
***** error <bar3h: invalid value for optional argument 'width'.> ...
 bar3h (ones (5), 'width', [0.8, 0.8, 0.8])
***** error <bar3h: missing value for optional argument 'color'.> ...
 bar3h (ones (5), 'color')
***** error <bar3h: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
 bar3h (ones (5), 'color', [0.8, 0.8])
***** error <bar3h: invalid value for optional argument 'color'.> ...
 bar3h (ones (5), 'color', "brown")
***** error <bar3h: invalid value for optional argument 'color'.> ...
 bar3h (ones (5), 'color', {"r", "k", "c", "m", "brown"})
***** error <bar3h: missing value for optional argument 'xlabel'.> ...
 bar3h (ones (5), 'xlabel')
***** error <bar3h: invalid value for optional argument 'xlabel'.> ...
 bar3h (ones (5), 'xlabel', 4)
***** error <bar3h: missing value for optional argument 'zlabel'.> ...
 bar3h (ones (5), 'zlabel')
***** error <bar3h: invalid value for optional argument 'zlabel'.> ...
 bar3h (ones (5), 'zlabel', 4)
***** error <bar3h: invalid optional argument.> bar3h (ones (5), 'this', 4)
***** error <bar3h: the elements in 'xlabel' must equal the columns in Z.> ...
 bar3h (ones (5), 'xlabel', {"A", "B", "C"})
***** error <bar3h: the elements in 'zlabel' must equal the rows in Z.> ...
 bar3h (ones (5), 'zlabel', {"A", "B", "C"})
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/bartlett_test.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/bartlett_test.m
***** error<bartlett_test: invalid number of input arguments.> bartlett_test ()
***** error<bartlett_test: invalid number of input arguments.> ...
 bartlett_test (1, 2, 3, 4);
***** error<bartlett_test: wrong value for alpha.> bartlett_test (randn (50, 2), 0);
***** error<bartlett_test: GROUP and X mismatch.> ...
 bartlett_test (randn (50, 2), [1, 2, 3]);
***** error<bartlett_test: GROUP and X mismatch.> ...
 bartlett_test (randn (50, 1), ones (55, 1));
***** error<bartlett_test: invalid second input argument.> ...
 bartlett_test (randn (50, 1), ones (50, 2));
***** error<bartlett_test: wrong value for alpha.> ...
 bartlett_test (randn (50, 2), [], 1.2);
***** error<bartlett_test: wrong value for alpha.> ...
 bartlett_test (randn (50, 2), [], "alpha");
***** error<bartlett_test: wrong value for alpha.> ...
 bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
***** error<bartlett_test: wrong value for alpha.> ...
 bartlett_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
***** warning<bartlett_test: GROUP> ...
 bartlett_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
***** test
 load examgrades
 [h, pval, chisq, df] = bartlett_test (grades);
 assert (h, 1);
 assert (pval, 7.908647337018238e-08, 1e-14);
 assert (chisq, 38.73324, 1e-5);
 assert (df, 4);
***** test
 load examgrades
 [h, pval, chisq, df] = bartlett_test (grades(:,[2:4]));
 assert (h, 1);
 assert (pval, 0.01172, 1e-5);
 assert (chisq, 8.89274, 1e-5);
 assert (df, 2);
***** test
 load examgrades
 [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
 assert (h, 0);
 assert (pval, 0.88118, 1e-5);
 assert (chisq, 0.02234, 1e-5);
 assert (df, 1);
***** test
 load examgrades
 grades = [grades; nan(10, 5)];
 [h, pval, chisq, df] = bartlett_test (grades(:,[1,4]));
 assert (h, 0);
 assert (pval, 0.88118, 1e-5);
 assert (chisq, 0.02234, 1e-5);
 assert (df, 1);
***** test
 load examgrades
 [h, pval, chisq, df] = bartlett_test (grades(:,[2,5]), 0.01);
 assert (h, 0);
 assert (pval, 0.01791, 1e-5);
 assert (chisq, 5.60486, 1e-5);
 assert (df, 1);
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/confusionmat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/confusionmat.m
***** test
 Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
 Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
 C  = [0 1 1 0 0 0 0 0; 0 0 0 0 1 0 0 0; 0 1 0 0 0 0 1 0; 0 0 0 1 0 1 0 0; ...
       0 0 0 0 3 0 0 0; 0 0 0 1 0 1 0 0; 0 0 0 0 0 0 0 0; 0 0 0 0 0 0 0 2];
 assert (confusionmat (Yt, Yp), C)
***** test
 g  = [1; 2; 3; 1];
 gh = [1; 2; 2; 1];
 [C, order] = confusionmat (g, gh);
 assert (C, [2 0 0; 0 1 0; 0 1 0]);
 assert (order, [1; 2; 3]);
***** test
 g  = [true; false; true; false];
 gh = [true; true;  false; false];
 [C, order] = confusionmat (g, gh);
 assert (C, [1 1; 1 1]);
 assert (order, [false; true]);
***** test
 g  = [1.1; 2.2; 1.1];
 gh = [1.1; 2.2; 2.2];
 [C, order] = confusionmat (g, gh);
 assert (C, [1 1; 0 1]);
 assert (order, [1.1; 2.2]);
***** test
 g  = [1; 2; NaN; 3];
 gh = [1; 1; 2;   3];
 [C, order] = confusionmat (g, gh);
 assert (C, [1 0 0; 1 0 0; 0 0 1]);
 assert (order, [1; 2; 3]);
***** error
 confusionmat ([], [])
***** test
 [C, order] = confusionmat (1, 1);
 assert (C, 1);
 assert (order, 1);
***** test
 g  = {'A'; ''; 'B'};
 gh = {'A'; 'B'; 'B'};
 [C, order] = confusionmat (g, gh);
 assert (C, [1 0; 0 1]);
 assert (order, {'A'; 'B'});
***** test
 g  = ['AA'; 'BB'; 'AA'; 'CC'];
 gh = ['AA'; 'BB'; 'BB'; 'CC'];
 [C, order] = confusionmat (g, gh);
 assert (C, [1 1 0; 0 1 0; 0 0 1]);
 assert (order, ['AA'; 'BB'; 'CC']);
***** test
 g  = char ('A', 'B', 'A');
 gh = char ('A', 'A', 'B');
 [C, order] = confusionmat (g, gh);
 assert (C, [1 1; 1 0]);
 assert (order, char ('A', 'B'));
***** test
 g  = {'Cat'; 'Dog'; 'Cat'; 'Bird'};
 gh = {'Cat'; 'Cat'; 'Bird'; 'Bird'};
 [C, order] = confusionmat (g, gh);
 assert (C, [1 0 1; 1 0 0; 0 0 1]);
 assert (order, {'Cat'; 'Dog'; 'Bird'});
***** test
 g  = ["Apple"; "Banana"; "Apple"];
 gh = ["Apple"; "Apple";  "Cherry"];
 [C, order] = confusionmat (g, gh);
 assert (C, [1 0 1; 1 0 0; 0 0 0]);
 assert (order, ["Apple"; "Banana"; "Cherry"]);
***** test
 g = string ({"A"; "B"; "B"});
 g(2) = missing;
 gh = string(["A"; "B"; "B"]);
 [C, order] = confusionmat (g, gh);
 assert (C, [1 0; 0 1]);
 assert (isequal (order, string(["A"; "B"])));
***** test
 g  = categorical ({'Small', 'Medium', 'Large'});
 gh = categorical ({'Small', 'Large',  'Large'});
 [C, order] = confusionmat (g, gh);
 assert (C, [1 0 0; 1 0 0; 0 0 1]);
 assert (cellstr (char (order)), {'Large'; 'Medium'; 'Small'});
***** test
 g = categorical ({'Red', 'Blue', 'Red'});
 g(2) = NaN;
 gh = categorical ({'Red', 'Blue', 'Red'});
 [C, order] = confusionmat (g, gh);
 assert (C, [0 0; 0 2]);
 assert (cellstr (char (order)), {'Blue'; 'Red'});
***** test
 vals = {'A', 'B', 'A'};
 cats = {'A', 'B', 'C'};
 g  = categorical (vals, cats);
 gh = categorical (vals, cats);
 [C, order] = confusionmat (g, gh);
 assert (size (C), [3 3]);
 assert (C(3,3), 0);
 assert (cellstr (char (order)), {'A'; 'B'; 'C'});
***** test
 g  = categorical ({'A'}, {'A', 'B'});
 gh = categorical ({'A'}, {'A', 'C'});
 [C, order] = confusionmat (g, gh);
 assert (size (C), [3 3]);
 assert (cellstr (char (order)), {'A'; 'B'; 'C'});
***** test
 g  = [1, 2, 3];
 gh = [1; 2; 3];
 [C, order] = confusionmat (g, gh);
 assert (C, eye(3));
 assert (order, [1; 2; 3]);
***** test
 g  = [1; 2; 3];
 gh = [1; 2; 3];
 myOrder = [3; 2; 1];
 [C, order] = confusionmat (g, gh, "Order", myOrder);
 assert (C, [1 0 0; 0 1 0; 0 0 1]);
 assert (order, [3; 2; 1]);
***** test
 g  = {'A'; 'B'};
 gh = {'A'; 'B'};
 [C, order] = confusionmat (g, gh, "Order", {'B'; 'A'});
 assert (C, [1 0; 0 1]);
 assert (order, {'B'; 'A'});
***** test
 g  = [1; 2; 3];
 gh = [1; 2; 3];
 [C, order] = confusionmat (g, gh, "Order", [1; 2]);
 assert (C, eye(2));
 assert (order, [1; 2]);
***** test
 g  = [1; 2];
 gh = [1; 2];
 [C, order] = confusionmat (g, gh, "Order", [1; 2; 4]);
 assert (C, [1 0 0; 0 1 0; 0 0 0]);
 assert (order, [1; 2; 4]);
***** test
 g  = [1; 1; 1];
 gh = [2; 2; 2];
 [C, order] = confusionmat (g, gh);
 assert (C, [0 3; 0 0]);
 assert (order, [1; 2]);
***** test
 g  = [1; 1; 1];
 gh = [1; 1; 1];
 [C, order] = confusionmat (g, gh);
 assert (C, 3);
 assert (order, 1);
***** error <confusionmat: group and grouphat must be of the same data type.>
 confusionmat ([1; 2], {'A'; 'B'})
***** error <confusionmat: group and grouphat must be of the same data type.>
 confusionmat ('A', [1])
***** error <confusionmat: group and grouphat must be of the same length.>
 confusionmat ([1; 2; 3], [1; 2])
***** error <confusionmat: group and grouporder must be of the same data type.>
 confusionmat ([1; 2], [1; 2], "Order", {'A'; 'B'})
***** error <confusionmat: group and grouporder must be of the same data type.>
 confusionmat ({'A'}, {'A'}, "Order", [1])
***** error <confusionmat: group must be a vector or character array.>
 confusionmat (eye(2), eye(2))
***** error <confusionmat: invalid data type.> confusionmat ({1; 2}, {1; 2})
31 tests, 31 passed, 0 known failure, 0 skipped
[inst/loadmodel.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/loadmodel.m
***** error<loadmodel: too few arguments.> loadmodel ()
***** error<loadmodel: 'fisheriris.mat' does not contain a Classification or Regression object.> ...
 loadmodel ("fisheriris.mat")
***** error<loadmodel: 'ClassificationModel' is not supported.> ...
 loadmodel ("fail_loadmodel.mdl")
***** error<ClassificationKNN.load_model: invalid model in 'fail_load_model.mdl'.> ...
 loadmodel ("fail_load_model.mdl")
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/dendrogram.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dendrogram.m
***** demo
 ## simple dendrogram
 y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
 y(:,3) = 1:5;
 dendrogram (y);
 title ("simple dendrogram");
***** demo
 ## another simple dendrogram
 v = 2 * rand (30, 1) - 1;
 d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
 y = linkage (squareform (d, "tovector"));
 dendrogram (y);
 title ("another simple dendrogram");
***** demo
 ## collapsed tree, find all the leaves of node 5
 X = randn (60, 2);
 D = pdist (X);
 y = linkage (D, "average");
 subplot (2, 1, 1);
 title ("original tree");
 dendrogram (y, 0);
 subplot (2, 1, 2);
 title ("collapsed tree");
 [~, t] = dendrogram (y, 20);
 find(t == 5)
***** demo
 ## optimal leaf order
 X = randn (30, 2);
 D = pdist (X);
 y = linkage (D, "average");
 order = optimalleaforder (y, D);
 subplot (2, 1, 1);
 title ("original leaf order");
 dendrogram (y);
 subplot (2, 1, 2);
 title ("optimal leaf order");
 dendrogram (y, "Reorder", order);
***** demo
 ## horizontal orientation and labels
 X = randn (8, 2);
 D = pdist (X);
 L = ["Snow White"; "Doc"; "Grumpy"; "Happy"; "Sleepy"; "Bashful"; ...
      "Sneezy"; "Dopey"];
 y = linkage (D, "average");
 dendrogram (y, "Orientation", "left", "Labels", L);
 title ("horizontal orientation and labels");
***** shared visibility_setting
 visibility_setting = get (0, "DefaultFigureVisible");
***** test
 hf = figure ("visible", "off");
 unwind_protect
   y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
   y(:,3) = 1:5;
   dendrogram (y);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   y = [4, 5; 2, 6; 3, 7; 8, 9; 1, 10];
   y(:,3) = 1:5;
   dendrogram (y);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   v = 2 * rand (30, 1) - 1;
   d = abs (bsxfun (@minus, v(:, 1), v(:, 1)'));
   y = linkage (squareform (d, "tovector"));
   dendrogram (y);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   X = randn (30, 2);
   D = pdist (X);
   y = linkage (D, "average");
   order = optimalleaforder (y, D);
   subplot (2, 1, 1);
   title ("original leaf order");
   dendrogram (y);
   subplot (2, 1, 2);
   title ("optimal leaf order");
   dendrogram (y, "Reorder", order);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error dendrogram ();
***** error <tree must be .*> dendrogram (ones (2, 2), 1);
***** error <unknown property .*> dendrogram ([1 2 1], 1, "xxx", "xxx");
***** error <reorder.*> dendrogram ([1 2 1], "Reorder", "xxx");
***** error <reorder.*> dendrogram ([1 2 1], "Reorder", [1 2 3 4]);
 fail ('dendrogram ([1 2 1], "Orientation", "north")', "invalid orientation .*")
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/gmdistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/gmdistribution.m
***** test
 mu = eye(2);
 Sigma = eye(2);
 GM = gmdistribution (mu, Sigma);
 density = GM.pdf ([0 0; 1 1]);
 assert (density(1) - density(2), 0, 1e-6);

 [idx, nlogl, P, logpdf,M] = cluster (GM, eye(2));
 assert (idx, [1; 2]);
 [idx2,nlogl2,P2,logpdf2] = GM.cluster (eye(2));
 assert (nlogl - nlogl2, 0, 1e-6);
 [idx3,nlogl3,P3] = cluster (GM, eye(2));
 assert (P - P3, zeros (2), 1e-6);
 [idx4,nlogl4] = cluster (GM, eye(2));
 assert (size (nlogl4), [1 1]);
 idx5 = cluster (GM, eye(2));
 assert (idx - idx5, zeros (2,1));

 D = GM.mahal ([1;0]);
 assert (D - M(1,:), zeros (1,2), 1e-6);

 P = GM.posterior ([0 1]);
 assert (P - P2(2,:), zeros (1,2), 1e-6);

 R = GM.random(20);
 assert (size(R), [20, 2]);

 R = GM.random();
 assert (size(R), [1, 2]);
1 test, 1 passed, 0 known failure, 0 skipped
[inst/glmfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/glmfit.m
***** demo
 x = [210, 230, 250, 270, 290, 310, 330, 350, 370, 390, 410, 430]';
 n = [48, 42, 31, 34, 31, 21, 23, 23, 21, 16, 17, 21]';
 y = [1, 2, 0, 3, 8, 8, 14, 17, 19, 15, 17, 21]';
 b = glmfit (x, [y n], "binomial", "Link", "probit");
 yfit = glmval (b, x, "probit", "Size", n);
 plot (x, y./n, 'o', x, yfit ./ n, '-')
***** demo
 load fisheriris
 X = meas (51:end, :);
 y = strcmp ("versicolor", species(51:end));
 b = glmfit (X, y, "binomial", "link", "logit")
***** test
 load fisheriris;
 X = meas(51:end,:);
 y = strcmp ("versicolor", species(51:end));
 b = glmfit (X, y, "binomial", "link", "logit");
 assert (b, [42.6379; 2.4652; 6.6809; -9.4294; -18.2861], 1e-4);
***** test
 X = [1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0, 10.1]';
 y = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4]';
 [Bnew, dev] = glmfit (X, y, "gamma", "link", "log");
 b_matlab = [-0.7631; 0.1113];
 dev_matlab = 0.0111;
 assert (Bnew, b_matlab, 0.001);
 assert (dev, dev_matlab, 0.001);
***** test
 X = [1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0, 10.1]';
 y = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4]';
 p_input = 1;
 [Bnew, dev] = glmfit (X, y, "inverse gaussian", "link", p_input);
 b_matlab = [0.3813; 0.0950];
 dev_matlab = 0.0051;
 assert (Bnew, b_matlab, 0.001);
 assert (dev, dev_matlab, 0.001);
***** error <glmfit: too few input arguments.> glmfit ()
***** error <glmfit: too few input arguments.> glmfit (1)
***** error <glmfit: too few input arguments.> glmfit (1, 2)
***** error <glmfit: Name-Value arguments must be in pairs.> ...
 glmfit (rand (6, 1), rand (6, 1), 'poisson', 'link')
***** error <glmfit: X must be a numeric matrix.> ...
 glmfit ('abc', rand (6, 1), 'poisson')
***** error <glmfit: X must be a numeric matrix.> ...
 glmfit ([], rand (6, 1), 'poisson')
***** error <glmfit: Y must be either a numeric matrix or a logical vector.> ...
 glmfit (rand (5, 2), 'abc', 'poisson')
***** error <glmfit: Y must be either a numeric matrix or a logical vector.> ...
 glmfit (rand (5, 2), [], 'poisson')
***** error <glmfit: X and Y must have the same number of observations.> ...
 glmfit (rand (5, 2), rand (6, 1), 'poisson')
***** error <glmfit: DISTRIBUTION must be a character vector.> ...
 glmfit (rand (6, 2), rand (6, 1), 3)
***** error <glmfit: DISTRIBUTION must be a character vector.> ...
 glmfit (rand (6, 2), rand (6, 1), {'poisson'})
***** error <glmfit: for a 'binomial' distribution, Y must be an n-by-1 or n-by-2 matrix.> ...
 glmfit (rand (5, 2), rand (5, 3), 'binomial')
***** error <glmfit: n-by-2 matrix Y for 'binomial' distribution must be numeric.> ...
 glmfit (rand (2, 2), [true, true; false, false], 'binomial')
***** error <glmfit: for distributions other than 'binomial', Y must be an n-by-1 column vector> ...
 glmfit (rand (5, 2), rand (5, 2), 'normal')
***** error <glmfit: unsupported distribution.> ...
 glmfit (rand (5, 2), rand (5, 1), 'chebychev')
***** error <glmfit: 'B0' must be a numeric vector of the same size as Y.> ...
 glmfit (rand (5, 2), rand (5, 1), 'normal', 'B0', [1; 2; 3; 4])
***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
 glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', 1)
***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
 glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', 'o')
***** error <glmfit: 'Constant' should be either 'on' or 'off'.> ...
 glmfit (rand (5, 2), rand (5, 1), 'normal', 'constant', true)
***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
 glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', 1)
***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
 glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', 'o')
***** error <glmfit: 'EstDisp' should be either 'on' or 'off'.> ...
 glmfit (rand (5, 2), rand (5, 1), 'normal', 'estdisp', true)
***** error <glmfit: structure with custom link functions must be a scalar.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", {1, 2}))
***** error <glmfit: structure with custom link functions requires the fields 'Link', 'Derivative', and 'Inverse'.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "norminv"))
***** error <glmfit: bad 'Link' function in custom link function structure.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "some", "Derivative", @(x)x, "Inverse", "normcdf"))
***** error <glmfit: bad 'Link' function in custom link function structure.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", 1, "Derivative", @(x)x, "Inverse", "normcdf"))
***** error <glmfit: custom 'Link' function must return an output of the same size as input.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x) [x, x], "Derivative", @(x)x, "Inverse", "normcdf"))
***** error <glmfit: invalid custom 'Link' function.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", "what", "Derivative", @(x)x, "Inverse", "normcdf"))
***** error <glmfit: bad 'Derivative' function in custom link function structure.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "some", "Inverse", "normcdf"))
***** error <glmfit: bad 'Derivative' function in custom link function structure.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", 1, "Inverse", "normcdf"))
***** error <glmfit: custom 'Derivative' function must return an output of the same size as input.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", @(x) [x, x], "Inverse", "normcdf"))
***** error <glmfit: invalid custom 'Derivative' function.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "what", "Inverse", "normcdf"))
***** error <glmfit: bad 'Inverse' function in custom link function structure.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "some"))
***** error <glmfit: bad 'Inverse' function in custom link function structure.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", 1))
***** error <glmfit: custom 'Inverse' function must return an output of the same size as input.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", @(x) [x, x]))
***** error <glmfit: invalid custom 'Inverse' function.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "what"))
***** error <glmfit: cell array with custom link functions must have three elements.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log'})
***** error <glmfit: cell array with custom link functions must have three elements.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {'log', 'hijy'})
***** error <glmfit: cell array with custom link functions must have three elements.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {1, 2, 3, 4})
***** error <glmfit: bad 'Link' function in custom link function cell array.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {"log", "dfv", "dfgvd"})
***** error <glmfit: custom 'Link' function must return an output of the same size as input.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) [x, x], "dfv", "dfgvd"})
***** error <glmfit: invalid custom 'Link' function.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) what (x), "dfv", "dfgvd"})
***** error <glmfit: bad 'Derivative' function in custom link function cell array.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, "dfv", "dfgvd"})
***** error <glmfit: custom 'Derivative' function must return an output of the same size as input.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) [x, x], "dfgvd"})
***** error <glmfit: invalid custom 'Derivative' function.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) what (x), "dfgvd"})
***** error <glmfit: bad 'Inverse' function in custom link function cell array.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, "dfgvd"})
***** error <glmfit: custom 'Inverse' function must return an output of the same size as input.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, @(x) [x, x]})
***** error <glmfit: invalid custom 'Inverse' function.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', {@(x) x, @(x) x, @(x) what (x)})
***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', NaN)
***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', [1, 2])
***** error <glmfit: numeric input for custom link function must be a finite real scalar value.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', [1i])
***** error <glmfit: canonical link function name must be a character vector.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', ["log"; "log1"])
***** error <glmfit: canonical link function 'somelinkfunction' is not supported.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', 'somelinkfunction')
***** error <glmfit: invalid value for custom link function.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'link', true)
***** error <glmfit: 'Options' must be a structure containing the fields 'MaxIter', and 'TolX'.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'options', true)
***** error <glmfit: 'Options' must be a structure containing the fields 'MaxIter', and 'TolX'.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100))
***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 4.5, "TolX", 1e-6))
***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 0, "TolX", 1e-6))
***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", -100, "TolX", 1e-6))
***** error <glmfit: 'MaxIter' in 'Options' structure must be a positive integer.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", [50 ,50], "TolX", 1e-6))
***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", 0))
***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", -1e-6))
***** error <glmfit: 'TolX' in 'Options' structure must be a positive scalar.> ...
 glmfit (rand(5,2), rand(5,1), 'poisson', 'options', struct ("MaxIter", 100, "TolX", [1e-6, 1e-6]))
***** error <glmfit: 'Offset' must be a numeric vector of the same size as Y.> ...
 glmfit (rand (5, 2), rand (5, 1), 'normal', 'offset', [1; 2; 3; 4])
***** error <glmfit: 'Offset' must be a numeric vector of the same size as Y.> ...
 glmfit (rand (5, 2), rand (5, 1), 'normal', 'offset', 'asdfg')
***** error <glmfit: 'Weights' must be a numeric vector of the same size as Y.> ...
 glmfit (rand (5, 2), rand (5, 1), 'normal', 'weights', [1; 2; 3; 4])
***** error <glmfit: 'Weights' must be a numeric vector of the same size as Y.> ...
 glmfit (rand (5, 2), rand (5, 1), 'normal', 'weights', 'asdfg')
70 tests, 70 passed, 0 known failure, 0 skipped
[inst/slicesample.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/slicesample.m
***** demo
 ## Define function to sample
 d = 2;
 mu = [-1; 2];
 rand ("seed", 5)  # for reproducibility
 Sigma = rand (d);
 Sigma = (Sigma + Sigma');
 Sigma += eye (d)*abs (eigs (Sigma, 1, "sa")) * 1.1;
 pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));

 ## Inputs
 start = ones (1,2);
 nsamples = 500;
 K = 500;
 m = 10;
 rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
 [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "burnin", K, "thin", m, "width", [20, 30]);
 figure;
 hold on;
 plot (smpl(:,1), smpl(:,2), 'x');
 [x, y] = meshgrid (linspace (-6,4), linspace(-3,7));
 z = reshape (pdf ([x(:), y(:)]), size(x));
 mesh (x, y, z, "facecolor", "None");

 ## Using sample points to find the volume of half a sphere with radius of .5
 f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
 int = mean (f (smpl) ./ pdf (smpl));
 errest = std (f (smpl) ./ pdf (smpl)) / nsamples^.5;
 trueerr = abs (2/3*pi*.25^(3/2)-int);
 fprintf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
 fprintf ("Monte Carlo integral error estimate %f\n", errest);
 fprintf ("The actual error %f\n", trueerr);
 mesh (x,y,reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
***** demo
 ## Integrate truncated normal distribution to find normalization constant
 pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
 nsamples = 1e3;
 rande ("seed", 4);  rand ("seed", 5)  # for reproducibility
 [smpl, accept] = slicesample (1, nsamples, "pdf", pdf, "thin", 4);
 f = @(x) exp (-.5 * x .^ 2) .* (x >= -2 & x <= 2);
 x = linspace (-3, 3, 1000);
 area (x, f(x));
 xlabel ("x");
 ylabel ("f(x)");
 int = mean (f (smpl) ./ pdf (smpl));
 errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ 0.5;
 trueerr = abs (erf (2 ^ 0.5) * 2 ^ 0.5 * pi ^ 0.5 - int);
 fprintf("Monte Carlo integral estimate int f(x) dx = %f\n", int);
 fprintf("Monte Carlo integral error estimate %f\n", errest);
 fprintf("The actual error %f\n", trueerr);
***** test
 start = 0.5;
 nsamples = 1e3;
 pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
 [smpl, accept] = slicesample (start, nsamples, "pdf", pdf, "thin", 2, "burnin", 0, "width", 5);
 assert (mean (smpl, 1), 1, .15);
 assert (var (smpl, 1), 1, .25);
***** error slicesample ();
***** error slicesample (1);
***** error slicesample (1, 1);
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/cdfplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/cdfplot.m
***** demo
 x = randn(100,1);
 cdfplot (x);
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
   [hCDF, stats] = cdfplot (x);
   assert (stats.min, 2);
   assert (stats.max, 6);
   assert (stats.median, 3.5);
   assert (stats.std, 1.35400640077266, 1e-14);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = randn(100,1);
   cdfplot (x);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error cdfplot ();
***** error cdfplot ([x',x']);
***** error cdfplot ([NaN, NaN, NaN, NaN]);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/bar3.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/bar3.m
***** demo
 ## Plotting 5 bars in the same series.

 z = [50; 40; 30; 20; 10];
 bar3 (z);
***** demo
 ## Plotting 5 bars in different groups.

 z = [50, 40, 30, 20, 10];
 bar3 (z);
***** demo
 ## A 3D bar graph with each series corresponding to a column in z.

 z = [1, 4, 7; 2, 5, 8; 3, 6, 9; 4, 7, 10];
 bar3 (z);
***** demo
 ## Specify y-axis locations as tick names. y must be a column vector!

 y = [1950, 1960, 1970, 1980, 1990]';
 z = [16, 8, 4, 2, 1]';
 bar3 (y, z);
***** demo
 ## Plot 3 series as a grouped plot without any space between the grouped bars

 z = [70 50 33 10; 75 55 35 15; 80 60 40 20];
 bar3 (z, 1, 'grouped');
***** demo
 ## Plot a stacked style 3D bar graph

 z = [19, 30, 21, 30; 40, 16, 32, 12];
 b = bar3 (z, 0.5, 'stacked');
***** error <bar3: Z must be numeric.> bar3 ("A")
***** error <bar3: Z must be numeric.> bar3 ({2,3,4,5})
***** error <bar3: inconsistent size in Y and Z input arguments.> ...
 bar3 ([1,2,3]', ones (2))
***** error <bar3: WIDTH must be a scalar in the range> ...
 bar3 ([1:5], 1.2)
***** error <bar3: WIDTH must be a scalar in the range> ...
 bar3 ([1:5]', ones (5), 1.2)
***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
 bar3 ([1:5]', ones (5), [0.8, 0.7])
***** error <bar3: missing value for optional argument 'width'.> ...
 bar3 (ones (5), 'width')
***** error <bar3: invalid value for optional argument 'width'.> ...
 bar3 (ones (5), 'width', 1.2)
***** error <bar3: invalid value for optional argument 'width'.> ...
 bar3 (ones (5), 'width', [0.8, 0.8, 0.8])
***** error <bar3: missing value for optional argument 'color'.> ...
 bar3 (ones (5), 'color')
***** error <bar3: numeric COLOR must be a 1x3 vector of an Nx3 matrix> ...
 bar3 (ones (5), 'color', [0.8, 0.8])
***** error <bar3: invalid value for optional argument 'color'.> ...
 bar3 (ones (5), 'color', "brown")
***** error <bar3: invalid value for optional argument 'color'.> ...
 bar3 (ones (5), 'color', {"r", "k", "c", "m", "brown"})
***** error <bar3: missing value for optional argument 'xlabel'.> ...
 bar3 (ones (5), 'xlabel')
***** error <bar3: invalid value for optional argument 'xlabel'.> ...
 bar3 (ones (5), 'xlabel', 4)
***** error <bar3: missing value for optional argument 'ylabel'.> ...
 bar3 (ones (5), 'ylabel')
***** error <bar3: invalid value for optional argument 'ylabel'.> ...
 bar3 (ones (5), 'ylabel', 4)
***** error <bar3: invalid optional argument.> bar3 (ones (5), 'this', 4)
***** error <bar3: the elements in 'xlabel' must equal the columns in Z.> ...
 bar3 (ones (5), 'xlabel', {"A", "B", "C"})
***** error <bar3: the elements in 'ylabel' must equal the rows in Z.> ...
 bar3 (ones (5), 'ylabel', {"A", "B", "C"})
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/manova1.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/manova1.m
***** demo
 load carbig
 [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin)
***** test
 load carbig
 [d,p] = manova1([MPG, Acceleration, Weight, Displacement], Origin);
 assert (d, 3);
 assert (p, [0, 3.140583347827075e-07, 0.007510999577743149, ...
             0.1934100745898493]', [1e-12, 1e-12, 1e-12, 1e-12]');
***** test
 load carbig
 [d,p] = manova1([MPG, Acceleration, Weight], Origin);
 assert (d, 2);
 assert (p, [0, 0.00516082975137544, 0.1206528056514453]', ...
            [1e-12, 1e-12, 1e-12]');
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/multcompare.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/multcompare.m
***** demo

 ## Demonstration using balanced one-way ANOVA from anova1

 x = ones (50, 4) .* [-2, 0, 1, 5];
 randn ("seed", 1);    # for reproducibility
 x = x + normrnd (0, 2, 50, 4);
 groups = {"A", "B", "C", "D"};
 [p, tbl, stats] = anova1 (x, groups, "off");
 multcompare (stats);
***** demo

 ## Demonstration using unbalanced one-way ANOVA example from anovan

 dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
      4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';

 [P,ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
                                  "ControlGroup", 1, "display", "on")

***** demo

 ## Demonstration using factorial ANCOVA example from anovan

 score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
 97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
 81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
 84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
 100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
 91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
 treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
 exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
             "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
             "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
             "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
             "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
             "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
 age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
 58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
 75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';

 [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, "model", ...
                            [1 0 0; 0 1 0; 0 0 1; 1 1 0], "continuous", 3, ...
                            "sstype", "h", "display", "off", "contrasts", ...
                            {"simple","poly",""});

 [C, M, H, GNAMES] = multcompare (STATS, "dim", [1 2], "ctype", "holm", ...
                                  "display", "on")

***** demo

 ## Demonstration using one-way ANOVA from anovan, with fit by weighted least
 ## squares to account for heteroskedasticity.

 g = [1, 1, 1, 1, 1, 1, 1, 1, ...
      2, 2, 2, 2, 2, 2, 2, 2, ...
      3, 3, 3, 3, 3, 3, 3, 3]';

 y = [13, 16, 16,  7, 11,  5,  1,  9, ...
      10, 25, 66, 43, 47, 56,  6, 39, ...
      11, 39, 26, 35, 25, 14, 24, 17]';

 [P,ATAB,STATS] = anovan(y, g, "display", "off");
 fitted = STATS.X * STATS.coeffs(:,1); # fitted values
 b = polyfit (fitted, abs (STATS.resid), 1);
 v = polyval (b, fitted);  # Variance as a function of the fitted values
 [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1, "display", "off");
 [C, M] =  multcompare (STATS, "display", "on", "ctype", "mvt")
***** demo

 ## Demonstration of p-value adjustments to control the false discovery rate
 ## Data from Westfall (1997) JASA. 92(437):299-306

 p = [.005708; .023544; .024193; .044895; ...
       .048805; .221227; .395867; .693051; .775755];

 padj = multcompare(p,'ctype','fdr')
***** test

 ## Tests using unbalanced one-way ANOVA example from anovan and anova1

 ## Test for anovan - compare pairwise comparisons with matlab for CTYPE "lsd"

 dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
      4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';

 [P, ATAB, STATS] = anovan (dv, g, "varnames", "score", "display", "off");
 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "lsd", ...
                                  "display", "off");
 assert (C(1,6), 2.85812420217898e-05, 1e-09);
 assert (C(2,6), 5.22936741204085e-07, 1e-09);
 assert (C(3,6), 2.12794763209146e-08, 1e-09);
 assert (C(4,6), 7.82091664406946e-15, 1e-09);
 assert (C(5,6), 0.546591417210693, 1e-09);
 assert (C(6,6), 0.0845897945254446, 1e-09);
 assert (C(7,6), 9.47436557975328e-08, 1e-09);
 assert (C(8,6), 0.188873478781067, 1e-09);
 assert (C(9,6), 4.08974010364197e-08, 1e-09);
 assert (C(10,6), 4.44427348175241e-06, 1e-09);
 assert (M(1,1), 10, 1e-09);
 assert (M(2,1), 18, 1e-09);
 assert (M(3,1), 19, 1e-09);
 assert (M(4,1), 21.0001428571429, 1e-09);
 assert (M(5,1), 29.0001111111111, 1e-09);
 assert (M(1,2), 1.0177537954095, 1e-09);
 assert (M(2,2), 1.28736803631001, 1e-09);
 assert (M(3,2), 1.0177537954095, 1e-09);
 assert (M(4,2), 1.0880245732889, 1e-09);
 assert (M(5,2), 0.959547480416536, 1e-09);

 ## Compare "fdr" adjusted p-values to those obtained using p.adjust in R

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "fdr", ...
                                  "display", "off");
 assert (C(1,6), 4.08303457454140e-05, 1e-09);
 assert (C(2,6), 1.04587348240817e-06, 1e-09);
 assert (C(3,6), 1.06397381604573e-07, 1e-09);
 assert (C(4,6), 7.82091664406946e-14, 1e-09);
 assert (C(5,6), 5.46591417210693e-01, 1e-09);
 assert (C(6,6), 1.05737243156806e-01, 1e-09);
 assert (C(7,6), 2.36859139493832e-07, 1e-09);
 assert (C(8,6), 2.09859420867852e-01, 1e-09);
 assert (C(9,6), 1.36324670121399e-07, 1e-09);
 assert (C(10,6), 7.40712246958735e-06, 1e-09);

 ## Compare "hochberg" adjusted p-values to those obtained using p.adjust in R

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "hochberg", ...
                                  "display", "off");
 assert (C(1,6), 1.14324968087159e-04, 1e-09);
 assert (C(2,6), 3.13762044722451e-06, 1e-09);
 assert (C(3,6), 1.91515286888231e-07, 1e-09);
 assert (C(4,6), 7.82091664406946e-14, 1e-09);
 assert (C(5,6), 5.46591417210693e-01, 1e-09);
 assert (C(6,6), 2.53769383576334e-01, 1e-09);
 assert (C(7,6), 6.63205590582730e-07, 1e-09);
 assert (C(8,6), 3.77746957562134e-01, 1e-09);
 assert (C(9,6), 3.27179208291358e-07, 1e-09);
 assert (C(10,6), 2.22213674087620e-05, 1e-09);

 ## Compare "holm" adjusted p-values to those obtained using p.adjust in R

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "holm", ...
                                  "display", "off");
 assert (C(1,6), 1.14324968087159e-04, 1e-09);
 assert (C(2,6), 3.13762044722451e-06, 1e-09);
 assert (C(3,6), 1.91515286888231e-07, 1e-09);
 assert (C(4,6), 7.82091664406946e-14, 1e-09);
 assert (C(5,6), 5.46591417210693e-01, 1e-09);
 assert (C(6,6), 2.53769383576334e-01, 1e-09);
 assert (C(7,6), 6.63205590582730e-07, 1e-09);
 assert (C(8,6), 3.77746957562134e-01, 1e-09);
 assert (C(9,6), 3.27179208291358e-07, 1e-09);
 assert (C(10,6), 2.22213674087620e-05, 1e-09);

 ## Compare "scheffe" adjusted p-values to those obtained using 'scheffe' in Matlab

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "scheffe", ...
                                  "display", "off");
 assert (C(1,6), 0.00108105386141085, 1e-09);
 assert (C(2,6), 2.7779386789517e-05, 1e-09);
 assert (C(3,6), 1.3599854038198e-06, 1e-09);
 assert (C(4,6), 7.58830197867751e-13, 1e-09);
 assert (C(5,6), 0.984039948220281, 1e-09);
 assert (C(6,6), 0.539077018557706, 1e-09);
 assert (C(7,6), 5.59475764460574e-06, 1e-09);
 assert (C(8,6), 0.771173490574105, 1e-09);
 assert (C(9,6), 2.52838425729905e-06, 1e-09);
 assert (C(10,6), 0.000200719143889168, 1e-09);

 ## Compare "bonferroni" adjusted p-values to those obtained using p.adjust in R

 [C, M, H, GNAMES] = multcompare (STATS, "dim", 1, "ctype", "bonferroni", ...
                                  "display", "off");
 assert (C(1,6), 2.85812420217898e-04, 1e-09);
 assert (C(2,6), 5.22936741204085e-06, 1e-09);
 assert (C(3,6), 2.12794763209146e-07, 1e-09);
 assert (C(4,6), 7.82091664406946e-14, 1e-09);
 assert (C(5,6), 1.00000000000000e+00, 1e-09);
 assert (C(6,6), 8.45897945254446e-01, 1e-09);
 assert (C(7,6), 9.47436557975328e-07, 1e-09);
 assert (C(8,6), 1.00000000000000e+00, 1e-09);
 assert (C(9,6), 4.08974010364197e-07, 1e-09);
 assert (C(10,6), 4.44427348175241e-05, 1e-09);

 ## Test for anova1 ("equal")- comparison of results from Matlab

 [P, ATAB, STATS] = anova1 (dv, g, "off", "equal");
 [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 2.85812420217898e-05, 1e-09);
 assert (C(2,6), 5.22936741204085e-07, 1e-09);
 assert (C(3,6), 2.12794763209146e-08, 1e-09);
 assert (C(4,6), 7.82091664406946e-15, 1e-09);
 assert (C(5,6), 0.546591417210693, 1e-09);
 assert (C(6,6), 0.0845897945254446, 1e-09);
 assert (C(7,6), 9.47436557975328e-08, 1e-09);
 assert (C(8,6), 0.188873478781067, 1e-09);
 assert (C(9,6), 4.08974010364197e-08, 1e-09);
 assert (C(10,6), 4.44427348175241e-06, 1e-09);
 assert (M(1,1), 10, 1e-09);
 assert (M(2,1), 18, 1e-09);
 assert (M(3,1), 19, 1e-09);
 assert (M(4,1), 21.0001428571429, 1e-09);
 assert (M(5,1), 29.0001111111111, 1e-09);
 assert (M(1,2), 1.0177537954095, 1e-09);
 assert (M(2,2), 1.28736803631001, 1e-09);
 assert (M(3,2), 1.0177537954095, 1e-09);
 assert (M(4,2), 1.0880245732889, 1e-09);
 assert (M(5,2), 0.959547480416536, 1e-09);

 ## Test for anova1 ("unequal") - comparison with results from GraphPad Prism 8
 [P, ATAB, STATS] = anova1 (dv, g, "off", "unequal");
 [C, M, H, GNAMES] = multcompare (STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.001247025266382, 1e-09);
 assert (C(2,6), 0.000018037115146, 1e-09);
 assert (C(3,6), 0.000002974595187, 1e-09);
 assert (C(4,6), 0.000000000786046, 1e-09);
 assert (C(5,6), 0.5693192886650109, 1e-09);
 assert (C(6,6), 0.110501699029776, 1e-09);
 assert (C(7,6), 0.000131226488700, 1e-09);
 assert (C(8,6), 0.1912101409715992, 1e-09);
 assert (C(9,6), 0.000005385256394, 1e-09);
 assert (C(10,6), 0.000074089106171, 1e-09);
***** test

 ## Test for anova2 ("interaction") - comparison with results from Matlab for column effect
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [P, ATAB, STATS] = anova2 (popcorn, 3, "off");
 [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
                                  "ctype", "lsd", "display", "off");
 assert (C(1,6), 1.49311100811177e-05, 1e-09);
 assert (C(2,6), 2.20506904243535e-07, 1e-09);
 assert (C(3,6), 0.00449897860490058, 1e-09);
 assert (M(1,1), 6.25, 1e-09);
 assert (M(2,1), 4.75, 1e-09);
 assert (M(3,1), 4, 1e-09);
 assert (M(1,2), 0.152145154862547, 1e-09);
 assert (M(2,2), 0.152145154862547, 1e-09);
 assert (M(3,2), 0.152145154862547, 1e-09);
***** test

 ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
 words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
          15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
 [P, ATAB, STATS] = anova2 (words, 1, "off", "linear");
 [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
                                  "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.000020799832702, 1e-09);
 assert (C(2,6), 0.000000035812410, 1e-09);
 assert (C(3,6), 0.003038942449215, 1e-09);
***** test

 ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
 data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
         6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
         5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
         6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
 [P, ATAB, STATS] = anova2 (data, 4, "off", "nested");
 [C, M, H, GNAMES] = multcompare (STATS, "estimate", "column",...
                                  "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.261031111511073, 1e-09);
 assert (C(2,6), 0.065879755907745, 1e-09);
 assert (C(3,6), 0.241874613529270, 1e-09);
***** shared visibility_setting
 visibility_setting = get (0, "DefaultFigureVisible");
***** test
 set (0, "DefaultFigureVisible", "off");

 ## Test for kruskalwallis - comparison with results from MATLAB
 data = [3,2,4; 5,4,4; 4,2,4; 4,2,4; 4,1,5; ...
         4,2,3; 4,3,5; 4,2,4; 5,2,4; 5,3,3];
 group = [1:3] .* ones (10,3);
 [P, ATAB, STATS] = kruskalwallis (data(:), group(:), "off");
 C = multcompare (STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.000163089828959986, 1e-09);
 assert (C(2,6), 0.630298044801257, 1e-09);
 assert (C(3,6), 0.00100567660695682, 1e-09);
 C = multcompare (STATS, "ctype", "bonferroni", "display", "off");
 assert (C(1,6), 0.000489269486879958, 1e-09);
 assert (C(2,6), 1, 1e-09);
 assert (C(3,6), 0.00301702982087047, 1e-09);
 C = multcompare(STATS, "ctype", "scheffe", "display", "off");
 assert (C(1,6), 0.000819054880289573, 1e-09);
 assert (C(2,6), 0.890628039849261, 1e-09);
 assert (C(3,6), 0.00447816059021654, 1e-09);
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 ## Test for friedman - comparison with results from MATLAB
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [P, ATAB, STATS] = friedman (popcorn, 3, "off");
 C = multcompare(STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 0.227424558028569, 1e-09);
 assert (C(2,6), 0.0327204848315735, 1e-09);
 assert (C(3,6), 0.353160353315988, 1e-09);
 C = multcompare(STATS, "ctype", "bonferroni", "display", "off");
 assert (C(1,6), 0.682273674085708, 1e-09);
 assert (C(2,6), 0.0981614544947206, 1e-09);
 assert (C(3,6), 1, 1e-09);
 C = multcompare(STATS, "ctype", "scheffe", "display", "off");
 assert (C(1,6), 0.482657360384373, 1e-09);
 assert (C(2,6), 0.102266573027672, 1e-09);
 assert (C(3,6), 0.649836502233148, 1e-09);
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 ## Test for fitlm - same comparisons as for first anovan example
 y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
 [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off",...
                      "contrasts","simple");
 [C, M] = multcompare(STATS, "ctype", "lsd", "display", "off");
 assert (C(1,6), 2.85812420217898e-05, 1e-09);
 assert (C(2,6), 5.22936741204085e-07, 1e-09);
 assert (C(3,6), 2.12794763209146e-08, 1e-09);
 assert (C(4,6), 7.82091664406946e-15, 1e-09);
 assert (C(5,6), 0.546591417210693, 1e-09);
 assert (C(6,6), 0.0845897945254446, 1e-09);
 assert (C(7,6), 9.47436557975328e-08, 1e-09);
 assert (C(8,6), 0.188873478781067, 1e-09);
 assert (C(9,6), 4.08974010364197e-08, 1e-09);
 assert (C(10,6), 4.44427348175241e-06, 1e-09);
 assert (M(1,1), 10, 1e-09);
 assert (M(2,1), 18, 1e-09);
 assert (M(3,1), 19, 1e-09);
 assert (M(4,1), 21.0001428571429, 1e-09);
 assert (M(5,1), 29.0001111111111, 1e-09);
 assert (M(1,2), 1.0177537954095, 1e-09);
 assert (M(2,2), 1.28736803631001, 1e-09);
 assert (M(3,2), 1.0177537954095, 1e-09);
 assert (M(4,2), 1.0880245732889, 1e-09);
 assert (M(5,2), 0.959547480416536, 1e-09);
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 ## Test p-value adjustments compared to R stats package function p.adjust
 ## Data from Westfall (1997) JASA. 92(437):299-306
 p = [.005708; .023544; .024193; .044895; ...
       .048805; .221227; .395867; .693051; .775755];
 padj = multcompare (p);
 assert (padj(1), 0.051372, 1e-06);
 assert (padj(2), 0.188352, 1e-06);
 assert (padj(3), 0.188352, 1e-06);
 assert (padj(4), 0.269370, 1e-06);
 assert (padj(5), 0.269370, 1e-06);
 assert (padj(6), 0.884908, 1e-06);
 assert (padj(7), 1.000000, 1e-06);
 assert (padj(8), 1.000000, 1e-06);
 assert (padj(9), 1.000000, 1e-06);
 padj = multcompare(p,'ctype','holm');
 assert (padj(1), 0.051372, 1e-06);
 assert (padj(2), 0.188352, 1e-06);
 assert (padj(3), 0.188352, 1e-06);
 assert (padj(4), 0.269370, 1e-06);
 assert (padj(5), 0.269370, 1e-06);
 assert (padj(6), 0.884908, 1e-06);
 assert (padj(7), 1.000000, 1e-06);
 assert (padj(8), 1.000000, 1e-06);
 assert (padj(9), 1.000000, 1e-06);
 padj = multcompare(p,'ctype','hochberg');
 assert (padj(1), 0.051372, 1e-06);
 assert (padj(2), 0.169351, 1e-06);
 assert (padj(3), 0.169351, 1e-06);
 assert (padj(4), 0.244025, 1e-06);
 assert (padj(5), 0.244025, 1e-06);
 assert (padj(6), 0.775755, 1e-06);
 assert (padj(7), 0.775755, 1e-06);
 assert (padj(8), 0.775755, 1e-06);
 assert (padj(9), 0.775755, 1e-06);
 padj = multcompare(p,'ctype','fdr');
 assert (padj(1), 0.0513720, 1e-07);
 assert (padj(2), 0.0725790, 1e-07);
 assert (padj(3), 0.0725790, 1e-07);
 assert (padj(4), 0.0878490, 1e-07);
 assert (padj(5), 0.0878490, 1e-07);
 assert (padj(6), 0.3318405, 1e-07);
 assert (padj(7), 0.5089719, 1e-07);
 assert (padj(8), 0.7757550, 1e-07);
 assert (padj(9), 0.7757550, 1e-07);
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/vartest2.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/vartest2.m
***** error<vartest2: too few input arguments.> vartest2 ();
***** error<vartest2: too few input arguments.> vartest2 (ones (20,1));
***** error<vartest2: X and Y must be vectors or matrices or N-D arrays.> ...
 vartest2 (rand (20,1), 5);
***** error<vartest2: invalid value for alpha.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0);
***** error<vartest2: invalid value for alpha.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", 1.2);
***** error<vartest2: invalid value for alpha.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", "some");
***** error<vartest2: invalid value for alpha.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", [0.05, 0.001]);
***** error<vartest2: invalid value for tail.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "tail", [0.05, 0.001]);
***** error<vartest2: invalid value for tail.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "tail", "some");
***** error<vartest2: invalid value for operating dimension.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "dim", 3);
***** error<vartest2: invalid value for operating dimension.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "alpha", 0.001, "dim", 3);
***** error<vartest2: invalid name for optional arguments.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "some", 3);
***** error<vartest2: optional arguments must be in name/value pairs.> ...
 vartest2 (rand (20,1), rand (25,1)*2, "some");
***** test
 load carsmall
 [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76));
 assert (h, 0);
 assert (pval, 0.6288022362718455, 1e-13);
 assert (ci, [0.4139; 1.7193], 1e-4);
 assert (stat.fstat, 0.8384, 1e-4);
 assert (stat.df1, 30);
 assert (stat.df2, 33);
***** test
 load carsmall
 [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76), ...
                                 "tail", "left");
 assert (h, 0);
 assert (pval, 0.314401118135922, 1e-13);
 assert (ci, [0; 1.5287], 1e-4);
 assert (stat.fstat, 0.8384, 1e-4);
 assert (stat.df1, 30);
 assert (stat.df2, 33);
***** test
 load carsmall
 [h, pval, ci, stat] = vartest2 (MPG(Model_Year==82), MPG(Model_Year==76), ...
                                 "tail", "right");
 assert (h, 0);
 assert (pval, 0.685598881864077, 1e-13);
 assert (ci, [0.4643; Inf], 1e-4);
 assert (stat.fstat, 0.8384, 1e-4);
 assert (stat.df1, 30);
 assert (stat.df2, 33);
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/fitcsvm.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/fitcsvm.m
***** demo
 ## Use a subset of Fisher's iris data set

 load fisheriris
 inds = ! strcmp (species, 'setosa');
 X = meas(inds, [3,4]);
 Y = species(inds);

 ## Train a linear SVM classifier
 SVMModel = fitcsvm (X, Y)

 ## Plot a scatter diagram of the data and circle the support vectors.
 sv = SVMModel.SupportVectors;
 figure
 gscatter (X(:,1), X(:,2), Y)
 hold on
 plot (sv(:,1), sv(:,2), 'ko', 'MarkerSize', 10)
 legend ('versicolor', 'virginica', 'Support Vector')
 hold off
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = {"a"; "a"; "b"; "b"};
 a = fitcsvm (x, y);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y}, {x, y})
 assert (a.NumObservations, 4)
 assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
 assert (a.ModelParameters.SVMtype, "c_svc")
 assert (a.ClassNames, {"a"; "b"})
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = fitcsvm (x, y);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
 assert (a.ModelParameters.BoxConstraint, 1)
 assert (a.ModelParameters.KernelOffset, 0)
 assert (a.ClassNames, [-1; 1])
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = fitcsvm (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
 "KernelOffset", 2);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
 assert (a.ModelParameters.BoxConstraint, 2)
 assert (a.ModelParameters.KernelOffset, 2)
 assert (isempty (a.Alpha), true)
 assert (isempty (a.Beta), false)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = fitcsvm (x, y, "KernelFunction", "polynomial", "PolynomialOrder", 3);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
 assert (a.ModelParameters.PolynomialOrder, 3)
 assert (isempty (a.Alpha), true)
 assert (isempty (a.Beta), false)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = fitcsvm (x, y, "KernelFunction", "linear", "PolynomialOrder", 3);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
 assert (a.ModelParameters.PolynomialOrder, 3)
 assert (isempty (a.Alpha), false)
 assert (isempty (a.Beta), true)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 status = warning;
 warning ('off');
 rand ("seed", 23);
 a = fitcsvm (x, y, "KernelFunction", "linear", "CrossVal", 'on');
 warning (status);
 assert (class (a), "ClassificationPartitionedModel");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
 assert (a.ModelParameters.PolynomialOrder, 3)
 assert (isempty (a.Trained{1}.Alpha), false)
 assert (isempty (a.Trained{1}.Beta), true)
***** error<fitcsvm: too few arguments.> fitcsvm ()
***** error<fitcsvm: too few arguments.> fitcsvm (ones (4,1))
***** error<fitcsvm: Name-Value arguments must be in pairs.>
 fitcsvm (ones (4,2), ones (4, 1), 'KFold')
***** error<fitcsvm: number of rows in X and Y must be equal.>
 fitcsvm (ones (4,2), ones (3, 1))
***** error<fitcsvm: number of rows in X and Y must be equal.>
 fitcsvm (ones (4,2), ones (3, 1), 'KFold', 2)
***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
 fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 2)
***** error <fitcsvm: 'CrossVal' must be either 'off' or 'on'.>
 fitcsvm (ones (4,2), ones (4, 1), "CrossVal", 'a')
***** error <fitcsvm: You can use only one cross-validation name-value pair argument> ...
 fitcsvm (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/regression_ftest.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/regression_ftest.m
***** error<Invalid call to regression_ftest.  Correct usage> regression_ftest ();
***** error<Invalid call to regression_ftest.  Correct usage> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]');
***** error<regression_ftest: Y must contain finite real numbers.> ...
 regression_ftest ([1 2 NaN]', [2 3 4; 3 4 5]', [1 0.5]);
***** error<regression_ftest: Y must contain finite real numbers.> ...
 regression_ftest ([1 2 Inf]', [2 3 4; 3 4 5]', [1 0.5]);
***** error<regression_ftest: Y must contain finite real numbers.> ...
 regression_ftest ([1 2 3+i]', [2 3 4; 3 4 5]', [1 0.5]);
***** error<regression_ftest: X must contain finite real numbers.> ...
 regression_ftest ([1 2 3]', [2 3 NaN; 3 4 5]', [1 0.5]);
***** error<regression_ftest: X must contain finite real numbers.> ...
 regression_ftest ([1 2 3]', [2 3 Inf; 3 4 5]', [1 0.5]);
***** error<regression_ftest: X must contain finite real numbers.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 3+i]', [1 0.5]);
***** error<regression_ftest: invalid value for alpha.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 0);
***** error<regression_ftest: invalid value for alpha.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", 1.2);
***** error<regression_ftest: invalid value for alpha.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", [.02 .1]);
***** error<regression_ftest: invalid value for alpha.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "alpha", "a");
***** error<regression_ftest: invalid Name argument.> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [], "some", 0.05);
***** error<regression_ftest: Y must be a vector of length> ...
 regression_ftest ([1 2 3]', [2 3; 3 4]', [1 0.5]);
***** error<regression_ftest: Y must be a vector of length> ...
 regression_ftest ([1 2; 3 4]', [2 3; 3 4]', [1 0.5]);
***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], ones (2));
***** error<regression_ftest: reduced model, RM, must be a numeric vector or> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], "alpha");
***** error<regression_ftest: reduced model, RM, must have smaller length than> ...
 regression_ftest ([1 2 3]', [2 3 4; 3 4 5]', [1 0.5], [1 2]);
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/chi2gof.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/chi2gof.m
***** demo
 x = normrnd (50, 5, 100, 1);
 [h, p, stats] = chi2gof (x)
 [h, p, stats] = chi2gof (x, "cdf", @(x)normcdf (x, mean(x), std(x)))
 [h, p, stats] = chi2gof (x, "cdf", {@normcdf, mean(x), std(x)})
***** demo
 x = rand (100,1 );
 n = length (x);
 binedges = linspace (0, 1, 11);
 expectedCounts = n * diff (binedges);
 [h, p, stats] = chi2gof (x, "binedges", binedges, "expected", expectedCounts)
***** demo
 bins = 0:5;
 obsCounts = [6 16 10 12 4 2];
 n = sum(obsCounts);
 lambdaHat = sum(bins.*obsCounts) / n;
 expCounts = n * poisspdf(bins,lambdaHat);
 [h, p, stats] = chi2gof (bins, "binctrs", bins, "frequency", obsCounts, ...
                          "expected", expCounts, "nparams",1)
***** error chi2gof ()
***** error chi2gof ([2,3;3,4])
***** error chi2gof ([1,2,3,4], "nbins", 3, "ctrs", [2,3,4])
***** error chi2gof ([1,2,3,4], "frequency", [2,3,2])
***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,-2])
***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "nparams", i)
***** error chi2gof ([1,2,3,4], "frequency", [2,3,2,2], "alpha", 1.3)
***** error chi2gof ([1,2,3,4], "expected", [-3,2,2])
***** error chi2gof ([1,2,3,4], "expected", [3,2,2], "nbins", 5)
***** error chi2gof ([1,2,3,4], "cdf", @normcdff)
***** test
 x = [1 2 1 3 2 4 3 2 4 3 2 2];
 [h, p, stats] = chi2gof (x);
 assert (h, 0);
 assert (p, NaN);
 assert (stats.chi2stat, 0.1205375022748029, 1e-14);
 assert (stats.df, 0);
 assert (stats.edges, [1, 2.5, 4], 1e-14);
 assert (stats.O, [7, 5], 1e-14);
 assert (stats.E, [6.399995519909668, 5.600004480090332], 1e-14);
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/crossval.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/crossval.m
***** demo
 ## Determine the optimal number of clusters using cross-validation

 ## Declare a function to compute the sum of squared distances
 ## between data points and a varying number of clusters.
 function D = dist2clusters (X, Y, k)
   [Z, Zmu, Zstd] = zscore (X);
   [~, C] = kmeans (Z, k);
   ZY = (Y - Zmu) ./ Zstd;
   d = pdist2 (C, ZY, 'euclidean', 'Smallest', 1);
   D = sum (d .^ 2);
 endfunction

 load fisheriris
 for k = 1:8
   fcn = @(X, Y) dist2clusters (X, Y, k);
   distances = crossval (fcn, meas);
   cvdist(k) = sum (distances);
 endfor

 plot(cvdist)
 xlabel('Number of Clusters')
 ylabel('CV Sum of Squared Distances')
 xlim ([1,8]);
***** test
 function yfit = regf (Xtrain, ytrain, Xtest)
   b = regress (ytrain, Xtrain);
   yfit = Xtest * b;
 endfunction

 load carsmall
 data = [Acceleration Horsepower Weight MPG];
 data(any(isnan(data),2),:) = [];

 y = data(:,4);
 X = [ones(length(y),1) data(:,1:3)];
 rand ("seed", 3);
 cvMSE = crossval('mse',X,y,'Predfun',@regf);
 assert (cvMSE, 18.720, 1e-3);
***** error <crossval: criterion must be 'mse' or 'mcr'.> ...
 crossval ('fe', rand (10, 1), rand (10, 1), 1);
***** error <crossval: prediction function handle is required for error evaluation.>  ...
 crossval ('mse', rand (10, 1), rand (10, 1), 1);
***** error <crossval: X and Y are required for error evaluation.> ...
 crossval ('mse', rand (10, 1), 'Predfun', @(x,y) x + y);
***** error <crossval: bad prediction function handle for error evaluation.> ...
 crossval ('mse', rand (10, 3), rand (10, 1), 'Predfun', @(x,y) sum (x + y));
***** error <crossval: prediction function handle must return a column vector with the same rows as XTest.> ...
 crossval ('mse', rand (10, 3), rand (10, 1), 'Predfun', @(x,y,z) sum (x + y));
***** error <crossval: X is required for values evaluation.> crossval (@(x) x);
***** error <crossval: bad function handle to cross-validate.> ...
 crossval (@(x) x, rand (10, 3), rand (10, 1));
***** error <crossval: function handle must return a scalar or a row vector.> ...
 crossval (@(x,y) [x, y], rand (10, 3), rand (10, 1));
***** error <crossval: invalid first input argument.> crossval ({1}, 1, 1);
***** error <crossval: you can only set one cvpartition type in paired arguments.> ...
 crossval (@(x,y) sum ([x; y]), rand (10, 3), 'Holdout', 0.1, 'Leaveout', true)
***** error <crossval: you cannot specify both 'Partition' and 'Stratify'.> ...
 crossval (@(x,y) sum ([x; y]), rand (10, 3), 'Partition', cvpartition (10, 'Leaveout'), 'Stratify', true)
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/plsregress.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/plsregress.m
***** demo
 ## Perform Partial Least-Squares Regression

 ## Load the spectra data set and use the near infrared (NIR) spectral
 ## intensities (NIR) as the predictor and the corresponding octave
 ## ratings (octave) as the response.
 load spectra

 ## Perform PLS regression with 10 components
 [xload, yload, xscore, yscore, coef, ptcVar] = plsregress (NIR, octane, 10);

 ## Plot the percentage of explained variance in the response variable
 ## (PCTVAR) as a function of the number of components.
 plot (1:10, cumsum (100 * ptcVar(2,:)), "-ro");
 xlim ([1, 10]);
 xlabel ("Number of PLS components");
 ylabel ("Percentage of Explained Variance in octane");
 title ("Explained Variance per PLS components");

 ## Compute the fitted response and display the residuals.
 octane_fitted = [ones(size(NIR,1),1), NIR] * coef;
 residuals = octane - octane_fitted;
 figure
 stem (residuals, "color", "r", "markersize", 4, "markeredgecolor", "r")
 xlabel ("Observations");
 ylabel ("Residuals");
 title ("Residuals in octane's fitted response");
***** demo
 ## Calculate Variable Importance in Projection (VIP) for PLS Regression

 ## Load the spectra data set and use the near infrared (NIR) spectral
 ## intensities (NIR) as the predictor and the corresponding octave
 ## ratings (octave) as the response.  Variables with a VIP score greater than
 ## 1 are considered important for the projection of the PLS regression model.
 load spectra

 ## Perform PLS regression with 10 components
 [xload, yload, xscore, yscore, coef, pctVar, mse, stats] = ...
                                                 plsregress (NIR, octane, 10);

 ## Calculate the normalized PLS weights
 W0 = stats.W ./ sqrt(sum(stats.W.^2,1));

 ## Calculate the VIP scores for 10 components
 nobs = size (xload, 1);
 SS = sum (xscore .^ 2, 1) .* sum (yload .^ 2, 1);
 VIPscore = sqrt (nobs * sum (SS .* (W0 .^ 2), 2) ./ sum (SS, 2));

 ## Find variables with a VIP score greater than or equal to 1
 VIPidx = find (VIPscore >= 1);

 ## Plot the VIP scores
 scatter (1:length (VIPscore), VIPscore, "xb");
 hold on
 scatter (VIPidx, VIPscore (VIPidx), "xr");
 plot ([1, length(VIPscore)], [1, 1], "--k");
 hold off
 axis ("tight");
 xlabel ("Predictor Variables");
 ylabel ("VIP scores");
 title ("VIP scores for each predictor variable with 10 components");
***** test
 load spectra
 [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 10);
 xload1_out = [-0.0170, 0.0039, 0.0095,  0.0258, 0.0025, ...
               -0.0075, 0.0000, 0.0018, -0.0027, 0.0020];
 yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625, ...
              0.5905, 0.4244, 0.2437, 0.3516, 0.2548];
 xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669,  0.1041, ...
                -0.2067,  0.0457,  0.1565, 0.0706, -0.1471];
 yscore1_out = [-12.4635, -15.0003,  0.0638,  0.0652, -0.0070, ...
                 -0.0634,   0.0062, -0.0012, -0.0151, -0.0173];
 assert (xload(1,:), xload1_out, 1e-4);
 assert (yload, yload_out, 1e-4);
 assert (xscore(1,:), xscore1_out, 1e-4);
 assert (yscore(1,:), yscore1_out, 1e-4);
***** test
 load spectra
 [xload, yload, xscore, yscore, coef, pctVar] = plsregress (NIR, octane, 5);
 xload1_out = [-0.0170, 0.0039, 0.0095, 0.0258, 0.0025];
 yload_out = [6.6384, 9.3106, 2.0505, 0.6471, 0.9625];
 xscore1_out = [-0.0401, -0.1764, -0.0340, 0.1669, 0.1041];
 yscore1_out = [-12.4635, -15.0003, 0.0638, 0.0652, -0.0070];
 assert (xload(1,:), xload1_out, 1e-4);
 assert (yload, yload_out, 1e-4);
 assert (xscore(1,:), xscore1_out, 1e-4);
 assert (yscore(1,:), yscore1_out, 1e-4);
***** error<plsregress: function called with too few input arguments.>
 plsregress (1)
***** error<plsregress: X and Y must be real matrices.> plsregress (1, "asd")
***** error<plsregress: X and Y must be real matrices.> plsregress (1, {1,2,3})
***** error<plsregress: X and Y must be real matrices.> plsregress ("asd", 1)
***** error<plsregress: X and Y must be real matrices.> plsregress ({1,2,3}, 1)
***** error<plsregress: X and Y observations mismatch.> ...
 plsregress (ones (20,3), ones (15,1))
***** error<plsregress: invalid value for NCOMP.> ...
 plsregress (ones (20,3), ones (20,1), 0)
***** error<plsregress: invalid value for NCOMP.> ...
 plsregress (ones (20,3), ones (20,1), -5)
***** error<plsregress: invalid value for NCOMP.> ...
 plsregress (ones (20,3), ones (20,1), 3.2)
***** error<plsregress: invalid value for NCOMP.> ...
 plsregress (ones (20,3), ones (20,1), [2, 3])
***** error<plsregress: NCOMP exceeds maximum components for X.> ...
 plsregress (ones (20,3), ones (20,1), 4)
***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 4.5)
***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", -1)
***** error<plsregress: invalid VALUE for 'cv' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", "somestring")
***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", 2.2)
***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", -2)
***** error<plsregress: invalid VALUE for 'mcreps' optional argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "mcreps", [1, 2])
***** error<plsregress: invalid NAME argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "Name", 3, "mcreps", 1)
***** error<plsregress: invalid NAME argument.> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", 3, "Name", 1)
***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
 plsregress (ones (20,3), ones (20,1), 3, "mcreps", 2)
***** error<plsregress: 'mcreps' must be 1 when 'resubstitution' is specified> ...
 plsregress (ones (20,3), ones (20,1), 3, "cv", "resubstitution", "mcreps", 2)
***** error<Invalid call to plsregress.  Correct usage is:> plsregress (1, 2)
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/ztest2.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/ztest2.m
***** error ztest2 ();
***** error ztest2 (1);
***** error ztest2 (1, 2);
***** error ztest2 (1, 2, 3);
***** error ztest2 (1, 2, 3, 2);
***** error<ztest2: optional arguments must be in NAME-VALUE pairs.> ...
 ztest2 (1, 2, 3, 4, "alpha")
***** error<ztest2: invalid VALUE for alpha.> ...
 ztest2 (1, 2, 3, 4, "alpha", 0);
***** error<ztest2: invalid VALUE for alpha.> ...
 ztest2 (1, 2, 3, 4, "alpha", 1.2);
***** error<ztest2: invalid VALUE for alpha.> ...
 ztest2 (1, 2, 3, 4, "alpha", "val");
***** error<ztest2: invalid VALUE for tail.>  ...
 ztest2 (1, 2, 3, 4, "tail", "val");
***** error<ztest2: invalid VALUE for tail.>  ...
 ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "val");
***** error<ztest2: invalid NAME for optional arguments.> ...
 ztest2 (1, 2, 3, 4, "alpha", 0.01, "tail", "both", "badoption", 3);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/Regression/RegressionGAM.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Regression/RegressionGAM.m
***** demo
 ## Train a RegressionGAM Model for synthetic values
 f1 = @(x) cos (3 * x);
 f2 = @(x) x .^ 3;
 x1 = 2 * rand (50, 1) - 1;
 x2 = 2 * rand (50, 1) - 1;
 y = f1(x1) + f2(x2);
 y = y + y .* 0.2 .* rand (50,1);
 X = [x1, x2];
 a = fitrgam (X, y, "tol", 1e-3)
***** demo
 ## Declare two different functions
 f1 = @(x) cos (3 * x);
 f2 = @(x) x .^ 3;

 ## Generate 80 samples for f1 and f2
 x = [-4*pi:0.1*pi:4*pi-0.1*pi]';
 X1 = f1 (x);
 X2 = f2 (x);

 ## Create a synthetic response by adding noise
 rand ("seed", 3);
 Ytrue = X1 + X2;
 Y = Ytrue + Ytrue .* 0.2 .* rand (80,1);

 ## Assemble predictor data
 X = [X1, X2];

 ## Train the GAM and test on the same data
 a = fitrgam (X, Y, "order", [5, 5]);
 [ypred, ySDsd, yInt] = predict (a, X);

 ## Plot the results
 figure
 [sortedY, indY] = sort (Ytrue);
 plot (sortedY, "r-");
 xlim ([0, 80]);
 hold on
 plot (ypred(indY), "g+")
 plot (yInt(indY,1), "k:")
 plot (yInt(indY,2), "k:")
 xlabel ("Predictor samples");
 ylabel ("Response");
 title ("actual vs predicted values for function f1(x) = cos (3x) ");
 legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});

 ## Use 30% Holdout partitioning for training and testing data
 C = cvpartition (80, "HoldOut", 0.3);
 [ypred, ySDsd, yInt] = predict (a, X(test(C),:));

 ## Plot the results
 figure
 [sortedY, indY] = sort (Ytrue(test(C)));
 plot (sortedY, 'r-');
 xlim ([0, sum(test(C))]);
 hold on
 plot (ypred(indY), "g+")
 plot (yInt(indY,1),'k:')
 plot (yInt(indY,2),'k:')
 xlabel ("Predictor samples");
 ylabel ("Response");
 title ("actual vs predicted values for function f1(x) = cos (3x) ");
 legend ({"Theoretical Response", "Predicted Response", "Prediction Intervals"});
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [1; 2; 3; 4];
 a = RegressionGAM (x, y);
 assert ({a.X, a.Y}, {x, y})
 assert ({a.BaseModel.Intercept}, {2.5000})
 assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
 assert ({a.NumObservations, a.NumPredictors}, {4, 3})
 assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
 assert ({a.Formula}, {[]})
***** test
 x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
 y = [1; 2; 3; 4];
 pnames = {"A", "B", "C", "D"};
 formula = "Y ~ A + B + C + D + A:C";
 intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
 a = RegressionGAM (x, y, "predictors", pnames, "formula", formula);
 assert (a.IntMatrix, double (intMat))
 assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
 assert (a.Formula, formula)
***** error<RegressionGAM: too few input arguments.> RegressionGAM ()
***** error<RegressionGAM: too few input arguments.> RegressionGAM (ones(10,2))
***** error<RegressionGAM: number of rows in X and Y must be equal.> ...
 RegressionGAM (ones(10,2), ones (5,1))
***** error<RegressionGAM: invalid values in X.> ...
 RegressionGAM ([1;2;3;"a";4], ones (5,1))
***** error<RegressionGAM: invalid parameter name in optional pair arguments.> ...
 RegressionGAM (ones(10,2), ones (10,1), "some", "some")
***** error<RegressionGAM: Formula must be a string.>
 RegressionGAM (ones(10,2), ones (10,1), "formula", {"y~x1+x2"})
***** error<RegressionGAM: Formula must be a string.>
 RegressionGAM (ones(10,2), ones (10,1), "formula", [0, 1, 0])
***** error<RegressionGAM: invalid syntax in Formula.> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "something")
***** error<RegressionGAM: no predictor terms in Formula.> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
***** error<RegressionGAM: no predictor terms in Formula.> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "something~")
***** error<RegressionGAM: some predictors have not been identified> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "something~x1:")
***** error<RegressionGAM: invalid Interactions parameter.> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", "some")
***** error<RegressionGAM: invalid Interactions parameter.> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", -1)
***** error<RegressionGAM: invalid Interactions parameter.> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", [1 2 3 4])
***** error<RegressionGAM: number of interaction terms requested is larger than> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", 3)
***** error<RegressionGAM: Formula has been already defined.> ...
 RegressionGAM (ones(10,2), ones (10,1), "formula", "y ~ x1 + x2", "interactions", 1)
***** error<RegressionGAM: Interactions have been already defined.> ...
 RegressionGAM (ones(10,2), ones (10,1), "interactions", 1, "formula", "y ~ x1 + x2")
***** error<RegressionGAM: invalid value for Knots.> ...
 RegressionGAM (ones(10,2), ones (10,1), "knots", "a")
***** error<RegressionGAM: DoF and Order have been set already.> ...
 RegressionGAM (ones(10,2), ones (10,1), "order", 3, "dof", 2, "knots", 5)
***** error<RegressionGAM: invalid value for DoF.> ...
 RegressionGAM (ones(10,2), ones (10,1), "dof", 'a')
***** error<RegressionGAM: Knots and Order have been set already.> ...
 RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "order", 3, "dof", 2)
***** error<RegressionGAM: invalid value for Order.> ...
 RegressionGAM (ones(10,2), ones (10,1), "order", 'a')
***** error<RegressionGAM: DoF and Knots have been set already.> ...
 RegressionGAM (ones(10,2), ones (10,1), "knots", 5, "dof", 2, "order", 2)
***** error<RegressionGAM: Tolerance must be a Positive scalar.> ...
 RegressionGAM (ones(10,2), ones (10,1), "tol", -1)
***** error<RegressionGAM: ResponseName must be a char string.> ...
 RegressionGAM (ones(10,2), ones (10,1), "responsename", -1)
***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
 RegressionGAM (ones(10,2), ones (10,1), "predictors", -1)
***** error<RegressionGAM: PredictorNames must be a cellstring array.> ...
 RegressionGAM (ones(10,2), ones (10,1), "predictors", ['a','b','c'])
***** error<RegressionGAM: PredictorNames must have same number of columns as X.> ...
 RegressionGAM (ones(10,2), ones (10,1), "predictors", {'a','b','c'})
***** error<RegressionGAM.predict: too few arguments.> ...
 predict (RegressionGAM (ones(10,1), ones(10,1)))
***** error<RegressionGAM.predict: Xfit is empty.> ...
 predict (RegressionGAM (ones(10,1), ones(10,1)), [])
***** error<RegressionGAM.predict: Xfit must have the same number of features> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), 2)
***** error<RegressionGAM.predict: invalid NAME in optional pairs of arguments.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "some", "some")
***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", "some")
***** error<RegressionGAM.predict: includeinteractions must be a logical value.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "includeinteractions", 5)
***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 5)
***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", -1)
***** error<RegressionGAM.predict: alpha must be a scalar value between 0 and 1.> ...
 predict (RegressionGAM(ones(10,2), ones(10,1)), ones (10,2), "alpha", 'a')
39 tests, 39 passed, 0 known failure, 0 skipped
[inst/nanmean.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/nanmean.m
***** demo
 ## Find the column means for a matrix with missing values.,

 x = magic (3);
 x([1, 4, 7:9]) = NaN
 y = nanmean (x)
***** demo
 ## Find the row means for a matrix with missing values.,

 x = magic (3);
 x([1, 4, 7:9]) = NaN
 y = nanmean (x, 2)
***** demo
 ## Find the mean of all the values in a multidimensional array
 ## with missing values.

 x = reshape (1:30, [2, 5, 3]);
 x([10:12, 25]) = NaN
 y = nanmean (x, "all")
***** demo
 ## Find the mean of a multidimensional array with missing values over
 ## multiple dimensions.

 x = reshape (1:30, [2, 5, 3]);
 x([10:12, 25]) = NaN
 y = nanmean (x, [2, 3])
***** assert (nanmean ([]), NaN)
***** assert (nanmean (NaN), NaN)
***** assert (nanmean (NaN(3)), [NaN, NaN, NaN])
***** assert (nanmean ([3 2 NaN 7]), 4)
***** assert (nanmean ([2 4 NaN Inf]), Inf)
***** assert (nanmean ([1 NaN 3; NaN 4 6; 7 8 NaN]), [4 6 4.5])
***** assert (nanmean ([1 NaN 3; NaN 5 6; 7 8 NaN], 2), [2; 5.5; 7.5])
***** assert (nanmean (uint8 ([2 4 1 7])), 3.5)
***** test
 x = magic(3);
 x([1 6:9]) = NaN;
 assert (nanmean (x), [3.5, 3, NaN])
 assert (nanmean (x, 2), [1; 4; 4])
***** test
 x = reshape(1:24, [2, 4, 3]);
 x([5:6, 20]) = NaN;
 assert (nanmean (x, "all"), 269/21)
***** test
 x = reshape(1:24,[2, 4, 3]);
 x([5:6, 20]) = NaN;
 assert (squeeze (nanmean (x, [1, 2])), [25/6; 100/8; 144/7])
 assert (nanmean (x, [2, 3]), [139/11; 13])
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/regress_gp.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/regress_gp.m
***** demo
 ## Linear fitting of 1D Data
 rand ("seed", 125);
 X = 2 * rand (5, 1) - 1;
 randn ("seed", 25);
 Y = 2 * X - 1 + 0.3 * randn (5, 1);

 ## Points for interpolation/extrapolation
 Xfit = linspace (-2, 2, 10)';

 ## Fit regression model
 [Yfit, Yint, m] = regress_gp (X, Y, Xfit);

 ## Plot fitted data
 plot (X, Y, "xk", Xfit, Yfit, "r-", Xfit, Yint, "b-");
 title ("Gaussian process regression with linear kernel");
***** demo
 ## Linear fitting of 2D Data
 rand ("seed", 135);
 X = 2 * rand (4, 2) - 1;
 randn ("seed", 35);
 Y = 2 * X(:,1) - 3 * X(:,2) - 1 + 1 * randn (4, 1);

 ## Mesh for interpolation/extrapolation
 [x1, x2] = meshgrid (linspace (-1, 1, 10));
 Xfit = [x1(:), x2(:)];

 ## Fit regression model
 [Ypred, Yint, Ysd] = regress_gp (X, Y, Xfit);
 Ypred = reshape (Ypred, 10, 10);
 YintU = reshape (Yint(:,1), 10, 10);
 YintL = reshape (Yint(:,2), 10, 10);

 ## Plot fitted data
 plot3 (X(:,1), X(:,2), Y, ".k", "markersize", 16);
 hold on;
 h = mesh (x1, x2, Ypred, zeros (10, 10));
 set (h, "facecolor", "none", "edgecolor", "yellow");
 h = mesh (x1, x2, YintU, ones (10, 10));
 set (h, "facecolor", "none", "edgecolor", "cyan");
 h = mesh (x1, x2, YintL, ones (10, 10));
 set (h, "facecolor", "none", "edgecolor", "cyan");
 hold off
 axis tight
 view (75, 25)
 title ("Gaussian process regression with linear kernel");
***** demo
 ## Projection over basis function with linear kernel
 pp = [2, 2, 0.3, 1];
 n = 10;
 rand ("seed", 145);
 X = 2 * rand (n, 1) - 1;
 randn ("seed", 45);
 Y = polyval (pp, X) + 0.3 * randn (n, 1);

 ## Powers
 px = [sqrt(abs(X)), X, X.^2, X.^3];

 ## Points for interpolation/extrapolation
 Xfit = linspace (-1, 1, 100)';
 pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];

 ## Define a prior covariance assuming that the sqrt component is not present
 Sp = 100 * eye (size (px, 2) + 1);
 Sp(2,2) = 1; # We don't believe the sqrt(abs(X)) is present

 ## Fit regression model
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, Sp);

 ## Plot fitted data
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("Linear kernel over basis function with prior covariance");
***** demo
 ## Projection over basis function with linear kernel
 pp = [2, 2, 0.3, 1];
 n = 10;
 rand ("seed", 145);
 X = 2 * rand (n, 1) - 1;
 randn ("seed", 45);
 Y = polyval (pp, X) + 0.3 * randn (n, 1);

 ## Powers
 px = [sqrt(abs(X)), X, X.^2, X.^3];

 ## Points for interpolation/extrapolation
 Xfit = linspace (-1, 1, 100)';
 pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];

 ## Fit regression model without any assumption on prior covariance
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi);

 ## Plot fitted data
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("Linear kernel over basis function without prior covariance");
***** demo
 ## Projection over basis function with rbf kernel
 pp = [2, 2, 0.3, 1];
 n = 10;
 rand ("seed", 145);
 X = 2 * rand (n, 1) - 1;
 randn ("seed", 45);
 Y = polyval (pp, X) + 0.3 * randn (n, 1);

 ## Powers
 px = [sqrt(abs(X)), X, X.^2, X.^3];

 ## Points for interpolation/extrapolation
 Xfit = linspace (-1, 1, 100)';
 pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];

 ## Fit regression model with RBF kernel (standard parameters)
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf");

 ## Plot fitted data
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("RBF kernel over basis function with standard parameters");
 text (-0.5, 4, "theta = 5\n g = 0.01");
***** demo
 ## Projection over basis function with rbf kernel
 pp = [2, 2, 0.3, 1];
 n = 10;
 rand ("seed", 145);
 X = 2 * rand (n, 1) - 1;
 randn ("seed", 45);
 Y = polyval (pp, X) + 0.3 * randn (n, 1);

 ## Powers
 px = [sqrt(abs(X)), X, X.^2, X.^3];

 ## Points for interpolation/extrapolation
 Xfit = linspace (-1, 1, 100)';
 pxi = [sqrt(abs(Xfit)), Xfit, Xfit.^2, Xfit.^3];

 ## Fit regression model with RBF kernel with different parameters
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 10, 0.01);

 ## Plot fitted data
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("GP regression with RBF kernel and non default parameters");
 text (-0.5, 4, "theta = 10\n g = 0.01");

 ## Fit regression model with RBF kernel with different parameters
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.01);

 ## Plot fitted data
 figure
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("GP regression with RBF kernel and non default parameters");
 text (-0.5, 4, "theta = 50\n g = 0.01");

 ## Fit regression model with RBF kernel with different parameters
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.001);

 ## Plot fitted data
 figure
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("GP regression with RBF kernel and non default parameters");
 text (-0.5, 4, "theta = 50\n g = 0.001");

 ## Fit regression model with RBF kernel with different parameters
 [Yfit, Yint, Ysd] = regress_gp (px, Y, pxi, "rbf", 50, 0.05);

 ## Plot fitted data
 figure
 plot (X, Y, "xk;Data;", Xfit, Yfit, "r-;Estimation;", ...
                         Xfit, polyval (pp, Xfit), "g-;True;");
 axis tight
 axis manual
 hold on
 plot (Xfit, Yint(:,1), "m-;Upper bound;", Xfit, Yint(:,2), "b-;Lower bound;");
 hold off
 title ("GP regression with RBF kernel and non default parameters");
 text (-0.5, 4, "theta = 50\n g = 0.05");
***** demo
 ## RBF fitting on noiseless 1D Data
 x = [0:2*pi/7:2*pi]';
 y = 5 * sin (x);

 ## Predictive grid of 500 equally spaced locations
 xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';

 ## Fit regression model with RBF kernel
 [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");

 ## Plot fitted data
 r = mvnrnd (Yfit, diag (Ysd)', 50);
 plot (xi, r', "c-");
 hold on
 plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
 plot (x, y, ".k;Predictor points;", "markersize", 20)
 plot (xi, 5 * sin (xi), "-y;True Function;");
 xlim ([-0.5,2*pi+0.5]);
 ylim ([-10,10]);
 hold off
 title ("GP regression with RBF kernel on noiseless 1D data");
 text (0, -7, "theta = 5\n g = 0.01");
***** demo
 ## RBF fitting on noisy 1D Data
 x = [0:2*pi/7:2*pi]';
 x = [x; x];
 y = 5 * sin (x) + randn (size (x));

 ## Predictive grid of 500 equally spaced locations
 xi = [-0.5:(2*pi+1)/499:2*pi+0.5]';

 ## Fit regression model with RBF kernel
 [Yfit, Yint, Ysd] = regress_gp (x, y, xi, "rbf");

 ## Plot fitted data
 r = mvnrnd (Yfit, diag (Ysd)', 50);
 plot (xi, r', "c-");
 hold on
 plot (xi, Yfit, "r-;Estimation;", xi, Yint, "b-;Confidence interval;");
 plot (x, y, ".k;Predictor points;", "markersize", 20)
 plot (xi, 5 * sin (xi), "-y;True Function;");
 xlim ([-0.5,2*pi+0.5]);
 ylim ([-10,10]);
 hold off
 title ("GP regression with RBF kernel on noisy 1D data");
 text (0, -7, "theta = 5\n g = 0.01");
***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2))
***** error<Invalid call to regress_gp.> regress_gp (ones (20, 2), ones (20, 1))
***** error<regress_gp: X must be a 2-D matrix.> ...
 regress_gp (ones (20, 2, 3), ones (20, 1), ones (20, 2))
***** error<regress_gp: Y must be a column vector.> ...
 regress_gp (ones (20, 2), ones (20, 2), ones (20, 2))
***** error<regress_gp: rows in X must equal the length of Y.> ...
 regress_gp (ones (20, 2), ones (15, 1), ones (20, 2))
***** error<regress_gp: X and XI must have the same number of columns.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (20, 3))
***** error<regress_gp: invalid 4th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), {[3]})
***** error<regress_gp: invalid 4th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "kernel")
***** error<regress_gp: theta must be a scalar when using RBF kernel.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", ones (4))
***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
***** error<regress_gp: invalid 5th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", "value")
***** error<regress_gp: invalid 5th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", {5})
***** error<regress_gp: invalid 5th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), ones (3), 5)
***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 5)
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, {5})
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, ones (2))
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, [1, 1])
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
***** error<regress_gp: invalid 6th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), 5, 0.01, "f")
***** error<regress_gp: invalid 7th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, "f")
***** error<regress_gp: invalid 7th argument.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "rbf", 5, 0.01, [1, 1])
***** error<regress_gp: wrong size for prior covariance matrix Sp.> ...
 regress_gp (ones (20, 2), ones (20, 1), ones (10, 2), "linear", 1)
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/ismissing.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/ismissing.m
***** assert (ismissing ([1, NaN, 3]), [false, true, false])
***** assert (ismissing ('abcd f'), [false, false, false, false, false, false])
***** assert (ismissing ({'xxx', '', 'xyz'}), [false, true, false])
***** assert (ismissing ({'x', '', 'y'}), [false, true, false])
***** assert (ismissing ({'x', '', 'y'; 'z', 'a', ''}), logical ([0, 1, 0; 0, 0, 1]))
***** assert (ismissing ([1, 2; NaN, 2]), [false, false; true, false])
***** assert (ismissing ([1, 2; NaN, 2], 2), [false, true; false, true])
***** assert (ismissing ([1, 2; NaN, 2], [1, 2]), [true, true; false, true])
***** assert (ismissing ([1, 2; NaN, 2], NaN), [false, false; true, false])
***** assert (ismissing (cat (3, magic (2), magic (2))), logical (zeros (2, 2, 2)))
***** assert (ismissing (cat (3, magic (2), [1, 2; 3, NaN])), ...
        logical (cat (3, [0, 0; 0, 0], [0, 0; 0, 1])))
***** assert (ismissing ([1, 2; 3, 4], [5, 1; 2, 0]), logical ([1, 1; 0, 0]))
***** assert (ismissing (cat (3, 'f oo', 'ba r')), ...
        logical (cat (3, [0, 0, 0, 0], [0, 0, 0, 0])))
***** assert (ismissing (cat (3, {'foo'}, {''}, {'bar'})), logical (cat (3, 0, 1, 0)))
***** assert (ismissing (double (NaN)), true)
***** assert (ismissing (single (NaN)), true)
***** assert (ismissing (' '), false)
***** assert (ismissing ({''}), true)
***** assert (ismissing ({' '}), false)
***** assert (ismissing (double (eye(3)), single (1)), logical (eye (3)))
***** assert (ismissing (double (eye(3)), int32 (1)), logical (eye (3)))
***** assert (ismissing (single (eye(3)), double (1)), logical (eye (3)))
***** assert (ismissing (single (eye(3)), int32 (1)), logical (eye (3)))
***** assert (ismissing ({'123', '', 123}), [false, false, false])
***** assert (ismissing (logical ([1, 0, 1])), [false, false, false])
***** assert (ismissing (int32 ([1, 2, 3])), [false, false, false])
***** assert (ismissing (uint32 ([1, 2, 3])), [false, false, false])
***** assert (ismissing ({1, 2, 3}), [false, false, false])
***** assert (ismissing ([struct struct struct]), [false, false, false])
***** assert (ismissing (logical (eye(3)), true), logical (eye (3)))
***** assert (ismissing (logical (eye(3)), double (1)), logical (eye (3)))
***** assert (ismissing (logical (eye(3)), single (1)), logical (eye (3)))
***** assert (ismissing (logical (eye(3)), int32 (1)), logical (eye (3)))
***** assert (ismissing (int32 (eye(3)), int32 (1)), logical (eye (3)))
***** assert (ismissing (int32 (eye(3)), double (1)), logical (eye (3)))
***** assert (ismissing (int32 (eye(3)), single (1)), logical (eye (3)))
***** assert (ismissing ([]), logical([]))
***** assert (ismissing (''), logical([]))
***** assert (ismissing (ones (0,1)), logical(ones(0,1)))
***** assert (ismissing (ones (1,0)), logical(ones(1,0)))
***** assert (ismissing (ones (1,2,0)), logical(ones(1,2,0)))
***** assert (ismissing ([1, NaN, 0, 2]), [false, true, false, false])
***** assert (ismissing ([1, NaN, 0, 2], [0, 1]), [true, false, true, false])
***** assert (ismissing ([1, NaN, 0, 2], [0, NaN]), [false, true, true, false])
***** assert (ismissing ([true, false, true]), [false, false, false])
***** assert (ismissing ([true, false, true], 1), [true, false, true])
***** assert (ismissing ([true, false, true], 0), [false, true, false])
***** assert (ismissing ({'', 'a', 'f'}), [true, false, false])
***** assert (ismissing ({'', 'a', 'f'}, 'a'), [false, true, false])
***** assert (ismissing ({'', 'a', 'f'}, {'a', 'g'}), [false, true, false])
***** assert (ismissing ({'', 'a', 'f'}, {'a', 'f'}), [false, true, true])
***** error ismissing ()
***** error ismissing (1, 2, 3)
***** error <ismissing: 'indicator' and 'A' must have the same data type.> ...
       ismissing ([1, 2; 3, 4], 'abc')
***** error <ismissing: 'indicator' and 'A' must have the same data type.> ...
       ismissing ({'', '', ''}, 1)
***** error <ismissing: 'indicator' and 'A' must have the same data type.> ...
       ismissing (1, struct)
***** error <ismissing: indicators not supported for data type 'struct'> ...
       ismissing (struct, 1)
***** error <ismissing: indicators not supported for data type 'cell'> ...
       ismissing ({1, 2, 3}, 2)
58 tests, 58 passed, 0 known failure, 0 skipped
[inst/canoncorr.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/canoncorr.m
***** shared X, Y, A, B, r, U, V, k, Cuv
 k = 10;
 X = [1:k; sin(1:k); cos(1:k)]';
 Y = [tan(1:k); tanh((1:k)/k)]';
 [A, B, r, U, V, stats] = canoncorr (X, Y);
 Cuv = (U' * V) / (k - 1);
***** assert (diag (Cuv)', r, 10 * eps);
***** assert (diag (diag (Cuv)), Cuv, eps);
***** assert (r, [0.99590, 0.26754], 1E-5);
***** assert (U, center(X) * A, 10 * eps);
***** assert (V, center(Y) * B, 10 * eps);
***** assert (cov(U), eye (size (U, 2)), 10 * eps);
***** assert (cov(V), eye (size (V, 2)), 10 * eps);
 rand ("state", 1); [A, B, r] = canoncorr (rand (5, 10), rand (5, 20));
***** assert (r, ones(1, 5), 10*eps);
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/kstest.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/kstest.m
***** demo
 ## Use the stock return data set to test the null hypothesis that the data
 ## come from a standard normal distribution against the alternative
 ## hypothesis that the population CDF of the data is larger that the
 ## standard normal CDF.

 load stockreturns;
 x = stocks(:,2);
 [h, p, k, c] = kstest (x, "Tail", "larger")

 ## Compute the empirical CDF and plot against the standard normal CDF
 [f, x_values] = ecdf (x);
 h1 = plot (x_values, f);
 hold on;
 h2 = plot (x_values, normcdf (x_values), 'r--');
 set (h1, "LineWidth", 2);
 set (h2, "LineWidth", 2);
 legend ([h1, h2], "Empirical CDF", "Standard Normal CDF", ...
         "Location", "southeast");
 title ("Empirical CDF of stock return data against standard normal CDF")
***** error<kstest: too few inputs.> kstest ()
***** error<kstest: X must be a vector of real numbers.> kstest (ones (2, 4))
***** error<kstest: X must be a vector of real numbers.> kstest ([2, 3, 5, 3+3i])
***** error<kstest: unknown option 'opt'.> kstest ([2, 3, 4, 5, 6], "opt", 0.51)
***** error<kstest: optional parameters must be in name/value pairs.> ...
 kstest ([2, 3, 4, 5, 6], "tail")
***** error<kstest: alpha must be a numeric scalar in the range> ...
 kstest ([2,3,4,5,6],"alpha", [0.05, 0.05])
***** error<kstest: alpha must be a numeric scalar in the range> ...
 kstest ([2, 3, 4, 5, 6], "alpha", NaN)
***** error<kstest: tail argument must be a string.> ...
 kstest ([2, 3, 4, 5, 6], "tail", 0)
***** error<kstest: tail value must be either 'both', right' or 'left'.> ...
 kstest ([2,3,4,5,6], "tail", "whatever")
***** error<kstest: invalid function handle.> ...
 kstest ([1, 2, 3, 4, 5], "CDF", @(x) repmat (x, 2, 3))
***** error<kstest: 'somedist' is not a supported distribution.> ...
 kstest ([1, 2, 3, 4, 5], "CDF", "somedist")
***** error<kstest: 'CDF' must be a probability distribution object.> ...
 kstest ([1, 2, 3, 4, 5], "CDF", cvpartition (5, 'resubstitution'))
***** error<kstest: numerical CDF should have only 2 columns.> ...
 kstest ([2, 3, 4, 5, 6], "alpha", 0.05, "CDF", [2, 3, 4; 1, 3, 4; 1, 2, 1])
***** error<kstest: numerical CDF should have at least one row.> ...
 kstest ([2, 3, 4, 5, 6], "alpha", 0.05, "CDF", nan (5, 2))
***** error<kstest: non-incrementing numerical CDF.> ...
 kstest ([2, 3, 4, 5, 6], "CDF", [2, 3; 1, 4; 3, 2])
***** error<kstest: wrong duplicates in numerical CDF.> ...
 kstest ([2, 3, 4, 5, 6], "CDF", [2, 3; 2, 4; 3, 5])
***** error<kstest: invalid value parsed as CDF optional argument.> ...
 kstest ([2, 3, 4, 5, 6], "CDF", {1, 2, 3, 4, 5})
***** test
 load examgrades
 [h, p] = kstest (grades(:,1));
 assert (h, true);
 assert (p, 7.58603305206105e-107, 1e-14);
***** test
 load examgrades
 [h, p] = kstest (grades(:,1), "CDF", @(x) normcdf(x, 75, 10));
 assert (h, false);
 assert (p, 0.5612, 1e-4);
***** test
 load examgrades
 x = grades(:,1);
 test_cdf = makedist ("tlocationscale", "mu", 75, "sigma", 10, "nu", 1);
 [h, p] = kstest (x, "alpha", 0.01, "CDF", test_cdf);
 assert (h, true);
 assert (p, 0.0021, 1e-4);
***** test
 load stockreturns
 x = stocks(:,3);
 [h,p,k,c] = kstest (x, "Tail", "larger");
 assert (h, true);
 assert (p, 5.085438806199252e-05, 1e-14);
 assert (k, 0.2197, 1e-4);
 assert (c, 0.1207, 1e-4);
21 tests, 21 passed, 0 known failure, 0 skipped
[inst/fullfact.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/fullfact.m
***** demo
 ## Full factorial design with 3 ordinal variables
 fullfact ([2, 3, 4])
***** error<fullfact: one input argument is required.> fullfact ();
***** error<fullfact: input argument must be a finite real numeric vector.> ...
 fullfact (Inf);
***** error<fullfact: input argument must be a finite real numeric vector.> ...
 fullfact (NaN);
***** error<fullfact: input argument must be a finite real numeric vector.> ...
 fullfact (ones (2));
***** error<fullfact: input argument must be a finite real numeric vector.> ...
 fullfact ([1, 2, NaN]);
***** error<fullfact: input argument must be a finite real numeric vector.> ...
 fullfact ([1, 2, Inf]);
***** error<fullfact: factor levels must be real positive integers.> fullfact (2.5);
***** error<fullfact: factor levels must be real positive integers.> fullfact (0);
***** error<fullfact: factor levels must be real positive integers.> fullfact (-3);
***** error<fullfact: factor levels must be real positive integers.> fullfact (3+2i);
***** error<fullfact: factor levels must be real positive integers.> fullfact ([1, 2, -3]);
***** error<fullfact: factor levels must be real positive integers.> fullfact ([0, 1, 2]);
***** test
 A = fullfact (1);
 assert (A, 1);
***** test
 A = fullfact (2);
 assert (A, [1; 2]);
***** test
***** test
 A = fullfact (3);
 assert (A, [1; 2; 3]);
***** test
 A = fullfact ([1, 2, 4]);
 A_out = [1, 1, 1; 1, 2, 1; 1, 1, 2; 1, 2, 2; ...
          1, 1, 3; 1, 2, 3; 1, 1, 4; 1, 2, 4];
 assert (A, A_out);
***** test
 A = fullfact ([2, 2]);
 assert (A, [1, 1; 2, 1; 1, 2; 2, 2]);
***** test
 A = fullfact ([2, 2, 4]);
 A_out = [1, 1, 1; 2, 1, 1; 1, 2, 1; 2, 2, 1; ...
          1, 1, 2; 2, 1, 2; 1, 2, 2; 2, 2, 2; ...
          1, 1, 3; 2, 1, 3; 1, 2, 3; 2, 2, 3; ...
          1, 1, 4; 2, 1, 4; 1, 2, 4; 2, 2, 4];
 assert (A, A_out);
***** test
 A = fullfact ([3, 2, 4]);
 A_out = [1, 1, 1; 2, 1, 1; 3, 1, 1; 1, 2, 1; 2, 2, 1; 3, 2, 1; ...
          1, 1, 2; 2, 1, 2; 3, 1, 2; 1, 2, 2; 2, 2, 2; 3, 2, 2; ...
          1, 1, 3; 2, 1, 3; 3, 1, 3; 1, 2, 3; 2, 2, 3; 3, 2, 3; ...
          1, 1, 4; 2, 1, 4; 3, 1, 4; 1, 2, 4; 2, 2, 4; 3, 2, 4];
 assert (A, A_out);
***** test
 A = fullfact ([4, 2]);
 assert (A, [1, 1; 2, 1; 3, 1; 4, 1; 1, 2; 2, 2; 3, 2; 4, 2]);
21 tests, 21 passed, 0 known failure, 0 skipped
[inst/pca.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/pca.m
***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
***** test
 x = [7, 4, 3; 4, 1, 8; 6, 3, 5; 8, 6, 1; 8, 5, 7; ...
      7, 2, 9; 5, 3, 3; 9, 5, 8; 7, 4, 5; 8, 2, 2];
 R = corrcoef (x);
 [V, lambda] = eig (R);
 [~, i] = sort (diag (lambda), "descend"); #arrange largest PC first
 S = V(:, i) * diag (sqrt (diag (lambda)(i)));
***** assert (diag (S(:, 1:2) * S(:, 1:2)'), [0.8662; 0.8420; 0.9876], 1E-4);
 B = V(:, i) * diag ( 1./ sqrt (diag (lambda)(i)));
 F = zscore (x) * B;
 [COEFF, SCORE, latent, tsquare] = pca (zscore (x, 1));
***** assert (tsquare, sumsq (F, 2), 1E4*eps);
***** test
 x = [1, 2, 3; 2, 1, 3]';
 [COEFF, SCORE, latent, tsquare] = pca (x, "Economy", false);
 m = [sqrt(2), sqrt(2); sqrt(2), -sqrt(2); -2*sqrt(2), 0] / 2;
 m(:,1) = m(:,1) * sign (COEFF(1,1));
 m(:,2) = m(:,2) * sign (COEFF(1,2));
***** assert (COEFF, m(1:2,:), 10*eps);
***** assert (SCORE, -m, 10*eps);
***** assert (latent, [1.5;.5], 10*eps);
***** assert (tsquare, [4;4;4]/3, 10*eps);
 [COEFF, SCORE, latent, tsquare] = pca (x, "Economy", false, "weights", ...
                                        [1 2 1], "variableweights", ...
                                        "variance");
***** assert (COEFF, [0.632455532033676, -0.632455532033676; ...
                0.741619848709566, 0.741619848709566], 10 * eps);
***** assert (SCORE, [-0.622019449426284, 0.959119380657905; ...
                -0.505649896847432, -0.505649896847431;
                1.633319243121148, 0.052180413036957], 10 * eps);
***** assert (latent, [1.783001790889027; 0.716998209110974], 10 * eps);
***** test assert (tsquare, [1.5; 0.5; 1.5], 10 * eps);
***** test
 x = [1,2,3;2,1,3]';
 [COEFF, SCORE, latent, tsquare] = pca (x, "Economy", false, "weights", ...
                                        [2 1 2], "variableweights", ...
                                        "variance");
 COEFF_exp = [0.7906, 0.7906; 0.6614, -0.6614];
 SCORE_exp = [-0.7836, -0.4813; -0.9071, 0.9071; 1.2372, 0.0277];
 latent_exp = [2.5562; 0.6438];
 tsquare_exp = [0.6000; 1.6000; 0.6000];
 assert (COEFF, COEFF_exp, 1e-4);
 assert (SCORE, SCORE_exp, 1e-4);
 assert (latent, latent_exp, 1e-4);
 assert (tsquare, tsquare_exp, 1e-4);
***** test
 x = [1,2,3;2,1,3]';
 [COEFF, SCORE, latent, tsquare] = pca (x, "Economy", false, "weights", ...
                                        [1 3 2], "variableweights", ...
                                        "variance");
 COEFF_exp = [0.6216, -0.6216; 0.8118, 0.8118];
 SCORE_exp = [-0.8358, 1.0411; -0.6473, -0.3792; 1.3889, 0.0482];
 latent_exp = [2.9067; 0.7599];
 tsquare_exp = [1.6667; 0.3333; 0.6667];
 assert (COEFF, COEFF_exp, 1e-4);
 assert (SCORE, SCORE_exp, 1e-4);
 assert (latent, latent_exp, 1e-4);
 assert (tsquare, tsquare_exp, 1e-4);
***** test
 x = [1,2,3;2,1,3]';
 [COEFF, SCORE, latent, tsquare] = pca (x, "Economy", false, "weights", ...
                                        [1 0.5 1.5], "variableweights", ...
                                        "variance");
 COEFF_exp = [0.8118, 0.8118; 0.6742, -0.6742];
 SCORE_exp = [-0.9657, -0.4713; -1.0915, 0.8862; 1.0076, 0.0188];
 latent_exp = [1.5257; 0.3077];
 tsquare_exp = [1.3333; 3.3333; 0.6667];
 assert (COEFF, COEFF_exp, 1e-4);
 assert (SCORE, SCORE_exp, 1e-4);
 assert (latent, latent_exp, 1e-4);
 assert (tsquare, tsquare_exp, 1e-4);
***** test
 x = [1,2,3;2,1,3]';
 [COEFF, SCORE, latent, tsquare] = pca(x, "Economy", true, "weights", ...
                                       [2 1 2], "variableweights", ...
                                       "variance");
 COEFF_exp = [0.7906,  0.7906; 0.6614, -0.6614];
 SCORE_exp = [-0.7836, -0.4813; -0.9071, 0.9071; 1.2372, 0.0277];
 latent_exp = [2.5562; 0.6438];
 tsquare_exp = [0.6000; 1.6000; 0.6000];
 assert (COEFF, COEFF_exp, 1e-4);
 assert (SCORE, SCORE_exp, 1e-4);
 assert (latent, latent_exp, 1e-4);
 assert (tsquare, tsquare_exp, 1e-4);
***** test
 x = [1,2,3;2,1,3]';
 [COEFF, SCORE, latent, tsquare] = pca (x, "Economy", true, "weights", ...
                                        [1 3 2], "variableweights", ...
                                        "variance");
 COEFF_exp = [0.6216, -0.6216; 0.8118, 0.8118];
 SCORE_exp = [-0.8358, 1.0411; -0.6473, -0.3792; 1.3889, 0.0482];
 latent_exp = [2.9067; 0.7599];
 tsquare_exp = [1.6667; 0.3333; 0.6667];
 assert (COEFF, COEFF_exp, 1e-4);
 assert (SCORE, SCORE_exp, 1e-4);
 assert (latent, latent_exp, 1e-4);
 assert (tsquare, tsquare_exp, 1e-4);
***** test
 x = [1,2,3;2,1,3]';
 [COEFF, SCORE, latent, tsquare] = pca (x, "Economy", true, "weights", ...
                                        [1 0.5 1.5], "variableweights", ...
                                        "variance");
 COEFF_exp = [0.8118, 0.8118; 0.6742, -0.6742];
 SCORE_exp = [-0.9657, -0.4713; -1.0915, 0.8862; 1.0076, 0.0188];
 latent_exp = [1.5257; 0.3077];
 tsquare_exp = [1.3333; 3.3333; 0.6667];
 assert (COEFF, COEFF_exp, 1e-4);
 assert (SCORE, SCORE_exp, 1e-4);
 assert (latent, latent_exp, 1e-4);
 assert (tsquare, tsquare_exp, 1e-4);
***** test
 x = x';
 [COEFF, SCORE, latent, tsquare] = pca (x, "Economy", false);
 m = [sqrt(2), sqrt(2), 0; -sqrt(2), sqrt(2), 0; 0, 0, 2] / 2;
 m(:,1) = m(:,1) * sign (COEFF(1,1));
 m(:,2) = m(:,2) * sign (COEFF(1,2));
 m(:,3) = m(:,3) * sign (COEFF(3,3));
***** assert (COEFF, m, 10*eps);
***** assert (SCORE(:,1), -m(1:2,1), 10*eps);
***** assert (SCORE(:,2:3), zeros(2), 10*eps);
***** assert (latent, [1;0;0], 10*eps);
***** assert (tsquare, [0.5;0.5], 10*eps)
***** test
 [COEFF, SCORE, latent, tsquare] = pca (x);
***** assert (COEFF, m(:, 1), 10*eps);
***** assert (SCORE, -m(1:2,1), 10*eps);
***** assert (latent, [1], 10*eps);
***** assert (tsquare, [0.5;0.5], 10*eps)
***** error <invalid algorithm> pca ([1 2; 3 4], "Algorithm", "xxx")
***** error <'centered' requires a boolean value> pca ([1 2; 3 4], "Centered", "xxx")
***** error <must be a positive integer> pca ([1 2; 3 4], "NumComponents", -4)
***** error <invalid value for rows> pca ([1 2; 3 4], "Rows", 1)
***** error <weights must be> pca ([1 2; 3 4], "Weights", [1 2 3])
***** error <weights must be> pca ([1 2; 3 4], "Weights", [-1 2])
***** error <variable weights must be> pca ([1 2; 3 4], "VariableWeights", [-1 2])
***** error <variable weights must be> pca ([1 2; 3 4], "VariableWeights", "xxx")
***** error <unknown property> pca ([1 2; 3 4], "XXX", 1)
38 tests, 38 passed, 0 known failure, 0 skipped
[inst/nanmax.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/nanmax.m
***** demo
 ## Find the column maximum values and their indices
 ## for matrix data with missing values.

 x = magic (3);
 x([1, 6:9]) = NaN
 [y, ind] = nanmax (x)
***** demo
 ## Find the maximum of all the values in an array, ignoring missing values.
 ## Create a 2-by-5-by-3 array x with some missing values.

 x = reshape (1:30, [2, 5, 3]);
 x([10:12, 25]) = NaN

 ## Find the maximum of the elements of x.

 y = nanmax (x, [], 'all')
***** assert (nanmax ([2, 4, NaN, 7]), 7)
***** assert (nanmax ([2, 4, NaN, Inf]), Inf)
***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [7, 8, 6])
***** assert (nanmax ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [3, 6, 8])
***** assert (nanmax (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([7, 8, 6]))
***** shared x, y
 x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
 x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
 y = x;
 y(2,3,1) = 0.51;
***** assert (nanmax (x, [], [1, 2])(:), [1.77;6.77])
***** assert (nanmax (x, [], [1, 3])(:), [6.77;5.34;NaN;5.19])
***** assert (nanmax (x, [], [2, 3])(:), [6.77;5.34])
***** assert (nanmax (x, [], [1, 2, 3]), 6.77)
***** assert (nanmax (x, [], 'all'), 6.77)
***** assert (nanmax (y, [], [1, 3])(:), [6.77;5.34;0.51;5.19])
***** assert (nanmax (x(1,:,1), x(2,:,1)), [1.77, 0.34, NaN, 0.19])
***** assert (nanmax (x(1,:,2), x(2,:,2)), [6.77, 5.34, NaN, 5.19])
***** assert (nanmax (y(1,:,1), y(2,:,1)), [1.77, 0.34, 0.51, 0.19])
***** assert (nanmax (y(1,:,2), y(2,:,2)), [6.77, 5.34, NaN, 5.19])
***** test
 xx = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (nanmax (xx, [], [3, 2])), [10, 1, 1, 3]);
 assert (size (nanmax (xx, [], [1, 2])), [1, 1, 6, 3]);
 assert (size (nanmax (xx, [], [1, 2, 4])), [1, 1, 6]);
 assert (size (nanmax (xx, [], [1, 4, 3])), [1, 40]);
 assert (size (nanmax (xx, [], [1, 2, 3, 4])), [1, 1]);
***** assert (nanmax (ones (2), [], 3), ones (2, 2))
***** assert (nanmax (ones (2, 2, 2), [], 99), ones (2, 2, 2))
***** assert (nanmax (magic (3), [], 3), magic (3))
***** assert (nanmax (magic (3), [], [1, 3]), [8, 9, 7])
***** assert (nanmax (magic (3), [], [1, 99]), [8, 9, 7])
***** assert (nanmax (ones (2), 3), 3 * ones (2,2))
***** error <nanmax: VECDIM must contain non-repeating positive integers.> ...
 nanmax (y, [], [1, 1, 2])
***** error <nanmax: a second output is not supported with this syntax.> ...
 [v, idx] = nanmax(x, y, [1 2])
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/datasample.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/datasample.m
***** error datasample();
***** error datasample(1);
***** error <data must be a vector or matrix> datasample({1, 2, 3}, 1);
***** error <k must be a positive integer scalar> datasample([1 2], -1);
***** error <k must be a positive integer scalar> datasample([1 2], 1.5);
***** error <k must be a positive integer scalar> datasample([1 2], [1 1]);
***** error <k must be a positive integer scalar> datasample([1 2], 'g', [1 1]);
***** error <DIM must be a positive integer scalar> datasample([1 2], 1, -1);
***** error <DIM must be a positive integer scalar> datasample([1 2], 1, 1.5);
***** error <DIM must be a positive integer scalar> datasample([1 2], 1, [1 1]);
***** error <Replace> datasample([1 2], 1, 1, "Replace", -2);
***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", "abc");
***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", [1 -2 3]);
***** error <weights must be defined> datasample([1 2], 1, 1, "Weights", ones (2));
***** error <weights must be equal> datasample([1 2], 1, 1, "Weights", [1 2 3]);
***** error <datasample: K must not exceed the number of available elements when sampling without replacement.> ...
 data = 1:5; weights = [0.077846, 0.103765, 0.703748, 0.840937, 0.422901];
 sampled = datasample (data, 8, 'Weights', weights, 'Replace', false);
***** error <datasample: sampling without replacement requires at least K elements with positive weights.> ...
 data = 1:5; weights = [1, 0, 1, 0, 0];
 sampled = datasample (data, 3, 'Weights', weights, 'Replace', false);
***** test
 dat = randn (10, 4);
 assert (size (datasample (dat, 3, 1)), [3 4]);
***** test
 dat = randn (10, 4);
 assert (size (datasample (dat, 3, 2)), [10 3]);
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/mhsample.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/mhsample.m
***** demo
 ## Define function to sample
 d = 2;
 mu = [-1; 2];
 rand ("seed", 5)  # for reproducibility
 Sigma = rand (d);
 Sigma = (Sigma + Sigma');
 Sigma += eye (d) * abs (eigs (Sigma, 1, "sa")) * 1.1;
 pdf = @(x)(2*pi)^(-d/2)*det(Sigma)^-.5*exp(-.5*sum((x.'-mu).*(Sigma\(x.'-mu)),1));
 ## Inputs
 start = ones (1, 2);
 nsamples = 500;
 sym = true;
 K = 500;
 m = 10;
 rand ("seed", 8)  # for reproducibility
 proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
 [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proprnd", proprnd, ...
                            "symmetric", sym, "burnin", K, "thin", m);
 figure;
 hold on;
 plot (smpl(:, 1), smpl(:, 2), 'x');
 [x, y] = meshgrid (linspace (-6, 4), linspace(-3, 7));
 z = reshape (pdf ([x(:), y(:)]), size(x));
 mesh (x, y, z, "facecolor", "None");
 ## Using sample points to find the volume of half a sphere with radius of .5
 f = @(x) ((.25-(x(:,1)+1).^2-(x(:,2)-2).^2).^.5.*(((x(:,1)+1).^2+(x(:,2)-2).^2)<.25)).';
 int = mean (f (smpl) ./ pdf (smpl));
 errest = std (f (smpl) ./ pdf (smpl)) / nsamples ^ .5;
 trueerr = abs (2 / 3 * pi * .25 ^ (3 / 2) - int);
 printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
 printf ("Monte Carlo integral error estimate %f\n", errest);
 printf ("The actual error %f\n", trueerr);
 mesh (x, y, reshape (f([x(:), y(:)]), size(x)), "facecolor", "None");
***** demo
 ## Integrate truncated normal distribution to find normalization constant
 pdf = @(x) exp (-.5*x.^2)/(pi^.5*2^.5);
 nsamples = 1e3;
 rand ("seed", 5)  # for reproducibility
 proprnd = @(x) (rand (size (x)) - .5) * 3 + x;
 [smpl, accept] = mhsample (1, nsamples, "pdf", pdf, "proprnd", proprnd, ...
                            "symmetric", true, "thin", 4);
 f = @(x) exp(-.5 * x .^ 2) .* (x >= -2 & x <= 2);
 x = linspace (-3, 3, 1000);
 area(x, f(x));
 xlabel ('x');
 ylabel ('f(x)');
 int = mean (f (smpl) ./ pdf (smpl));
 errest = std (f (smpl) ./ pdf (smpl)) / nsamples^ .5;
 trueerr = abs (erf (2 ^ .5) * 2 ^ .5 * pi ^ .5 - int);
 printf ("Monte Carlo integral estimate int f(x) dx = %f\n", int);
 printf ("Monte Carlo integral error estimate %f\n", errest);
 printf ("The actual error %f\n", trueerr);
***** test
 nchain = 1e4;
 start = rand (nchain, 1);
 nsamples = 1e3;
 pdf = @(x) exp (-.5*(x-1).^2)/(2*pi)^.5;
 proppdf = @(x, y) 1/3;
 proprnd = @(x) 3 * (rand (size (x)) - .5) + x;
 [smpl, accept] = mhsample (start, nsamples, "pdf", pdf, "proppdf", proppdf, ...
                            "proprnd", proprnd, "thin", 2, "nchain", nchain, ...
                            "burnin", 0);
 assert (mean (mean (smpl, 1), 3), 1, .01);
 assert (mean (var (smpl, 1), 3), 1, .01)
***** error mhsample ();
***** error mhsample (1);
***** error mhsample (1, 1);
***** error mhsample (1, 1, "pdf", @(x)x);
***** error mhsample (1, 1, "pdf", @(x)x, "proprnd", @(x)x+rand(size(x)));
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/mnrfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/mnrfit.m
***** error<mnrfit: too few input arguments.> mnrfit (ones (50,1))
***** error<mnrfit: Predictors must be numeric.> ...
 mnrfit ({1 ;2 ;3 ;4 ;5}, ones (5,1))
***** error<mnrfit: Predictors must be a vector or a 2D matrix.> ...
 mnrfit (ones (50, 4, 2), ones (50, 1))
***** error<mnrfit: Response must be a vector or a 2D matrix.> ...
 mnrfit (ones (50, 4), ones (50, 1, 3))
***** error<mnrfit: Y must have the same number of rows as X.> ...
 mnrfit (ones (50, 4), ones (45,1))
***** error<mnrfit: Response labels must be a character array, a cell vector> ...
 mnrfit (ones (5, 4), {1 ;2 ;3 ;4 ;5})
***** error<mnrfit: optional arguments must be in pairs.> ...
 mnrfit (ones (5, 4), ones (5, 1), "model")
***** error<mnrfit: Y must be a column vector when given as cellstr.> ...
 mnrfit (ones (5, 4), {"q","q";"w","w";"q","q";"w","w";"q","q"})
***** error<mnrfit: Y must contain only 1 and 0 when given as a 2D matrix.> ...
 mnrfit (ones (5, 4), [1, 2; 1, 2; 1, 2; 1, 2; 1, 2])
***** error<mnrfit: Y must contain positive integer category numbers.> ...
 mnrfit (ones (5, 4), [1; -1; 1; 2; 1])
***** error<mnrfit: fitting more than 2 nominal responses not supported.> ...
 mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "nominal")
***** error<mnrfit: fitting hierarchical responses not supported.> ...
 mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "hierarchical")
***** error<mnrfit: model type not recognised.> ...
 mnrfit (ones (5, 4), [1; 2; 3; 2; 1], "model", "whatever")
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/gscatter.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/gscatter.m
***** demo
 load fisheriris;
 X = meas(:,3:4);
 cidcs = kmeans (X, 3, "Replicates", 5);
 gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
 title ("Fisher's iris data");
***** shared visibility_setting
 visibility_setting = get (0, "DefaultFigureVisible");
***** test
 hf = figure ("visible", "off");
 unwind_protect
   load fisheriris;
   X = meas(:,3:4);
   cidcs = kmeans (X, 3, "Replicates", 5);
   gscatter (X(:,1), X(:,2), cidcs, [.75 .75 0; 0 .75 .75; .75 0 .75], "os^");
   title ("Fisher's iris data");
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
warning: legend: 'best' not yet implemented for location specifier, using 'northeast' instead
***** error gscatter ();
***** error gscatter ([1]);
***** error gscatter ([1], [2]);
***** error <x must be a numeric vector> gscatter ('abc', [1 2 3], [1]);
***** error <x and y must have the same size> gscatter ([1 2 3], [1 2], [1]);
***** error <y must be a numeric vector> gscatter ([1 2 3], 'abc', [1]);
***** error <g must have the same size as x and y> gscatter ([1 2], [1 2], [1]);
***** error <invalid dolegend> gscatter ([1 2], [1 2], [1 2], 'rb', 'so', 12, 'xxx');
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/binotest.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/binotest.m
***** demo
 % flip a coin 1000 times, showing 475 heads
 % Hypothesis: coin is fair, i.e. p=1/2
 [h,p_val,ci] = binotest(475,1000,0.5)
 % Result: h = 0 : null hypothesis not rejected, coin could be fair
 %         P value 0.12, i.e. hypothesis not rejected for alpha up to 12%
 %         0.444 <= p <= 0.506 with 95% confidence
***** demo
 % flip a coin 100 times, showing 65 heads
 % Hypothesis: coin shows less than 50% heads, i.e. p<=1/2
 [h,p_val,ci] = binotest(65,100,0.5,'tail','left','alpha',0.01)
 % Result: h = 1 : null hypothesis is rejected, i.e. coin shows more heads than tails
 %         P value 0.0018, i.e. hypothesis not rejected for alpha up to 0.18%
 %         0 <= p <= 0.76 with 99% confidence
***** test #example from https://en.wikipedia.org/wiki/Binomial_test
 [h,p_val,ci] = binotest (51,235,1/6);
 assert (p_val, 0.0437, 0.00005)
 [h,p_val,ci] = binotest (51,235,1/6,'tail','left');
 assert (p_val, 0.027, 0.0005)
1 test, 1 passed, 0 known failure, 0 skipped
[inst/factoran.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/factoran.m
***** demo
 x = [ 7    26     6    60;
       1    29    15    52;
      11    56     8    20;
      11    31     8    47;
       7    52     6    33;
      11    55     9    22;
       3    71    17     6;
       1    31    22    44;
       2    54    18    22;
      21    47     4    26;
       1    40    23    34;
      11    66     9    12;
      10    68     8    12
     ];
 [loadings, specvar, fscores] = factoran (x, 2);
***** test
 x = [1, 2; 2, 1; 3, 3];
 [loadings, specvar, fscores] = factoran (x, 1);
 l_out = [0.7071; 0.7071];
 s_out = [0.5000; 0.5000];
 f_out = [-0.7071; -0.7071; 1.4142];
 assert (loadings, l_out, 1.3e-4);
 assert (specvar, s_out, 1.3e-4);
 assert (fscores, f_out, 1.3e-4);
***** error factoran ()
***** error factoran (ones (5,3), 0)
***** error factoran (ones (5,3), 3)
***** error<factoran: X must be a numeric matrix.> factoran ({1,2}, 1)
***** error<factoran: X must be a numeric matrix.> factoran (ones (2,2,2), 1)
***** error<EIG: matrix contains Inf or NaN values> x=ones (3,2); x(:,2)=0; factoran (x,1)
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/mcnemar_test.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/mcnemar_test.m
***** test
 [h, pval, chisq] = mcnemar_test ([101,121;59,33]);
 assert (h, 1);
 assert (pval, 3.8151e-06, 1e-10);
 assert (chisq, 21.356, 1e-3);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80]);
 assert (h, 1);
 assert (pval, 0.034690, 1e-6);
 assert (isempty (chisq), true);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.01);
 assert (h, 0);
 assert (pval, 0.034690, 1e-6);
 assert (isempty (chisq), true);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], "mid-p");
 assert (h, 1);
 assert (pval, 0.034690, 1e-6);
 assert (isempty (chisq), true);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], "asymptotic");
 assert (h, 1);
 assert (pval, 0.033006, 1e-6);
 assert (chisq, 4.5455, 1e-4);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], "exact");
 assert (h, 0);
 assert (pval, 0.052479, 1e-6);
 assert (isempty (chisq), true);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], "corrected");
 assert (h, 0);
 assert (pval, 0.055009, 1e-6);
 assert (chisq, 3.6818, 1e-4);
***** test
 [h, pval, chisq] = mcnemar_test ([59,6;16,80], 0.1, "corrected");
 assert (h, 1);
 assert (pval, 0.055009, 1e-6);
 assert (chisq, 3.6818, 1e-4);
***** error<mcnemar_test: too many input arguments.> mcnemar_test (59, 6, 16, 80)
***** error<mcnemar_test: X must be a 2x2 matrix.> mcnemar_test (ones (3, 3))
***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
 mcnemar_test ([59,6;16,-80])
***** error<mcnemar_test: all entries of X must be non-negative integers.> ...
 mcnemar_test ([59,6;16,4.5])
***** error<mcnemar_test: invalid 2nd input argument.> ...
 mcnemar_test ([59,6;16,80], {""})
***** error<mcnemar_test: invalid value for ALPHA.> ...
 mcnemar_test ([59,6;16,80], -0.2)
***** error<mcnemar_test: invalid value for ALPHA.> ...
 mcnemar_test ([59,6;16,80], [0.05, 0.1])
***** error<mcnemar_test: invalid value for ALPHA.> ...
 mcnemar_test ([59,6;16,80], 1)
***** error<mcnemar_test: invalid value for TESTTYPE.> ...
 mcnemar_test ([59,6;16,80], "")
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/vartest.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/vartest.m
***** error<vartest: too few input arguments.> vartest ();
***** error<vartest: invalid value for variance.> vartest ([1, 2, 3, 4], -0.5);
***** error<vartest: invalid value for alpha.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0);
***** error<vartest: invalid value for alpha.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 1.2);
***** error<vartest: invalid value for alpha.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", "val");
***** error<vartest: invalid value for tail.>  ...
 vartest ([1, 2, 3, 4], 1, "tail", "val");
***** error<vartest: invalid value for tail.>  ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "val");
***** error<vartest: invalid value for operating dimension.> ...
 vartest ([1, 2, 3, 4], 1, "dim", 3);
***** error<vartest: invalid value for operating dimension.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "dim", 3);
***** error<vartest: invalid name for optional arguments.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail", "both", "badoption", 3);
***** error<vartest: optional arguments must be in name/value pairs.> ...
 vartest ([1, 2, 3, 4], 1, "alpha", 0.01, "tail");
***** test
 load carsmall
 [h, pval, ci] = vartest (MPG, 7^2);
 assert (h, 1);
 assert (pval, 0.04335086742174443, 1e-14);
 assert (ci, [49.397; 88.039], 1e-3);
***** test
 load carsmall
 [h, pval, ci] = vartest (MPG, 7^2, "tail", "left");
 assert (h, 0);
 assert (pval, 0.978324566289128, 1e-14);
 assert (ci, [0; 83.685], 1e-3);
***** test
 load carsmall
 [h, pval, ci] = vartest (MPG, 7^2, "tail", "right");
 assert (h, 1);
 assert (pval, 0.021675433710872, 1e-14);
 assert (ci, [51.543; Inf], 1e-3);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/Clustering/hnswSearcher.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Clustering/hnswSearcher.m
***** demo
 ## Create an hnswSearcher with Euclidean distance
 X = [1, 2; 3, 4; 5, 6];
 obj = hnswSearcher (X);
 ## Find the nearest neighbor to [2, 3]
 Y = [2, 3];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 disp ("Nearest neighbor index:");
 disp (idx);
 disp ("Distance:");
 disp (D);
***** demo
 ## Create an hnswSearcher with Minkowski distance (P=3)
 X = [0, 0; 1, 0; 2, 0];
 obj = hnswSearcher (X, "Distance", "minkowski", "P", 3);
 ## Find the nearest neighbor to [1, 0]
 Y = [1, 0];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 disp ("Nearest neighbor index:");
 disp (idx);
 disp ("Distance:");
 disp (D);
***** test
 load fisheriris
 X = meas;
 obj = hnswSearcher (X, "Distance", "chebychev");
 Y = X(30:35,:);
 [idx, D] = knnsearch (obj, Y, "K", 4);
 assert (idx, [[30 31 4 12]; [31 30 10 35]; [32 21 37 28]; [33 47 20 34]; ...
               [34 16 33 15]; [35 10 26 2]])
 assert (D, [[0 0.1000 0.1000 0.2000]; [0 0.1000 0.1000 0.1000]; [0 0.2000 ...
              0.2000 0.2000]; [0 0.3000 0.3000 0.3000]; [0 0.2000 0.3000 ...
              0.3000]; [0 0.1000 0.1000 0.1000]], 5e-15)
***** test
 load fisheriris
 X = meas;
 C = cov (X);
 obj = hnswSearcher (X, "Distance", "mahalanobis", "Cov", C);
 Y = X(120:125,:);
 [idx, D] = knnsearch (obj, Y, "K", 2);
 assert (idx(1, :), [120 82])
 assert (idx(4, :), [123 106])
 assert (idx(5, :), [124 127])
 assert (idx(6, :), [125 57])
 assert (D(1, :), [0 0.7734], 1e-4)
 assert (D(4, :), [0 0.8452], 1e-4)
 assert (D(5, :), [0 0.4152], 1e-4)
 assert (D(6, :), [0 0.7322], 1e-4)
***** test
 ## Basic constructor with default Euclidean
 X = [1, 2; 3, 4; 5, 6];
 obj = hnswSearcher (X);
 assert (obj.X, X);
 assert (obj.Distance, "euclidean");
 assert (isempty (obj.DistParameter));
***** test
 ## Minkowski distance with custom P
 X = [0, 0; 1, 1; 2, 2];
 obj = hnswSearcher (X, "Distance", "minkowski", "P", 3);
 assert (obj.Distance, "minkowski");
 assert (obj.DistParameter, 3);
***** test
 ## Seuclidean distance with custom Scale
 X = [1, 2; 3, 4; 5, 6];
 S = [1, 2];
 obj = hnswSearcher (X, "Distance", "seuclidean", "Scale", S);
 assert (obj.Distance, "seuclidean");
 assert (obj.DistParameter, S);
***** test
 ## Mahalanobis distance with custom Cov
 X = [1, 2; 3, 4; 5, 6];
 C = [1, 0; 0, 1];
 obj = hnswSearcher (X, "Distance", "mahalanobis", "Cov", C);
 assert (obj.Distance, "mahalanobis");
 assert (obj.DistParameter, C);
***** test
 ## knnsearch with Euclidean distance
 X = [1, 2; 3, 4; 5, 6];
 obj = hnswSearcher (X);
 Y = [2, 3];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (ismember (idx, [2]));
 assert (abs (D - sqrt(2)) < 1e-2);
***** test
 ## knnsearch with Cityblock distance
 X = [0, 0; 1, 1; 2, 2];
 obj = hnswSearcher (X, "Distance", "cityblock");
 Y = [1, 0];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (ismember (idx, [1, 2]));
 assert (abs (D - 1) < 1e-2);
***** test
 ## knnsearch with Chebychev distance
 X = [1, 1; 2, 3; 4, 2];
 obj = hnswSearcher (X, "Distance", "chebychev");
 Y = [2, 2];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (ismember (idx, [1, 2]));
 assert (abs (D - 1) < 1e-2);
***** test
 ## knnsearch with Minkowski P=3
 X = [0, 0; 1, 0; 2, 0];
 obj = hnswSearcher (X, "Distance", "minkowski", "P", 3);
 Y = [1, 0];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (ismember (idx, [2]));
 assert (abs (D - 0) < 1e-2);
***** test
 ## Diverse dataset with Euclidean
 X = [0, 10; 5, 5; 10, 0];
 obj = hnswSearcher (X);
 Y = [5, 5];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (ismember (idx, [2]));
 assert (abs (D - 0) < 1e-2);
***** test
 ## High-dimensional data with Cityblock
 X = [1, 2, 3; 4, 5, 6; 7, 8, 9];
 obj = hnswSearcher (X, "Distance", "cityblock");
 Y = [4, 5, 6];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (ismember (idx, [2]));
 assert (abs (D - 0) < 1e-2);
***** error<hnswSearcher: too few input arguments.> ...
 hnswSearcher ()
***** error<hnswSearcher: Name-Value arguments must be in pairs.> ...
 hnswSearcher (ones(3,2), "Distance")
***** error<hnswSearcher: X cannot be empty.> ...
 hnswSearcher ([])
***** error<hnswSearcher: X must be a finite numeric matrix.> ...
 hnswSearcher ("abc")
***** error<hnswSearcher: X must be a finite numeric matrix.> ...
 hnswSearcher ([1; Inf; 3])
***** error<hnswSearcher: invalid parameter name: 'foo'.> ...
 hnswSearcher (ones(3,2), "foo", "bar")
***** error<hnswSearcher: unsupported distance metric 'invalid'.> ...
 hnswSearcher (ones(3,2), "Distance", "invalid")
***** error<hnswSearcher: 'Distance' must be a string.> ...
 hnswSearcher (ones(3,2), "Distance", 1)
***** error<hnswSearcher: 'P' must be a positive finite scalar.> ...
 hnswSearcher (ones(3,2), "Distance", "minkowski", "P", -1)
***** error<hnswSearcher: 'Scale' must be a nonnegative vector matching X columns.> ...
 hnswSearcher (ones(3,2), "Distance", "seuclidean", "Scale", [-1, 1])
***** error<hnswSearcher: 'Cov' must be a square matrix matching X columns.> ...
 hnswSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", ones(3,3))
***** error<hnswSearcher: 'Cov' must be symmetric for mahalanobis.> ...
 hnswSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", [1, 2; 3, 4])
***** error<hnswSearcher: 'Cov' must be positive definite for mahalanobis.> ...
 hnswSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", -eye(2))
***** error<hnswSearcher: 'MaxNumLinksPerNode' must be a positive integer.> ...
 hnswSearcher (ones(3,2), "MaxNumLinksPerNode", 0)
***** error<hnswSearcher: 'TrainSetSize' must be a positive integer.> ...
 hnswSearcher (ones(3,2), "TrainSetSize", -1)
***** error<hnswSearcher: 'TrainSetSize' cannot exceed the number of rows in X.> ...
 hnswSearcher (ones(3,2), "TrainSetSize", 4)
***** error<hnswSearcher: 'TrainSetSize' cannot exceed the number of rows in X.> ...
 hnswSearcher (ones(3,2), "MaxNumLinksPerNode", 200, "TrainSetSize", 100)
***** error<hnswSearcher.knnsearch: too few input arguments.> ...
 knnsearch (hnswSearcher (ones(3,2)))
***** error<hnswSearcher.knnsearch: Name-Value arguments must be in pairs.> ...
 knnsearch (hnswSearcher (ones(3,2)), ones(3,2), "K")
***** error<hnswSearcher.knnsearch: Y cannot be empty.> ...
 knnsearch (hnswSearcher (ones(3,2)), [])
***** error<hnswSearcher.knnsearch: Y must be a finite numeric matrix.> ...
 knnsearch (hnswSearcher (ones(3,2)), "abc")
***** error<hnswSearcher.knnsearch: Y must have the same number of columns as the training data in OBJ.X.> ...
 knnsearch (hnswSearcher (ones(3,2)), ones(3,3))
***** error<hnswSearcher.knnsearch: 'K' must be a positive integer.> ...
 knnsearch (hnswSearcher (ones(3,2)), ones(3,2), "K", 0)
***** error<hnswSearcher.knnsearch: invalid parameter name: 'foo'.> ...
 knnsearch (hnswSearcher (ones(3,2)), ones(3,2), "foo", "bar")
***** error<hnswSearcher.subsref: \(\) indexing not supported.> ...
 obj = hnswSearcher (ones(3,2)); obj(1)
***** error<hnswSearcher.subsref: {} indexing not supported.> ...
 obj = hnswSearcher (ones(3,2)); obj{1}
***** error<hnswSearcher.subsref: unrecognized property: 'invalid'.> ...
 obj = hnswSearcher (ones(3,2)); obj.invalid
***** error<hnswSearcher.subsasgn: \(\) indexing not supported.> ...
 obj = hnswSearcher (ones(3,2)); obj(1) = 1
***** error<hnswSearcher.subsasgn: {} indexing not supported.> ...
 obj = hnswSearcher (ones(3,2)); obj{1} = 1
***** error<hnswSearcher.subsasgn: 'X' is read-only and cannot be modified.> ...
 obj = hnswSearcher (ones(3,2)); obj.X = 1
***** error<hnswSearcher.subsasgn: 'HNSWGraph' is read-only and cannot be modified.> ...
 obj = hnswSearcher (ones(3,2)); obj.HNSWGraph = 1
***** error<hnswSearcher.subsasgn: 'Distance' is read-only and cannot be modified.> ...
 obj = hnswSearcher (ones(3,2)); obj.Distance = "invalid"
***** error<hnswSearcher.subsasgn: 'Distance' is read-only and cannot be modified.> ...
 obj = hnswSearcher (ones(3,2)); obj.Distance = 1
***** error<hnswSearcher.subsasgn: 'DistParameter' is read-only and cannot be modified.> ...
 obj = hnswSearcher (ones(3,2), "Distance", "minkowski"); obj.DistParameter = -1
***** error<hnswSearcher.subsasgn: 'DistParameter' is read-only and cannot be modified.> ...
 obj = hnswSearcher (ones(3,2), "Distance", "seuclidean"); obj.DistParameter = [-1, 1]
***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric and positive definite.> ...
 obj = hnswSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = ones(3,3)
***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric and positive definite.> ...
 obj = hnswSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = -eye(2)
***** error<hnswSearcher.subsasgn: 'DistParameter' is read-only and cannot be modified.> ...
 obj = hnswSearcher (ones(3,2)); obj.DistParameter = 1
***** error<hnswSearcher.subsasgn: 'MaxNumLinksPerNode' is read-only and cannot be modified.> ...
 obj = hnswSearcher (ones(3,2)); obj.MaxNumLinksPerNode = 0
***** error<hnswSearcher.subsasgn: 'TrainSetSize' is read-only and cannot be modified.> ...
 obj = hnswSearcher (ones(3,2)); obj.TrainSetSize = -1
***** error<hnswSearcher.subsasgn: unrecognized property: 'efSearch'.> ...
 obj = hnswSearcher (ones(3,2)); obj.efSearch = 1.5
***** error<hnswSearcher.subsasgn: unrecognized property: 'invalid'.> ...
 obj = hnswSearcher (ones(3,2)); obj.invalid = 1
54 tests, 54 passed, 0 known failure, 0 skipped
[inst/Clustering/cvpartition.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Clustering/cvpartition.m
***** test
 custom = [1, 1, 1, 2, 2, 2, 1, 2, 3, 2, 3, 3, 2, 1, 3]';
 cv = cvpartition ('CustomPartition', custom);
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 15);
 assert (cv.NumTestSets, 3);
 assert (cv.TrainSize, [10, 9, 11]);
 assert (cv.TestSize, [5, 6, 4]);
 assert (cv.IsCustom, true);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, false);
 idx = training (cv, 1);
 assert (idx, custom != 1);
 idx = test (cv, 1);
 assert (idx, custom == 1);
 idx = training (cv, 2);
 assert (idx, custom != 2);
 idx = test (cv, 2);
 assert (idx, custom == 2);
 idx = training (cv, 3);
 assert (idx, custom != 3);
 idx = test (cv, 3);
 assert (idx, custom == 3);
 idx1 = training (cv, 'all');
 idx2 = test (cv, 'all');
 assert (idx1, ! idx2);
***** test
 custom = logical ([1, 1, 1, 0, 0, 0, 1, 0, 1, 1])';
 cv = cvpartition ('CustomPartition', custom);
 assert (cv.Type, 'holdout');
 assert (cv.NumObservations, 10);
 assert (cv.NumTestSets, 1);
 assert (cv.TrainSize, 4);
 assert (cv.TestSize, 6);
 assert (cv.IsCustom, true);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, false);
 idx = training (cv, 1);
 assert (idx, custom != 1);
 assert (idx, training (cv, 'all'));
 idx = test (cv, 1);
 assert (idx, custom == 1);
 assert (idx, test (cv, 'all'));
***** test
 custom = logical ([1, 0, 0; 0, 1, 0; 1, 0, 0; 0, 0, 1]);
 cv = cvpartition ('CustomPartition', custom);
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 4);
 assert (cv.NumTestSets, 3);
 assert (cv.TrainSize, [2, 3, 3]);
 assert (cv.TestSize, [2, 1, 1]);
 assert (cv.IsCustom, true);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, false);
 idx = training (cv, 1);
 assert (idx, custom(:,1) == false);
 idx = test (cv, 1);
 assert (idx, custom(:,1) == true);
 idx = training (cv, 2);
 assert (idx, custom(:,2) == false);
 idx = test (cv, 2);
 assert (idx, custom(:,2) == true);
 assert (! custom, training (cv, 'all'));
 assert (custom, test (cv, 'all'));
***** test
 cv = cvpartition ('CustomPartition', [1:8]);
 assert (cv.Type, 'leaveout');
 assert (cv.NumObservations, 8);
 assert (cv.NumTestSets, 8);
 assert (cv.TrainSize, [7, 7, 7, 7, 7, 7, 7, 7]);
 assert (cv.TestSize, [1, 1, 1, 1, 1, 1, 1, 1]);
 assert (cv.IsCustom, true);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, false);
 assert (class (training (cv, 1)), 'logical');
 assert (sum (training (cv, 1)), 7);
 assert (sum (training (cv, 'all')), cv.TrainSize);
 assert (class (test (cv, 1)), 'logical');
 assert (sum (test (cv, 1)), 1);
 assert (sum (test (cv, 'all')), cv.TestSize);
 assert (! training (cv, 'all'), test (cv, 'all'));
***** test
 cv = cvpartition ('CustomPartition', logical (eye (8)));
 assert (cv.Type, 'leaveout');
 assert (cv.NumObservations, 8);
 assert (cv.NumTestSets, 8);
 assert (cv.TrainSize, [7, 7, 7, 7, 7, 7, 7, 7]);
 assert (cv.TestSize, [1, 1, 1, 1, 1, 1, 1, 1]);
 assert (cv.IsCustom, true);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, false);
 assert (class (training (cv, 1)), 'logical');
 assert (sum (training (cv, 1)), 7);
 assert (sum (training (cv, 'all')), cv.TrainSize);
 assert (class (test (cv, 1)), 'logical');
 assert (sum (test (cv, 1)), 1);
 assert (sum (test (cv, 'all')), cv.TestSize);
 assert (! training (cv, 'all'), test (cv, 'all'));
***** test
 cv = cvpartition (10, 'resubstitution');
 assert (cv.Type, 'resubstitution');
 assert (cv.NumObservations, 10);
 assert (cv.NumTestSets, 1);
 assert (cv.TrainSize, 10);
 assert (cv.TestSize, 10);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, false);
 assert (class (training (cv, 1)), 'logical');
 assert (sum (training (cv, 1)), 10);
 assert (training (cv, 'all'), logical (ones (10, 1)));
 assert (class (test (cv, 1)), 'logical');
 assert (sum (test (cv, 1)), 10);
 assert (test (cv, 'all'), logical (ones (10, 1)));
 assert (test (cv), training (cv));
***** test
 cv = cvpartition (10, 'leaveout');
 assert (cv.Type, 'leaveout');
 assert (cv.NumObservations, 10);
 assert (cv.NumTestSets, 10);
 assert (cv.TrainSize, ones (1, 10) * 9);
 assert (cv.TestSize, ones (1, 10));
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, false);
 assert (class (training (cv, 1)), 'logical');
 assert (sum (training (cv, 1)), 9);
 assert (training (cv, 'all'), ! logical (eye (10)));
 assert (class (test (cv, 1)), 'logical');
 assert (sum (test (cv, 1)), 1);
 assert (test (cv, 'all'), logical (eye (10)));
 assert (test (cv), ! training (cv));
 assert (test (cv, 'all'), ! training (cv, 'all'));
***** test
 rand ('seed', 5);  # for reproducibility
 cv = cvpartition (10, 'holdout', 0.3);
 assert (cv.Type, 'holdout');
 assert (cv.NumObservations, 10);
 assert (cv.NumTestSets, 1);
 assert (cv.TrainSize, 7);
 assert (cv.TestSize, 3);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, false);
 assert (class (training (cv, 1)), 'logical');
 assert (sum (training (cv, 1)), 7);
 assert (training (cv, 'all'), logical ([1, 0, 1, 1, 0, 1, 1, 1, 0, 1])');
 assert (class (test (cv, 1)), 'logical');
 assert (sum (test (cv, 1)), 3);
 assert (test (cv, 'all'), logical ([0, 1, 0, 0, 1, 0, 0, 0, 1, 0])');
 assert (test (cv), ! training (cv));
 assert (test (cv, 'all'), ! training (cv, 'all'));
***** test
 cv = cvpartition (10, 'holdout', 4);
 assert (cv.Type, 'holdout');
 assert (cv.NumObservations, 10);
 assert (cv.NumTestSets, 1);
 assert (cv.TrainSize, 6);
 assert (cv.TestSize, 4);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, false);
 assert (class (training (cv, 1)), 'logical');
 assert (sum (training (cv, 1)), 6);
 assert (class (test (cv, 1)), 'logical');
 assert (sum (test (cv, 1)), 4);
 assert (test (cv), ! training (cv));
 assert (test (cv, 'all'), ! training (cv, 'all'));
***** test
 cv = cvpartition (5, 'holdout', 4);
 assert (cv.Type, 'holdout');
 assert (cv.NumObservations, 5);
 assert (cv.NumTestSets, 1);
 assert (cv.TrainSize, 1);
 assert (cv.TestSize, 4);
 assert (sum (test (cv, 1)), 4);
***** test
 cv = cvpartition (5, 'holdout', 1);
 assert (cv.Type, 'holdout');
 assert (cv.NumObservations, 5);
 assert (cv.NumTestSets, 1);
 assert (cv.TrainSize, 4);
 assert (cv.TestSize, 1);
 assert (sum (test (cv, 1)), 1);
***** test
 cv = cvpartition (5, 'kfold');
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 5);
 assert (cv.NumTestSets, 5);
***** test
 cv = cvpartition (20, 'kfold');
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 20);
 assert (cv.NumTestSets, 10);
***** test
 cv = cvpartition (10, 'kfold', 5);
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 10);
 assert (cv.NumTestSets, 5);
 assert (cv.TrainSize, [8, 8, 8, 8, 8]);
 assert (cv.TestSize, [2, 2, 2, 2, 2]);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, false);
 assert (test (cv, 1), ! training (cv, 1));
 assert (test (cv, 'all'), ! training (cv, 'all'));
 assert (size (test (cv, 'all')), [10, 5]);
***** test
 grpvar = [1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 5, 5];
 rand ('seed', 5);
 cv = cvpartition (12, 'kfold', 5, 'GroupingVariables', grpvar);
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 12);
 assert (cv.NumTestSets, 5);
 assert (cv.TrainSize, [10, 10, 10, 8, 10]);
 assert (cv.TestSize, [2, 2, 2, 4, 2]);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, true);
 assert (cv.IsStratified, false);
 assert (test (cv, 1), ! training (cv, 1));
 assert (test (cv, 'all'), ! training (cv, 'all'));
 assert (size (test (cv, 'all')), [12, 5]);
 assert (sum (test (cv, 'all')), [2, 2, 2, 4, 2]);
 assert (sum (training (cv, 'all')), [10, 10, 10, 8, 10]);
***** test
 grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3];
 rand ('seed', 5);
 cv = cvpartition (12, 'kfold', 3, 'GroupingVariables', grpvar);
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 12);
 assert (cv.NumTestSets, 3);
 assert (cv.TrainSize, [9, 10, 5]);
 assert (cv.TestSize, [3, 2, 7]);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, true);
 assert (cv.IsStratified, false);
 assert (test (cv, 1), ! training (cv, 1));
 assert (test (cv, 'all'), ! training (cv, 'all'));
 assert (size (test (cv, 'all')), [12, 3]);
 assert (sum (test (cv, 'all')), [3, 2, 7]);
 assert (sum (training (cv, 'all')), [9, 10, 5]);
***** test
 grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3];
 rand ('seed', 5);
 cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 12);
 assert (cv.NumTestSets, 2);
 assert (cv.TrainSize, [6, 6]);
 assert (cv.TestSize, [6, 6]);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, true);
 assert (cv.IsStratified, false);
 assert (test (cv, 1), ! training (cv, 1));
 assert (test (cv, 'all'), ! training (cv, 'all'));
 assert (size (test (cv, 'all')), [12, 2]);
 assert (sum (test (cv, 'all')), [6, 6]);
 assert (sum (training (cv, 'all')), [6, 6]);
***** test
 grpvar = [1, 1, 1, 2, 2, 2, 2, NaN, 2, 3, 3, 3];
 rand ('seed', 5);
 cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 12);
 assert (cv.NumTestSets, 2);
 assert (cv.TrainSize, [6, 5]);
 assert (cv.TestSize, [5, 6]);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, true);
 assert (cv.IsStratified, false);
 idx = ! isnan (grpvar);
 assert (test (cv, 1)(idx), ! training (cv, 1)(idx));
 assert (test (cv, 'all')(idx, :), ! training (cv, 'all')(idx, :));
 assert (size (test (cv, 'all')), [12, 2]);
 assert (sum (test (cv, 'all')), [5, 6]);
 assert (sum (training (cv, 'all')), [6, 5]);
***** test
 grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 3];
 rand ('seed', 5);
 cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 12);
 assert (cv.NumTestSets, 2);
 assert (cv.TrainSize, [5, 7]);
 assert (cv.TestSize, [7, 5]);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, true);
 assert (cv.IsStratified, false);
 assert (test (cv, 1), ! training (cv, 1));
 assert (test (cv, 'all'), ! training (cv, 'all'));
 assert (size (test (cv, 'all')), [12, 2]);
 assert (sum (test (cv, 'all')), [7, 5]);
 assert (sum (training (cv, 'all')), [5, 7]);
 assert (test (cv, 1)', grpvar == 2);
 assert (test (cv, 2)', grpvar != 2);
***** test
 grpvar = [1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3];
 rand ('seed', 5);
 cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 12);
 assert (cv.NumTestSets, 2);
 assert (cv.TrainSize, [7, 5]);
 assert (cv.TestSize, [5, 7]);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, true);
 assert (cv.IsStratified, false);
 assert (test (cv, 1), ! training (cv, 1));
 assert (test (cv, 'all'), ! training (cv, 'all'));
 assert (size (test (cv, 'all')), [12, 2]);
 assert (sum (test (cv, 'all')), [5, 7]);
 assert (sum (training (cv, 'all')), [7, 5]);
 assert (test (cv, 1)', grpvar == 2);
 assert (test (cv, 2)', grpvar != 2);
***** test
 grpvar = [1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3];
 rand ('seed', 5);
 cv = cvpartition (12, 'kfold', 2, 'GroupingVariables', grpvar);
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 12);
 assert (cv.NumTestSets, 2);
 assert (cv.TrainSize, [7, 5]);
 assert (cv.TestSize, [5, 7]);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, true);
 assert (cv.IsStratified, false);
 assert (test (cv, 1), ! training (cv, 1));
 assert (test (cv, 'all'), ! training (cv, 'all'));
 assert (size (test (cv, 'all')), [12, 2]);
 assert (sum (test (cv, 'all')), [5, 7]);
 assert (sum (training (cv, 'all')), [7, 5]);
 assert (test (cv, 1)', grpvar == 3);
 assert (test (cv, 2)', grpvar != 3);
***** test
 status = warning;
 warning ('off');
 cv = cvpartition (5, 'kfold', 5, 'GroupingVariables', {'a';'a';'b';'b';''});
 warning (status);
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 5);
 assert (cv.NumTestSets, 2);
 assert (cv.TrainSize, [2, 2]);
 assert (cv.TestSize, [2, 2]);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, true);
 assert (cv.IsStratified, false);
 idx = ! ismissing ({'a';'a';'b';'b';''});
 assert (test (cv, 1)(idx), ! training (cv, 1)(idx));
 assert (test (cv, 'all')(idx,:), ! training (cv, 'all')(idx,:));
 assert (size (test (cv, 'all')), [5, 2]);
 assert (sum (test (cv, 'all')), [2, 2]);
 assert (sum (test (cv, 'all'), 2), [1; 1; 1; 1; 0]);
***** test
 rand ('seed', 5);
 cv = cvpartition ([1, 1, 1, 1, 1, 2, 2, 2, 2, 2], 'holdout', 3);
 assert (cv.Type, 'holdout');
 assert (cv.NumObservations, 10);
 assert (cv.NumTestSets, 1);
 assert (cv.TrainSize, 7);
 assert (cv.TestSize, 3);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, true);
 assert (test (cv, 1), ! training (cv, 1));
 assert (test (cv), logical ([0, 0, 0, 0, 1, 0, 1, 0, 0, 1])');
***** test
 cv = cvpartition ([1, 1, 1, 1, 1, 2, 2, 2, 2, 2], 'holdout', 4);
 assert (cv.Type, 'holdout');
 assert (cv.NumObservations, 10);
 assert (cv.NumTestSets, 1);
 assert (cv.TrainSize, 6);
 assert (cv.TestSize, 4);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, true);
 assert (test (cv, 1), ! training (cv, 1));
 assert (sum (test (cv)(1:5)), 2);
 assert (sum (test (cv)(6:10)), 2);
***** test
 grpvar = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2];
 rand ('seed', 5);
 cv = cvpartition (grpvar, 'holdout', 4, 'Stratify', false);
 assert (cv.Type, 'holdout');
 assert (cv.NumObservations, 10);
 assert (cv.NumTestSets, 1);
 assert (cv.TrainSize, 6);
 assert (cv.TestSize, 4);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, false);
 assert (test (cv, 1), ! training (cv, 1));
 assert (sum (test (cv)(1:5)), 3);
 assert (sum (test (cv)(6:10)), 1);
***** test
 cv = cvpartition ([1 1 1 1 1 2 2 2 2 1], 'kfold', 2);
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 10);
 assert (cv.NumTestSets, 2);
 assert (cv.TrainSize, [5, 5]);
 assert (cv.TestSize, [5, 5]);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, true);
 assert (test (cv, 1), ! training (cv, 1));
 assert (test (cv, 'all'), ! training (cv, 'all'));
 assert (sum (test (cv, 1)(1:5)), 3);
 assert (sum (test (cv, 2)(1:5)), 2);
 assert (sum (test (cv, 1)(6:10)), 2);
 assert (sum (test (cv, 2)(6:10)), 3);
***** test
 grpvar = [1 1 1 1 1 2 2 2 2 1];
 rand ('seed', 5);
 cv = cvpartition (grpvar, 'kfold', 2, 'Stratify', false);
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 10);
 assert (cv.NumTestSets, 2);
 assert (cv.TrainSize, [5, 5]);
 assert (cv.TestSize, [5, 5]);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, false);
 assert (test (cv, 1), ! training (cv, 1));
 assert (test (cv, 'all'), ! training (cv, 'all'));
 assert (sum (test (cv, 1)(1:5)), 4);
 assert (sum (test (cv, 2)(1:5)), 1);
 assert (sum (test (cv, 1)(6:10)), 1);
 assert (sum (test (cv, 2)(6:10)), 4);
***** test
 status = warning;
 warning ('off');
 cv = cvpartition ({'a','a','b','b',''}, 'kfold');
 warning (status);
 assert (cv.Type, 'kfold');
 assert (cv.NumObservations, 5);
 assert (cv.NumTestSets, 4);
 assert (cv.TrainSize, [3, 3, 3, 3]);
 assert (cv.TestSize, [1, 1, 1, 1]);
 assert (cv.IsCustom, false);
 assert (cv.IsGrouped, false);
 assert (cv.IsStratified, true);
 idx = ! ismissing ({'a','a','b','b',''});
 assert (test (cv, 1)(idx), ! training (cv, 1)(idx));
 assert (test (cv, 'all')(idx,:), ! training (cv, 'all')(idx,:));
 assert (sum (test (cv, 'all'), 2), [1; 1; 1; 1; 0]);
***** error <cvpartition: too few input arguments.> cvpartition (2)
***** error <cvpartition: too many input arguments.> cvpartition (1, 2, 3, 4, 5, 6)
***** error <cvpartition: TESTSETS must be numeric of logical.> ...
 cvpartition ("CustomPartition", 'a')
***** error <cvpartition: TESTSETS must be a numeric vector.> ...
 cvpartition ("CustomPartition", [2, 3; 2, 3])
***** error <cvpartition: TESTSETS must be a logical vector or matrix.> ...
 cvpartition ("CustomPartition", false (3, 3, 3))
***** error <cvpartition: each observation in TESTSETS must be exactly one in each row.> ...
 cvpartition ("CustomPartition", [false, true; true, true; true, false])
***** error <cvpartition: a logical matrix in TESTSETS must not have more columns that rows.> ...
 cvpartition ("CustomPartition", false (3, 5))
***** error <cvpartition: X must be a scalar positive integer value.> ...
 cvpartition (-20, "LeaveOut")
***** error <cvpartition: X must be a scalar positive integer value.> ...
 cvpartition (20.5, "LeaveOut")
***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
 cvpartition (20, "HoldOut", [0.2, 0.3])
***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
 cvpartition (20, "HoldOut", 'a')
***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, N\).> ...
 cvpartition (20, "HoldOut", 0)
***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, N\).> ...
 cvpartition (20, "HoldOut", -0.1)
***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, N\).> ...
 cvpartition (20, "HoldOut", 21)
***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
 cvpartition (20, "kfold", [2, 3])
***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
 cvpartition (20, "kfold", 'a')
***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, N\].> ...
 cvpartition (20, "kfold", 2.5)
***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, N\].> ...
 cvpartition (20, "kfold", 21)
***** error <cvpartition: invalid optional paired argument for 'GroupingVariables'.> ...
 cvpartition (10, "kfold", 3, "Group")
***** error <cvpartition: missing value for optional paired argument 'GroupingVariables'.> ...
 cvpartition (10, "kfold", 3, "GroupingVariables")
***** error <cvpartition: invalid value for optional paired argument 'GroupingVariables'.> ...
 cvpartition (10, "kfold", 3, "GroupingVariables", ones (3, 3, 3))
***** error <cvpartition: grouping variable does not match the number of observations.> ...
 cvpartition (10, "kfold", 3, "GroupingVariables", {'a', 'a', 'a', 'b', 'b'})
***** warning <cvpartition: number of folds K is greater than the groups in 'GroupingVariables'. K is set to the number of groups.> ...
 cvpartition (5, "kfold", 3, "GroupingVariables", {'a', 'a', 'a', 'b', 'b'});
***** error <cvpartition: invalid optional paired argument.> ...
 cvpartition (20, "some")
***** error <cvpartition: invalid optional paired argument for stratification.> ...
 cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "strat")
***** error <cvpartition: missing value for optional paired argument 'stratify'.> ...
 cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "stratify")
***** error <cvpartition: invalid value for optional paired argument 'stratify'.> ...
 cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "stratify", [true, true])
***** error <cvpartition: invalid value for optional paired argument 'stratify'.> ...
 cvpartition ([1, 1, 1, 2, 2], "kfold", 2, "stratify", 'no')
***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
 cvpartition ([1, 1, 1, 2, 2], "holdout", 'a')
***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
 cvpartition ([1, 1, 1, 2, 2], "holdout", 'a', "stratify", true)
***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
 cvpartition ([1, 1, 1, 2, 2], "holdout", [0.2, 0.3])
***** error <cvpartition: P value for 'holdout' must be a numeric scalar.> ...
 cvpartition ([1, 1, 1, 2, 2], "holdout", [0.2, 0.3], "stratify", true)
***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, N\), where N is the number of nonmissing observations in X.> ...
 cvpartition ([1, 1, 1, 2, 2], "holdout", 0)
***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, N\), where N is the number of nonmissing observations in X.> ...
 cvpartition ([1, 1, 1, 2, 2], "holdout", 0, "stratify", true)
***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, N\), where N is the number of nonmissing observations in X.> ...
 cvpartition ([1, 1, 1, 2, 2], "holdout", -0.1)
***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, N\), where N is the number of nonmissing observations in X.> ...
 cvpartition ([1, 1, 1, 2, 2], "holdout", -0.1, "stratify", true)
***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, N\), where N is the number of nonmissing observations in X.> ...
 cvpartition ([1, 1, 1, 2, 2], "holdout", 1.2)
***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, N\), where N is the number of nonmissing observations in X.> ...
 cvpartition ([1, 1, 1, 2, 2], "holdout", 1.2, "stratify", false)
***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, N\), where N is the number of nonmissing observations in X.> ...
 cvpartition ([1, 1, 1, 2, 2], "holdout", 6)
***** error <cvpartition: P value for 'holdout' must be a scalar in the range \(0,1\) or an integer scalar in the range \[1, N\), where N is the number of nonmissing observations in X.> ...
 cvpartition ([1, 1, 1, 2, 2], "holdout", 6, "stratify", false)
***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
 cvpartition ([1, 1, 1, 2, 2], "kfold", 'a')
***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
 cvpartition ([1, 1, 1, 2, 2], "kfold", 'a', "stratify", true)
***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
 cvpartition ([1, 1, 1, 2, 2], "kfold", [2, 3])
***** error <cvpartition: K value for 'kfold' must be a numeric scalar.> ...
 cvpartition ([1, 1, 1, 2, 2], "kfold", [2, 3], "stratify", false)
***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, N\], where N is the number of nonmissing observations in X.> ...
 cvpartition ([1, 1, 1, 2, 2], "kfold", 0)
***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, N\], where N is the number of nonmissing observations in X.> ...
 cvpartition ([1, 1, 1, 2, 2], "kfold", 0, "stratify", true)
***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, N\], where N is the number of nonmissing observations in X.> ...
 cvpartition ([1, 1, 1, 2, 2], "kfold", 1.5)
***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, N\], where N is the number of nonmissing observations in X.> ...
 cvpartition ([1, 1, 1, 2, 2], "kfold", 1.5, "stratify", true)
***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, N\], where N is the number of nonmissing observations in X.> ...
 cvpartition ([1, 1, 1, 2, 2], "kfold", 6)
***** error <cvpartition: K value for 'kfold' must be an integer scalar in the range \[1, N\], where N is the number of nonmissing observations in X.> ...
 cvpartition ([1, 1, 1, 2, 2], "kfold", 6, "stratify", true)
***** error <cvpartition: invalid optional paired argument.> ...
 cvpartition ([1, 1, 1, 2, 2], "leaveout")
***** error <cvpartition: invalid optional paired argument.> ...
 cvpartition ([1, 1, 1, 2, 2], "resubstitution")
***** error <cvpartition: invalid optional paired argument.> ...
 cvpartition ([1, 1, 1, 2, 2], "some")
***** error <cvpartition: invalid first input argument.> ...
 cvpartition ({1, 1; 2, 2}, "kfold")
***** error <cvpartition.repartition: cannot repartition a custom partition.> ...
 repartition (cvpartition ('CustomPartition', [1,1,2,2,3,3]))
***** error <cvpartition.repartition: 'legacy' flag does not apply to stratified or grouped 'kfold' partitioned objects.> ...
 repartition (cvpartition ([1 1 1 1 1 2 2 2 2 1], 'kfold', 2, 'Stratify', true), 'legacy')
***** error <cvpartition.repartition: 'legacy' flag is only valid for 'kfold' partitioned objects.> ...
 repartition (cvpartition (20, 'Leaveout', 0.2), 'legacy')
***** error <cvpartition.repartition: SVAL must be a real scalar or vector.> ...
 repartition (cvpartition (20, 'Leaveout', 0.2), 'asd')
***** error <cvpartition.repartition: SVAL must be a real scalar or vector.> ...
 repartition (cvpartition (20, 'Leaveout', 0.2), 2+i)
***** error <cvpartition.repartition: SVAL must be a real scalar or vector.> ...
 repartition (cvpartition (20, 'KFold', 5), [34, 56; 2, 3])
***** error <cvpartition.test: too many input arguments.> ...
 test (cvpartition (20, "kfold"), 2, 3)
***** error <cvpartition.test: set index must be a positive integer vector.> ...
 test (cvpartition (20, "kfold"), 0)
***** error <cvpartition.test: set index must be a positive integer vector.> ...
 test (cvpartition (20, "kfold"), 1.5)
***** error <cvpartition.test: set index must be a positive integer vector.> ...
 test (cvpartition (20, "kfold"), [1, 1.5])
***** error <cvpartition.test: set index must be a positive integer vector.> ...
 test (cvpartition (20, "kfold"), [2, 3; 2, 3])
***** error <cvpartition.test: set index exceeds 'NumTestSets'.> ...
 test (cvpartition (20, "kfold"), 21)
***** error <cvpartition.test: set index exceeds 'NumTestSets'.> ...
 test (cvpartition (20, "kfold"), [18, 21])
***** error <cvpartition.training: too many input arguments.> ...
 training (cvpartition (20, "kfold"), 2, 3)
***** error <cvpartition.training: set index must be a positive integer vector.> ...
 training (cvpartition (20, "kfold"), 0)
***** error <cvpartition.training: set index must be a positive integer vector.> ...
 training (cvpartition (20, "kfold"), 1.5)
***** error <cvpartition.training: set index must be a positive integer vector.> ...
 training (cvpartition (20, "kfold"), [1, 1.5])
***** error <cvpartition.training: set index must be a positive integer vector.> ...
 training (cvpartition (20, "kfold"), [2, 3; 2, 3])
***** error <cvpartition.training: set index exceeds 'NumTestSets'.> ...
 training (cvpartition (20, "kfold"), 21)
***** error <cvpartition.training: set index exceeds 'NumTestSets'.> ...
 training (cvpartition (20, "kfold"), [18, 21])
***** test
 ## 1. Stratified K-Fold: Basic Text Labels
 species = [repmat({"Setosa"}, 10, 1); repmat({"Versicolor"}, 10, 1)];
 rand ("state", 42);
 c = cvpartition (species, "KFold", 2);
 T = summary (c);
 assert (height (T), 10);
 assert (all (ismember ({"Set", "SetSize", "StratificationLabel", ...
                        "StratificationCount", "PercentInSet"}, ...
                        T.Properties.VariableNames)));

 ## Check Output Type (String Array) and Counts
 if (exist ("string", "class"))
   assert (isa (T.Set, "string"));
   assert (isa (T.StratificationLabel, "string"));
   mask = (T.Set == "all") & (T.StratificationLabel == "Setosa");
 else
   ## Fallback for older environments
   mask = strcmp (T.Set, "all") & strcmp (T.StratificationLabel, "Setosa");
 endif
 assert (T.StratificationCount(mask), 10);
***** test
 ## 2. Grouped K-Fold: Basic Numeric Labels
 groups = [1; 1; 1; 2; 2; 3; 3; 3; 3; 3];
 rand ("state", 100);
 c = cvpartition (numel (groups), "KFold", 2, "GroupingVariables", groups);
 T = summary (c);
 assert (any (strcmp ("GroupLabel", T.Properties.VariableNames)));

 ## Verify Group Integrity
 if (iscell (T.GroupLabel))
   vals = cell2mat (T.GroupLabel);
 else
   vals = T.GroupLabel;
 endif
 mask_g3 = (vals == 3);

 if (exist ("string", "class"))
   mask_t1 = (T.Set == "test1");
 else
   mask_t1 = strcmp (T.Set, "test1");
 endif

 count_g3 = T.GroupCount(mask_g3 & mask_t1);
 assert (count_g3 == 5 || count_g3 == 0);
***** test
 ## 3. Grouped K-Fold: Matrix Grouping
 g1 = [1; 1; 1; 2; 2; 2];
 g2 = [1; 1; 2; 1; 2; 2];
 groups = [g1, g2];
 c = cvpartition (6, "KFold", 2, "GroupingVariables", groups);
 T = summary (c);
 ## 4 unique groups * 5 sets (all + 2 train + 2 test)
 assert (height (T), 20);
***** test
 ## 4. Stratified Holdout: Basic
 species = [repmat({"A"}, 10, 1); repmat({"B"}, 10, 1)];
 c = cvpartition (species, "Holdout", 0.5);
 T = summary (c);
 sets = unique (T.Set);
 assert (numel (sets), 3); ## all, train1, test1
***** test
 ## 5. Mathematical Consistency: Percentages
 classes = [1; 1; 2; 2; 3; 3];
 c = cvpartition (classes, "KFold", 2);
 T = summary (c);

 if (exist ("string", "class"))
   mask_all = (T.Set == "all");
   mask_tr1 = (T.Set == "train1");
 else
   mask_all = strcmp (T.Set, "all");
   mask_tr1 = strcmp (T.Set, "train1");
 endif

 assert (sum (T.PercentInSet(mask_all)), 100, 1e-10);
 assert (sum (T.PercentInSet(mask_tr1)), 100, 1e-10);
***** test
 ## 6. Mathematical Consistency: Set Sizes
 N = 20;
 c = cvpartition (ones (N, 1), "KFold", 4);
 T = summary (c);

 if (exist ("string", "class"))
   mask_tr1 = (T.Set == "train1");
   mask_ts1 = (T.Set == "test1");
 else
   mask_tr1 = strcmp (T.Set, "train1");
   mask_ts1 = strcmp (T.Set, "test1");
 endif

 size_tr1 = T.SetSize(find (mask_tr1, 1));
 size_ts1 = T.SetSize(find (mask_ts1, 1));
 assert (size_tr1 + size_ts1, N);
***** test
 ## 7. Logical Grouping Variables
 groups = [true; true; true; false; false];
 c = cvpartition (5, "KFold", 2, "GroupingVariables", groups);
 T = summary (c);
 assert (height (T), 2 * 5);
 if (iscell (T.GroupLabel))
   u_labels = unique (cell2mat (T.GroupLabel));
 else
   u_labels = unique (T.GroupLabel);
 endif
 assert (numel (u_labels), 2);
***** test
 ## 8. Char Array Grouping Variables
 groups = ['A'; 'A'; 'B'; 'B'; 'C'];
 c = cvpartition (5, "KFold", 2, "GroupingVariables", groups);
 T = summary (c);
 assert (height (T), 3 * 5);
 assert (any (strcmp ("GroupLabel", T.Properties.VariableNames)));
***** test
 ## 9. Floating Point Grouping Variables
 groups = [1.1; 1.1; 2.2; 2.2];
 c = cvpartition (4, "KFold", 2, "GroupingVariables", groups);
 T = summary (c);
 if (iscell (T.GroupLabel))
   vals = cell2mat (T.GroupLabel);
 else
   vals = T.GroupLabel;
 endif
 assert (any (abs (vals - 1.1) < 1e-10));
 assert (any (abs (vals - 2.2) < 1e-10));
***** test
 ## 10. Negative Numeric Grouping
 groups = [-5; -5; -10; -10];
 c = cvpartition (4, "KFold", 2, "GroupingVariables", groups);
 T = summary (c);
 assert (height (T), 2 * 5);
***** test
 ## 11. Missing Values in Stratification (NaN)
 classes = [1; 1; 2; 2; NaN; NaN];
 c = cvpartition (classes, "KFold", 2);
 T = summary (c);
 if (exist ("string", "class"))
   mask_all = (T.Set == "all");
 else
   mask_all = strcmp (T.Set, "all");
 endif
 total_obs = T.SetSize(find (mask_all, 1));
 assert (total_obs, 4);
***** test
 ## 12. Missing Values in Grouping (NaN)
 groups = [1; 1; 2; 2; NaN];
 c = cvpartition (5, "KFold", 2, "GroupingVariables", groups);
 T = summary (c);
 if (exist ("string", "class"))
   mask_all = (T.Set == "all");
 else
   mask_all = strcmp (T.Set, "all");
 endif
 assert (T.SetSize(find (mask_all, 1)), 4);
***** test
 ## 13. Unbalanced Stratification
 species = [repmat({"C1"}, 90, 1); repmat({"C2"}, 10, 1)];
 c = cvpartition (species, "KFold", 2);
 T = summary (c);
 if (exist ("string", "class"))
   mask_ts1 = (T.Set == "test1");
   subT = T(mask_ts1, :);
   c1_count = subT.StratificationCount(subT.StratificationLabel == "C1");
   c2_count = subT.StratificationCount(subT.StratificationLabel == "C2");
 else
   mask_ts1 = strcmp (T.Set, "test1");
   subT = T(mask_ts1, :);
   c1_count = subT.StratificationCount(strcmp (subT.StratificationLabel, "C1"));
   c2_count = subT.StratificationCount(strcmp (subT.StratificationLabel, "C2"));
 endif
 assert (c1_count == 45);
 assert (c2_count == 5);
***** test
 ## 14. Single Observation per Group (Edge Case)
 groups = [1; 2; 3; 4];
 c = cvpartition (4, "KFold", 2, "GroupingVariables", groups);
 T = summary (c);
 if (exist ("string", "class"))
   mask_ts1 = (T.Set == "test1");
 else
   mask_ts1 = strcmp (T.Set, "test1");
 endif
 counts = T.GroupCount(mask_ts1);
 assert (sum (counts == 1), 2);
 assert (sum (counts == 0), 2);
***** test
 ## 15. Set Name Generation Verification
 species = [1; 1; 2; 2];
 c = cvpartition (species, "KFold", 2);
 T = summary (c);
 set_names = unique (T.Set);
 expected = {"all"; "train1"; "test1"; "train2"; "test2"};
 if (exist ("string", "class"))
   ## Convert string array to cell for sort comparison
   assert (sort (cellstr (set_names)), sort (expected));
 else
   assert (sort (set_names), sort (expected));
 endif
***** test
 ## 16. Label Column Consistency
 groups = ['A'; 'B'];
 c = cvpartition (2, "KFold", 2, "GroupingVariables", groups);
 T = summary (c);
 if (exist ("string", "class"))
   assert (isa (T.GroupLabel, "string"));
 else
   assert (iscellstr (T.GroupLabel));
 endif
***** test
 ## 17. Valid "Blank" Labels (Space) - FIX APPLIED
 species = {"A"; "A"; " "; " "};
 c = cvpartition (species, "KFold", 2);
 T = summary (c);
 if (exist ("string", "class"))
   labels = cellstr (T.StratificationLabel);
   sets = cellstr (T.Set);
   assert (any (strcmp (labels, " ")));
   mask_space = strcmp (labels, " ");
   mask_all = strcmp (sets, "all");
 else
   assert (any (strcmp (T.StratificationLabel, " ")));
   mask_space = strcmp (T.StratificationLabel, " ");
   mask_all = strcmp (T.Set, "all");
 endif
 assert (sum (T.StratificationCount(mask_space & mask_all)), 2);
***** test
 ## 18. Large K (Leave-One-Out Simulation) - FIX APPLIED
 species = [1; 1; 2; 2];
 warn_state = warning ("off", "all");
 c = cvpartition (species, "KFold", 4);
 warning (warn_state);
 T = summary (c);
 assert (height (T), 18);
 if (exist ("string", "class"))
   mask_test = startsWith (cellstr(T.Set), "test");
 else
   mask_test = strncmp (T.Set, "test", 4);
 endif
 assert (all (T.SetSize(mask_test) == 1));
***** test
 ## 19. Repeated Holdout Integrity
 species = [1; 1; 2; 2];
 rand ("state", 42);
 c = cvpartition (species, "Holdout", 0.5);
 T = summary (c);
 if (exist ("string", "class"))
   mask_ts1 = (T.Set == "test1");
 else
   mask_ts1 = strcmp (T.Set, "test1");
 endif
 size_ts1 = T.SetSize(find (mask_ts1, 1));
 assert (size_ts1, 2);
***** test
 ## 20. Empty String Handling (Missing Data)
 species = {"A"; "A"; ""; ""};
 c = cvpartition (species, "KFold", 2);
 T = summary (c);
 if (exist ("string", "class"))
   assert (! any (T.StratificationLabel == ""));
   mask_all = (T.Set == "all");
 else
   assert (! any (strcmp (T.StratificationLabel, "")));
   mask_all = strcmp (T.Set, "all");
 endif
 total_rows = T.SetSize(find (mask_all, 1));
 assert (total_rows, 2);
***** test
 ## 21. Basic Unstacking (Stratified K-Fold)
 species = [repmat({"Alpha"}, 10, 1); repmat({"Beta"}, 10, 1)];
 c = cvpartition (species, "KFold", 2);
 T = summary (c);
 T_wide = unstack (T(:, 1:4), "StratificationCount", "StratificationLabel");
 ## Check dimensions: 3 sets (all, train1, test1, etc) x (Set+SetSize + 2 Labels)
 assert (height (T_wide), 5);
 assert (width (T_wide), 4);
 assert (all (ismember ({"Alpha", "Beta"}, T_wide.Properties.VariableNames)));
***** test
 ## 22. Data Integrity Check (Row Sums)
 species = [repmat({"Control"}, 20, 1); repmat({"Treatment"}, 80, 1)];
 c = cvpartition (species, "Holdout", 0.25);
 T = summary (c);
 T_wide = unstack (T(:, 1:4), "StratificationCount", "StratificationLabel");
 row_sums = T_wide.Control + T_wide.Treatment;
 assert (all (row_sums == T_wide.SetSize));
***** test
 ## 23. Unstacking Grouped Data (Numeric Labels)
 groups = [1; 1; 2; 2; 2];
 c = cvpartition (5, "KFold", 2, "GroupingVariables", groups);
 T = summary (c);
 T_wide = unstack (T(:, 1:4), "GroupCount", "GroupLabel");
 ## Check if numeric columns were created successfully
 col_names = T_wide.Properties.VariableNames;
 assert (any (cellfun (@(x) ~isempty (strfind (x, "1")), col_names)));
 assert (any (cellfun (@(x) ~isempty (strfind (x, "2")), col_names)));
***** test
 ## 24. Unstacking with Missing/NaN Groups
 groups = [1; 1; 2; 2; NaN];
 c = cvpartition (5, "KFold", 2, "GroupingVariables", groups);
 T = summary (c);
 T_wide = unstack (T(:, 1:4), "GroupCount", "GroupLabel");
 ## Should only have columns for 1 and 2, not NaN or 'undefined'
 assert (width (T_wide), 4); ## Set, SetSize, x1, x2
***** test
 ## 25. Unstacking String Array Inputs
 species = {"Red"; "Blue"; "Red"; "Blue"};
 c = cvpartition (species, "KFold", 2);
 T = summary (c);
 ## Verify input is actually string before unstacking checks
 if (exist ("string", "class"))
   assert (isa (T.Set, "string"));
 endif
 T_wide = unstack (T(:, 1:4), "StratificationCount", "StratificationLabel");
 ## Check the 'all' row count for Red
 assert (T_wide.Red(strcmp(cellstr(T_wide.Set), "all")) == 2);
***** test
 ## 26. Large K Unstacking (Many Rows)
 species = [repmat({"High"}, 10, 1); repmat({"Low"}, 10, 1)];
 c = cvpartition (species, "KFold", 10);
 T = summary (c);
 T_wide = unstack (T(:, 1:4), "StratificationCount", "StratificationLabel");
 ## 10 folds * 2 (train/test) + 1 (all) = 21 rows
 assert (height (T_wide), 21);
***** test
 ## 27. Unstacking with Special Characters in Labels
 species = {"Type A"; "Type A"; "Type-B"; "Type-B"};
 c = cvpartition (species, "KFold", 2);
 T = summary (c);
 T_wide = unstack (T(:, 1:4), "StratificationCount", "StratificationLabel");
 vnames = T_wide.Properties.VariableNames;
 ## Check if spaces/dashes were handled/preserved in some valid form
 assert (numel (vnames), 4);
***** test
 ## 28. Verification of 'all' row logic after Unstacking
 species = [repmat({"Yes"}, 50, 1); repmat({"No"}, 50, 1)];
 c = cvpartition (species, "Holdout", 0.2);
 T = summary (c);
 T_wide = unstack (T(:, 1:4), "StratificationCount", "StratificationLabel");
 mask = strcmp (cellstr (T_wide.Set), "all");
 assert (T_wide.Yes(mask) == 50);
 assert (T_wide.No(mask) == 50);
***** test
 ## 29. Robustness against re-ordering
 species = {"Left"; "Left"; "Right"; "Right"};
 c = cvpartition (species, "Holdout", 0.5);
 T = summary (c);
 T_shuffled = T([3, 1, 2], :);
 T_wide = unstack (T_shuffled(:, 1:4), "StratificationCount", "StratificationLabel");
 mask = strcmp (cellstr (T_wide.Set), "all");
 assert (T_wide.Left(mask) == 2);
***** error <cvpartition.summary: partition must be stratified or grouped.>
 c = cvpartition (20, "KFold", 5);
 summary (c);
***** error <cvpartition.summary: partition must be stratified or grouped.>
 c = cvpartition (10, "LeaveOut");
 summary (c);
133 tests, 133 passed, 0 known failure, 0 skipped
[inst/Clustering/GapEvaluation.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Clustering/GapEvaluation.m
***** test
 load fisheriris
 eva = evalclusters (meas([1:50],:), "kmeans", "gap", "KList", [1:3], ...
                     "referencedistribution", "uniform");
 assert (class (eva), "GapEvaluation");
1 test, 1 passed, 0 known failure, 0 skipped
[inst/Clustering/KDTreeSearcher.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Clustering/KDTreeSearcher.m
***** demo
 ## Demo to verify implementation using fisheriris dataset
 load fisheriris
 numSamples = size (meas, 1);
 queryIndices = [1, 23, 46, 63, 109];
 dataIndices = ~ismember (1:numSamples, queryIndices);
 queryPoints = meas(queryIndices, :);
 dataPoints = meas(dataIndices, :);
 searchRadius = 0.3;
 kdTree = KDTreeSearcher (dataPoints, 'Distance', 'minkowski')
 nearestNeighbors = knnsearch (kdTree, queryPoints, "K", 2)
 neighborsInRange = rangesearch (kdTree, queryPoints, searchRadius)
***** demo
 ## Create a KDTreeSearcher with Euclidean distance
 X = [1, 2; 3, 4; 5, 6];
 obj = KDTreeSearcher (X);
 ## Find the nearest neighbor to [2, 3]
 Y = [2, 3];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 disp ("Nearest neighbor index:");
 disp (idx);
 disp ("Distance:");
 disp (D);
 ## Find all points within radius 2
 [idx, D] = rangesearch (obj, Y, 2);
 disp ("Indices within radius:");
 disp (idx);
 disp ("Distances:");
 disp (D);
***** demo
 ## Create a KDTreeSearcher with Minkowski distance (P=3)
 X = [0, 0; 1, 0; 2, 0];
 obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
 ## Find the nearest neighbor to [1, 0]
 Y = [1, 0];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 disp ("Nearest neighbor index:");
 disp (idx);
 disp ("Distance:");
 disp (D);
***** demo
 rng(42);
 disp('Demonstrating KDTreeSearcher');

 n = 100;
 mu1 = [0.3, 0.3];
 mu2 = [0.7, 0.7];
 sigma = 0.1;
 X1 = mu1 + sigma * randn (n / 2, 2);
 X2 = mu2 + sigma * randn (n / 2, 2);
 X = [X1; X2];

 obj = KDTreeSearcher(X);

 Y = [0.3, 0.3; 0.7, 0.7; 0.5, 0.5];

 K = 5;
 [idx, D] = knnsearch (obj, Y, "K", K);

 disp ('For the first query point:');
 disp (['Query point: ', num2str(Y(1,:))]);
 disp ('Indices of nearest neighbors:');
 disp (idx(1,:));
 disp ('Distances:');
 disp (D(1,:));

 figure;
 scatter (X(:,1), X(:,2), 36, 'b', 'filled'); # Training points
 hold on;
 scatter (Y(:,1), Y(:,2), 36, 'r', 'filled'); # Query points
 for i = 1:size (Y, 1)
     query = Y(i,:);
     neighbors = X(idx(i,:), :);
     for j = 1:K
         plot ([query(1), neighbors(j,1)], [query(2), neighbors(j,2)], 'k-');
     endfor
 endfor
 hold off;
 title ('K Nearest Neighbors with KDTreeSearcher');
 xlabel ('X1');
 ylabel ('X2');

 r = 0.15;
 [idx, D] = rangesearch (obj, Y, r);

 disp ('For the first query point in rangesearch:');
 disp (['Query point: ', num2str(Y(1,:))]);
 disp ('Indices of points within radius:');
 disp (idx{1});
 disp ('Distances:');
 disp (D{1});

 figure;
 scatter (X(:,1), X(:,2), 36, 'b', 'filled');
 hold on;
 scatter (Y(:,1), Y(:,2), 36, 'r', 'filled');
 theta = linspace (0, 2 * pi, 100);
 for i = 1:size (Y, 1)
     center = Y(i,:);
     x_circle = center(1) + r * cos (theta);
     y_circle = center(2) + r * sin (theta);
     plot (x_circle, y_circle, 'g-');
     ## Highlight points within radius
     if (! isempty (idx{i}))
       in_radius = X(idx{i}, :);
       scatter (in_radius(:,1), in_radius(:,2), 36, 'g', 'filled');
     endif
 endfor
 hold off
 title ('Points within Radius with KDTreeSearcher');
 xlabel ('X1');
 ylabel ('X2');
***** test
 load fisheriris
 X = meas;
 obj = KDTreeSearcher (X);
 Y = X(1:5,:);
 [idx, D] = knnsearch (obj, Y, "K", 3);
 assert (idx, [[1, 18, 5]; [2, 35, 46]; [3, 48, 4]; [4, 48, 30]; [5, 38, 1]])
 assert (D, [[0, 0.1000, 0.1414]; [0,  0.1414,  0.1414]; [0, 0.1414, 0.2449];
             [0, 0.1414, 0.1732]; [0, 0.1414, 0.1414]], 5e-5)
***** test
 load fisheriris
 X = meas;
 obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
 Y = X(10:15,:);
 [idx, D] = knnsearch (obj, Y, "K", 2);
 assert (idx, [[10, 35]; [11, 49]; [12, 30]; [13, 2]; [14, 39]; [15, 34]])
 assert (D, [[0, 0.1000]; [0, 0.1000]; [0, 0.2080]; [0, 0.1260]; [0, 0.2154];
             [0, 0.3503]], 5e-5)
***** test
 load fisheriris
 X = meas;
 obj = KDTreeSearcher (X, "Distance", "cityblock");
 Y = X(20:25,:);
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (idx, [20; 21; 22; 23; 24; 25])
 assert (D, [0; 0; 0; 0; 0; 0])
***** test
 load fisheriris
 X = meas;
 obj = KDTreeSearcher (X, "Distance", "chebychev");
 Y = X(30:35,:);
 [idx, D] = knnsearch (obj, Y, "K", 4);
 assert (idx, [[30, 31, 4, 12]; [31, 30, 10, 35]; [32, 21, 37, 28];
               [33, 20, 34, 47]; [34, 16, 15, 33]; [35, 10, 2, 26]])
 assert (D, [[0, 0.1000, 0.1000, 0.2000]; [0, 0.1000, 0.1000, 0.1000];
             [0, 0.2000, 0.2000, 0.2000]; [0, 0.3000, 0.3000, 0.3000];
             [0, 0.2000, 0.3000, 0.3000]; [0, 0.1000, 0.1000, 0.1000]], 5e-15)
***** test
 load fisheriris
 X = meas;
 obj = KDTreeSearcher (X, "BucketSize", 20);
 Y = X(40:45,:);
 [idx, D] = knnsearch (obj, Y, "K", 2);
 assert (idx, [[40, 8]; [41, 18]; [42, 9]; [43, 39]; [44, 27]; [45, 47]])
 assert (D, [[0, 0.1000]; [0, 0.1414]; [0, 0.6245]; [0, 0.2000]; [0, 0.2236];
             [0, 0.3606]], 4.7e-5)
***** test
 load fisheriris
 X = meas;
 obj = KDTreeSearcher (X);
 Y = X(50:55,:);
 [idx, D] = knnsearch (obj, Y, "K", 3, "IncludeTies", true);
 assert (idx, {[50; 8; 40]; [51; 53; 87]; [52; 57; 76]; [53; 51; 87]; [54; ...
                90; 81]; [55; 59; 76]})
 assert (D, {[0; 0.1414; 0.1732]; [0; 0.2646; 0.3317]; [0; 0.2646; 0.3162];
             [0; 0.2646; 0.2828]; [0; 0.2000; 0.3000]; [0; 0.2449; 0.3162]}, 5e-5)
***** test
 load fisheriris
 X = meas;
 obj = KDTreeSearcher (X);
 Y = X(60:65,:);
 [idx, D] = rangesearch (obj, Y, 0.4);
 assert (idx, {[60; 90]; [61; 94]; [62; 97; 79; 96; 100; 89; 98; 72]; [63];
               [64; 92; 74; 79]; [65]})
 assert (D, {[0; 0.3873]; [0; 0.3606];
             [0; 0.3000; 0.3317; 0.3606; 0.3606; 0.3742; 0.3873; 0.4000]; [0];
             [0; 0.1414; 0.2236; 0.2449]; [0]}, 5e-5)
***** test
 load fisheriris
 X = meas;
 obj = KDTreeSearcher (X, "Distance", "cityblock");
 Y = X(70:72,:);
 [idx, D] = rangesearch (obj, Y, 1.0);
 assert (idx, {[70; 81; 90; 82; 83; 93; 54; 68; 95; 80; 91; 100; 60; 65; ...
                89; 63]; [71; 139; 128; 150; 127; 57; 86; 64; 79; 92; 124];
               [72; 100; 98; 83; 93; 97; 75; 68; 62; 89; 95; 74; 56; 90; ...
                79; 92; 96; 64; 63; 65]})
 assert (D, {[0; 0.3000; 0.4000; 0.5000; 0.5000; 0.5000; 0.6000; 0.7000; ...
              0.7000; 0.7000; 0.8000; 0.8000; 0.9000; 0.9000; 0.9000; 0.9000];
             [0; 0.3000; 0.5000; 0.5000; 0.7000; 0.8000; 0.8000; 1.0000; ...
              1.0000; 1.0000; 1]; [0; 0.5000; 0.5000; 0.6000; 0.6000; ...
              0.7000; 0.7000; 0.8000; 0.8000; 0.8000; 0.8000; 0.8000; ...
              0.9000; 0.9000; 0.9000; 0.9000; 0.9000; 0.9000; 1.0000; 1]}, 5e-5)
***** test
 load fisheriris
 X = meas;
 obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
 Y = X(80:85,:);
 [idx, D] = rangesearch (obj, Y, 0.8);
 assert (idx, {[80; 82; 81; 65; 70; 83; 93; 90; 54; 63; 68; 72; 100; 60; ...
                89; 99; 95; 94; 97; 96]; [81; 82; 70; 54; 90; 93; 80; 83; ...
                60; 68; 95; 100; 65; 63; 97; 61; 91; 94; 89; 96; 72; 58; ...
                62; 56]; [82; 81; 70; 80; 54; 90; 93; 83; 68; 60; 65; 63; ...
                100; 95; 94; 61; 58; 97; 89; 72; 96; 91; 99]; [83; 93; ...
                100; 68; 70; 72; 95; 90; 97; 65; 89; 96; 81; 82; 80; 62; ...
                54; 98; 63; 91; 56; 60; 79; 67; 75; 88; 85; 92; 69]; [84; ...
                134; 102; 143; 150; 124; 128; 73; 127; 139; 147; 64; 112; ...
                114; 120; 74; 135; 122; 71; 92; 104; 138; 148; 117; 79; ...
                55; 56; 57; 67; 111; 129; 69; 78; 59; 52; 133; 85; 88; ...
                87]; [85; 67; 56; 97; 95; 89; 96; 91; 100; 62; 71; 122; ...
                79; 60; 107; 90; 139; 93; 68; 86; 83; 92; 64; 150; 102; ...
                143; 74; 114; 70; 128; 84; 54; 72]})
 assert (D, {[0; 0.2884; 0.3530; 0.3826; 0.4062; 0.4198; 0.5117; 0.5440; 0.5718;
              0.6000; 0.6018; 0.6073; 0.6308; 0.6333; 0.6753; 0.7000; 0.7192;
              0.7230; 0.7350; 0.7459];
             [0; 0.1260; 0.1442; 0.2571; 0.2571; 0.3530; 0.3530; 0.3826; 0.4344;
              0.4344; 0.4642; 0.4747; 0.5217; 0.5217; 0.5896; 0.6009; 0.6082;
              0.6316; 0.6316; 0.6611; 0.6664; 0.6993; 0.7417; 0.7507];
             [0; 0.1260; 0.2224; 0.2884; 0.3803; 0.3803; 0.4121; 0.4121; 0.4905;
              0.5013; 0.5360; 0.5429; 0.5463; 0.5646; 0.5749; 0.5819; 0.6542;
              0.6581; 0.6753; 0.6938; 0.7094; 0.7107; 0.7423];
             [0; 0.1260; 0.2224; 0.2520; 0.2571; 0.3107; 0.3302; 0.3332; 0.3332;
              0.3530; 0.3530; 0.3803; 0.3826; 0.4121; 0.4198; 0.4344; 0.4531;
              0.5155; 0.5217; 0.5348; 0.6028; 0.6073; 0.6374; 0.6527; 0.6611;
              0.6804; 0.6938; 0.7399; 0.7560];
             [0; 0.3072; 0.3271; 0.3271; 0.3302; 0.3503; 0.3530; 0.3530; 0.3530;
              0.3958; 0.3979; 0.4327; 0.4626; 0.4642; 0.5027; 0.5066; 0.5130;
              0.5155; 0.5440; 0.5440; 0.5518; 0.5848; 0.6009; 0.6073; 0.6082;
              0.6316; 0.6471; 0.6746; 0.6753; 0.6797; 0.6804; 0.7047; 0.7192;
              0.7218; 0.7405; 0.7405; 0.7719; 0.7725; 0.7786];
             [0; 0.2000; 0.3503; 0.3979; 0.4121; 0.4309; 0.4327; 0.4531; 0.4747;
              0.5337; 0.5718; 0.5896; 0.6009; 0.6316; 0.6366; 0.6374; 0.6463;
              0.6542; 0.6542; 0.6550; 0.6938; 0.7014; 0.7067; 0.7166; 0.7186;
              0.7186; 0.7281; 0.7380; 0.7447; 0.7571; 0.7719; 0.7813; 0.7851]}, 5e-5)
***** test
 load fisheriris
 X = meas;
 obj = KDTreeSearcher (X, "Distance", "chebychev");
 Y = X(90,:);
 [idx, D] = rangesearch (obj, Y, 0.7);
 assert (idx, {[90; 70; 54; 81; 95; 60; 83; 93; 100; 68; 82; 65; 97; 91; ...
                56; 61; 62; 63; 67; 79; 80; 85; 89; 96; 72; 92; 107]})
 assert (D, {[0; 0.2000; 0.2000; 0.2000; 0.2000; 0.3000; 0.3000; 0.3000; ...
             0.3000; 0.3000; 0.3000; 0.4000; 0.4000; 0.4000; 0.5000; ...
             0.5000; 0.5000; 0.5000; 0.5000; 0.5000; 0.5000; 0.5000; ...
             0.5000; 0.5000; 0.6000; 0.6000; 0.6000]}, 5e-16)
***** test
 ## Constructor with single-point dataset
 X = [0, 0];
 obj = KDTreeSearcher (X);
 assert (obj.X, X);
 assert (obj.Distance, "euclidean");
 assert (isempty (obj.DistParameter));
 assert (obj.BucketSize, 50);
***** test
 ## Constructor with duplicate points
 X = [0, 0; 0, 0; 1, 0];
 obj = KDTreeSearcher (X, "Distance", "cityblock");
 assert (obj.X, X);
 assert (obj.Distance, "cityblock");
***** test
 ## Constructor with 3D data
 X = [0, 0, 0; 1, 0, 0; 0, 1, 0];
 obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
 assert (obj.X, X);
 assert (obj.DistParameter, 3);
***** test
 ## knnsearch with grid, K = 1
 X = [0, 0; 0, 1; 1, 0; 1, 1];
 obj = KDTreeSearcher (X, "Distance", "euclidean");
 Y = [0.5, 0.5];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 D_true = pdist2 (X, Y, "euclidean");
 assert (D, min (D_true), 1e-10);
 assert (any (idx == find (D_true == min (D_true))));
***** test
 ## knnsearch with IncludeTies, all points equidistant
 X = [0, 0; 0, 1; 1, 0; 1, 1];
 obj = KDTreeSearcher (X);
 Y = [0.5, 0.5];
 [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
 D_true = pdist2 (X, Y, "euclidean");
 expected_idx = find (D_true == min (D_true));
 assert (sort (idx{1}(:)), sort (expected_idx));
 assert (D{1}(:)', repmat (min (D_true), 1, 4), 1e-10);
***** test
 ## rangesearch with line dataset
 X = [0, 0; 1, 0; 2, 0; 3, 0];
 obj = KDTreeSearcher (X);
 Y = [1.5, 0];
 r = 1;
 [idx, D] = rangesearch (obj, Y, r);
 D_true = pdist2 (X, Y, "euclidean");
 expected_idx = find (D_true <= r);
 assert (sort (idx{1}(:)), sort (expected_idx));
 assert (D{1}, sort (D_true(expected_idx)), 1e-10);
***** test
 ## knnsearch with duplicates
 X = [0, 0; 0, 0; 1, 0];
 obj = KDTreeSearcher (X, "Distance", "cityblock");
 Y = [0, 0];
 [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
 assert (sort (idx{1}(:))', [1, 2]);
 assert (D{1}', [0, 0], 1e-10);
***** test
 ## rangesearch with 3D data
 X = [0, 0, 0; 1, 0, 0; 0, 1, 0];
 obj = KDTreeSearcher (X, "Distance", "cityblock");
 Y = [0, 0, 0];
 r = 1;
 [idx, D] = rangesearch (obj, Y, r);
 assert (sort (idx{1}(:))', [1, 2, 3]);
 assert (D{1}', [0, 1, 1], 1e-10);
***** test
 ## knnsearch with P = 2 (Euclidean equivalent)
 X = [0, 0; 1, 1];
 obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 2);
 Y = [0, 1];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (idx, 1);
 assert (D, 1, 1e-10);
***** test
 ## rangesearch with P = 3
 X = [0, 0; 1, 0; 0, 1];
 obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
 Y = [0.5, 0.5];
 r = 0.8;
 [idx, D] = rangesearch (obj, Y, r);
 D_true = pdist2 (X, Y, "minkowski", 3);
 expected_idx = find (D_true <= r);
 assert (sort (idx{1}(:)), sort (expected_idx));
 assert (D{1}, sort (D_true(expected_idx)), 1e-10);
***** test
 ## knnsearch with P = 4, random data
 X = rand (5, 2);
 obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 4);
 Y = rand (1, 2);
 [idx, D] = knnsearch (obj, Y, "K", 3);
 D_true = pdist2 (X, Y, "minkowski", 4);
 [sorted_D, sort_idx] = sort (D_true);
 assert (idx', sort_idx(1:3));
 assert (D', sorted_D(1:3), 1e-10);
***** test
 ## knnsearch with all same points
 X = [1, 1; 1, 1; 1, 1];
 obj = KDTreeSearcher (X, "Distance", "chebychev");
 Y = [1, 1];
 [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
 assert (sort (idx{1}(:))', [1, 2, 3]);
 assert (D{1}', [0, 0, 0], 1e-10);
***** test
 ## rangesearch with grid
 X = [0, 0; 0, 1; 1, 0; 1, 1];
 obj = KDTreeSearcher (X, "Distance", "chebychev");
 Y = [0.5, 0.5];
 r = 0.5;
 [idx, D] = rangesearch (obj, Y, r);
 D_true = pdist2 (X, Y, "chebychev");
 expected_idx = find (D_true <= r);
 assert (sort (idx{1}(:)), sort (expected_idx));
 assert (D{1}, D_true(expected_idx), 1e-10);
***** test
 ## Changing Distance and verifying search
 X = [0,0; 1,0];
 obj = KDTreeSearcher(X, "Distance", "euclidean");
 Y = [0,1];
 [idx, D] = knnsearch(obj, Y, "K", 1);
 assert(D, 1, 1e-10);
 obj.Distance = "chebychev";
 [idx, D] = knnsearch(obj, Y, "K", 1);
 assert(D, 1, 1e-10);
***** test
 ## Changing DistParameter for minkowski
 X = [0,0; 1,0];
 obj = KDTreeSearcher(X, "Distance", "minkowski", "P", 1);
 Y = [0,1];
 [idx, D] = knnsearch(obj, Y, "K", 1);
 assert(D, 1, 1e-10);
 obj.DistParameter = 3;
 [idx, D] = knnsearch(obj, Y, "K", 1);
 assert(D, 1, 1e-10);
***** test
 ## Different BucketSize values
 X = rand(20,2);
 obj1 = KDTreeSearcher(X, "BucketSize", 5);
 obj2 = KDTreeSearcher(X, "BucketSize", 15);
 Y = rand(1,2);
 [idx1, D1] = knnsearch(obj1, Y, "K", 3);
 [idx2, D2] = knnsearch(obj2, Y, "K", 3);
 assert(idx1, idx2);
 assert(D1, D2, 1e-10);
***** test
 ## Basic constructor with default Euclidean
 X = [1, 2; 3, 4; 5, 6];
 obj = KDTreeSearcher (X);
 assert (obj.X, X);
 assert (obj.Distance, "euclidean");
 assert (isempty (obj.DistParameter));
 assert (obj.BucketSize, 50);
***** test
 ## Minkowski distance with custom P
 X = [0, 0; 1, 1; 2, 2];
 obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
 assert (obj.Distance, "minkowski");
 assert (obj.DistParameter, 3);
***** test
 ## Cityblock distance
 X = [0, 0; 1, 0; 0, 1];
 obj = KDTreeSearcher (X, "Distance", "cityblock");
 assert (obj.Distance, "cityblock");
 assert (isempty (obj.DistParameter));
***** test
 ## Chebychev distance
 X = [1, 1; 2, 3; 4, 2];
 obj = KDTreeSearcher (X, "Distance", "chebychev");
 assert (obj.Distance, "chebychev");
 assert (isempty (obj.DistParameter));
***** test
 ## knnsearch with Euclidean distance
 X = [1, 2; 3, 4; 5, 6];
 obj = KDTreeSearcher (X);
 Y = [2, 3];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (idx, 1);
 assert (D, sqrt(2), 1e-10);
***** test
 ## knnsearch with Cityblock distance
 X = [0, 0; 1, 1; 2, 2];
 obj = KDTreeSearcher (X, "Distance", "cityblock");
 Y = [1, 0];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (ismember (idx, [1, 2]));
 assert (D, 1, 1e-10);
***** test
 ## knnsearch with Chebychev distance
 X = [1, 1; 2, 3; 4, 2];
 obj = KDTreeSearcher (X, "Distance", "chebychev");
 Y = [2, 2];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (ismember (idx, [1, 2]));
 assert (D, 1, 1e-10);
***** test
 ## knnsearch with Minkowski P=3
 X = [0, 0; 1, 0; 2, 0];
 obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
 Y = [1, 0];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (idx, 2);
 assert (D, 0, 1e-10);
***** test
 ## knnsearch with IncludeTies
 X = [0, 0; 1, 0; 0, 1];
 obj = KDTreeSearcher (X);
 Y = [0.5, 0];
 [idx, D] = knnsearch (obj, Y, "K", 1, "IncludeTies", true);
 assert (iscell (idx));
 assert (sort (idx{1}(:))', [1, 2]);
 assert (sort (D{1}(:)), [0.5; 0.5], 1e-10);
***** test
 ## rangesearch with Euclidean
 X = [1, 1; 2, 2; 3, 3];
 obj = KDTreeSearcher (X);
 Y = [0, 0];
 [idx, D] = rangesearch (obj, Y, 2);
 assert (idx{1}, [1]);
 assert (D{1}, [sqrt(2)], 1e-10);
***** test
 ## rangesearch with Cityblock
 X = [0, 0; 1, 1; 2, 2];
 obj = KDTreeSearcher (X, "Distance", "cityblock");
 Y = [0, 0];
 [idx, D] = rangesearch (obj, Y, 1);
 assert (idx{1}, [1]);
 assert (D{1}, [0], 1e-10);
***** test
 ## rangesearch with Chebychev
 X = [1, 1; 2, 3; 4, 2];
 obj = KDTreeSearcher (X, "Distance", "chebychev");
 Y = [2, 2];
 [idx, D] = rangesearch (obj, Y, 1);
 assert (sort (idx{1}(:))', [1, 2]);
 assert (sort (D{1}(:))', [1, 1], 1e-10);
***** test
 ## rangesearch with Minkowski P=3
 X = [0, 0; 1, 0; 2, 0];
 obj = KDTreeSearcher (X, "Distance", "minkowski", "P", 3);
 Y = [1, 0];
 [idx, D] = rangesearch (obj, Y, 1);
 assert (sort (idx{1}(:))', [1, 2, 3]);
 assert (sort (D{1}(:))', [0, 1, 1], 1e-10);
***** test
 ## Diverse dataset with Euclidean
 X = [0, 10; 5, 5; 10, 0];
 obj = KDTreeSearcher (X);
 Y = [5, 5];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (idx, 2);
 assert (D, 0, 1e-10);
***** test
 ## High-dimensional data with Cityblock
 X = [1, 2, 3; 4, 5, 6; 7, 8, 9];
 obj = KDTreeSearcher (X, "Distance", "cityblock");
 Y = [4, 5, 6];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (idx, 2);
 assert (D, 0, 1e-10);
***** error<KDTreeSearcher: too few input arguments.> ...
 KDTreeSearcher ()
***** error<KDTreeSearcher: Name-Value arguments must be in pairs.> ...
 KDTreeSearcher (ones(3,2), "Distance")
***** error<KDTreeSearcher: X must be a finite numeric matrix.> ...
 KDTreeSearcher ("abc")
***** error<KDTreeSearcher: X must be a finite numeric matrix.> ...
 KDTreeSearcher ([1; Inf; 3])
***** error<KDTreeSearcher: invalid parameter name: 'foo'.> ...
 KDTreeSearcher (ones(3,2), "foo", "bar")
***** error<KDTreeSearcher: unsupported distance metric 'invalid'.> ...
 KDTreeSearcher (ones(3,2), "Distance", "invalid")
***** error<KDTreeSearcher: Distance must be a string.> ...
 KDTreeSearcher (ones(3,2), "Distance", 1)
***** error<KDTreeSearcher: P must be a positive finite scalar.> ...
 KDTreeSearcher (ones(3,2), "Distance", "minkowski", "P", -1)
***** error<KDTreeSearcher: BucketSize must be a positive integer.> ...
 KDTreeSearcher (ones(3,2), "BucketSize", 0)
***** error<KDTreeSearcher: BucketSize must be a positive integer.> ...
 KDTreeSearcher(ones(3,2), "BucketSize", -1)
***** error<KDTreeSearcher.knnsearch: too few input arguments.> ...
 knnsearch (KDTreeSearcher (ones(3,2)))
***** error<KDTreeSearcher.knnsearch: Name-Value arguments must be in pairs.> ...
 knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "IncludeTies")
***** error<KDTreeSearcher.knnsearch: Y must be a finite numeric matrix.> ...
 knnsearch (KDTreeSearcher (ones(3,2)), "abc", "K", 1)
***** error<KDTreeSearcher.knnsearch: Y must have the same number of columns as the training data in OBJ.X.> ...
 knnsearch (KDTreeSearcher (ones(3,2)), ones(3,3), "K", 1)
***** error<KDTreeSearcher.knnsearch: 'K' must be a positive integer.> ...
 knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 0)
***** error<KDTreeSearcher.knnsearch: 'K' must be a positive integer.> ...
 obj = KDTreeSearcher(ones(3,2)); knnsearch(obj, ones(1,2), "K", Inf)
***** error<KDTreeSearcher.knnsearch: invalid parameter name: 'foo'.> ...
 knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "foo", "bar")
***** error<KDTreeSearcher.knnsearch: IncludeTies must be a logical scalar.> ...
 knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "IncludeTies", 1)
***** error<KDTreeSearcher.knnsearch: SortIndices must be a logical scalar.> ...
 knnsearch (KDTreeSearcher (ones(3,2)), ones(3,2), "K", 1, "SortIndices", 1)
***** error<KDTreeSearcher.rangesearch: too few input arguments.> ...
 rangesearch (KDTreeSearcher (ones(3,2)))
***** error<KDTreeSearcher.rangesearch: Name-Value arguments must be in pairs.> ...
 rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), 1, "SortIndices")
***** error<KDTreeSearcher.rangesearch: Y must be a finite numeric matrix.> ...
 rangesearch (KDTreeSearcher (ones(3,2)), "abc", 1)
***** error<KDTreeSearcher.rangesearch: number of columns in X and Y must match.> ...
 rangesearch (KDTreeSearcher (ones(3,2)), ones(3,3), 1)
***** error<KDTreeSearcher.rangesearch: R must be a nonnegative finite scalar.> ...
 rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), -1)
***** error<KDTreeSearcher.rangesearch: R must be a nonnegative finite scalar.> ...
 obj = KDTreeSearcher(ones(3,2)); rangesearch(obj, ones(1,2), Inf)
***** error<KDTreeSearcher.rangesearch: invalid parameter name: 'foo'.> ...
 rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), 1, "foo", "bar")
***** error<KDTreeSearcher.rangesearch: SortIndices must be a logical scalar.> ...
 rangesearch (KDTreeSearcher (ones(3,2)), ones(3,2), 1, "SortIndices", 1)
***** error<KDTreeSearcher.subsref: \(\) indexing not supported.> ...
 obj = KDTreeSearcher (ones(3,2)); obj(1)
***** error<KDTreeSearcher.subsref: {} indexing not supported.> ...
 obj = KDTreeSearcher (ones(3,2)); obj{1}
***** error<KDTreeSearcher.subsref: unrecognized property: 'invalid'.> ...
 obj = KDTreeSearcher (ones(3,2)); obj.invalid
***** error<KDTreeSearcher.subsasgn: \(\) indexing not supported.> ...
 obj = KDTreeSearcher (ones(3,2)); obj(1) = 1
***** error<KDTreeSearcher.subsasgn: {} indexing not supported.> ...
 obj = KDTreeSearcher (ones(3,2)); obj{1} = 1
***** error<KDTreeSearcher.subsasgn: chained subscripts not allowed.> ...
 obj = KDTreeSearcher (ones(3,2)); obj.X.Y = 1
***** error<KDTreeSearcher.subsasgn: 'X' is read-only and cannot be modified.> ...
 obj = KDTreeSearcher (ones(3,2)); obj.X = 1
***** error<KDTreeSearcher.subsasgn: 'KDTree' is read-only and cannot be modified.> ...
 obj = KDTreeSearcher (ones(3,2)); obj.KDTree = 1
***** error<KDTreeSearcher.subsasgn: unsupported distance metric 'invalid'.> ...
 obj = KDTreeSearcher (ones(3,2)); obj.Distance = "invalid"
***** error<KDTreeSearcher.subsasgn: 'Distance' must be a string.> ...
 obj = KDTreeSearcher (ones(3,2)); obj.Distance = 1
***** error<KDTreeSearcher.subsasgn: 'DistParameter' must be a positive finite scalar for Minkowski distance.> ...
 obj = KDTreeSearcher (ones(3,2), "Distance", "minkowski"); obj.DistParameter = -1
***** error<KDTreeSearcher.subsasgn: 'DistParameter' must be empty for this distance metric.> ...
 obj = KDTreeSearcher (ones(3,2)); obj.DistParameter = 1
***** error<KDTreeSearcher.subsasgn: 'BucketSize' is read-only and cannot be modified.> ...
 obj = KDTreeSearcher (ones(3,2)); obj.BucketSize = 0
***** error<KDTreeSearcher.subsasgn: 'BucketSize' is read-only and cannot be modified.> ...
 obj = KDTreeSearcher(ones(3,2)); obj.BucketSize = -1
***** error<KDTreeSearcher.subsasgn: 'BucketSize' is read-only and cannot be modified.> ...
 obj = KDTreeSearcher(ones(3,2)); obj.BucketSize = 1.5
***** error<KDTreeSearcher.subsasgn: unrecognized property: 'invalid'.> ...
 obj = KDTreeSearcher (ones(3,2)); obj.invalid = 1
84 tests, 84 passed, 0 known failure, 0 skipped
[inst/Clustering/SilhouetteEvaluation.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Clustering/SilhouetteEvaluation.m
***** test
 load fisheriris
 eva = evalclusters (meas, "kmeans", "silhouette", "KList", [1:6]);
 assert (class (eva), "SilhouetteEvaluation");
1 test, 1 passed, 0 known failure, 0 skipped
[inst/Clustering/CalinskiHarabaszEvaluation.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Clustering/CalinskiHarabaszEvaluation.m
***** test
 load fisheriris
 eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
 assert (class (eva), "CalinskiHarabaszEvaluation");
1 test, 1 passed, 0 known failure, 0 skipped
[inst/Clustering/ClusterCriterion.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Clustering/ClusterCriterion.m
***** error <ClusterCriterion: X must be a numeric matrix.> ...
 ClusterCriterion ("1", "kmeans", [1:6])
***** error <ClusterCriterion: unknown clustering algorithm 'k'.> ...
 ClusterCriterion ([1, 2, 1, 3, 2, 4, 3], "k", [1:6])
***** error <ClusterCriterion: invalid matrix of clustering solutions.> ...
 ClusterCriterion ([1, 2, 1; 3, 2, 4], 1, [1:6])
***** error <ClusterCriterion: invalid argument.> ...
 ClusterCriterion ([1, 2, 1; 3, 2, 4], ones (2, 2, 2), [1:6])
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/Clustering/DaviesBouldinEvaluation.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Clustering/DaviesBouldinEvaluation.m
***** test
 load fisheriris
 eva = evalclusters (meas, "kmeans", "DaviesBouldin", "KList", [1:6]);
 assert (class (eva), "DaviesBouldinEvaluation");
1 test, 1 passed, 0 known failure, 0 skipped
[inst/Clustering/ExhaustiveSearcher.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Clustering/ExhaustiveSearcher.m
***** demo
 ## Demo to verify implementation using fisheriris dataset
 load fisheriris
 rng('default');
 numSamples = size (meas, 1);
 queryIndices = [20, 95, 123, 136, 138];
 dataPoints = meas(~ismember (1:numSamples, queryIndices), :);
 queryPoints = meas(queryIndices, :);
 searchModel = ExhaustiveSearcher (dataPoints, 'Distance', 'mahalanobis')
 mahalanobisParam = searchModel.DistParameter
 searchRadius = 3;
 nearestNeighbors = knnsearch (searchModel, queryPoints, "K", 2)
 neighborsInRange = rangesearch (searchModel, queryPoints, searchRadius)
***** demo
 ## Create an ExhaustiveSearcher with Euclidean distance
 X = [1, 2; 3, 4; 5, 6];
 obj = ExhaustiveSearcher (X);
 ## Find the nearest neighbor to [2, 3]
 Y = [2, 3];
 [idx, D] = knnsearch (obj, Y);
 disp ("Nearest neighbor index:"); disp (idx);
 disp ("Distance:"); disp (D);
 ## Find all points within radius 2
 [idx, D] = rangesearch (obj, Y, 2);
 disp ("Indices within radius:"); disp (idx);
 disp ("Distances:"); disp (D);
***** demo
 ## Create an ExhaustiveSearcher with Minkowski distance (P=1)
 X = [0, 0; 1, 0; 0, 1];
 obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 1);
 ## Find the 2 nearest neighbors to [0.5, 0.5]
 Y = [0.5, 0.5];
 [idx, D] = knnsearch (obj, Y, "K", 2);
 disp ("Nearest neighbor indices:"); disp (idx);
 disp ("Distances:"); disp (D);
***** demo
 rng(42);
 disp('Demonstrating ExhaustiveSearcher');

 n = 100;
 mu1 = [0.3, 0.3];
 mu2 = [0.7, 0.7];
 sigma = 0.1;
 X1 = mu1 + sigma * randn(n/2, 2);
 X2 = mu2 + sigma * randn(n/2, 2);
 X = [X1; X2];

 obj = ExhaustiveSearcher(X);

 Y = [0.3, 0.3; 0.7, 0.7; 0.5, 0.5];

 K = 5;
 [idx, D] = knnsearch(obj, Y, "K", K);

 disp('For the first query point:');
 disp(['Query point: ', num2str(Y(1,:))]);
 disp('Indices of nearest neighbors:');
 disp(idx(1,:));
 disp('Distances:');
 disp(D(1,:));

 figure;
 scatter(X(:,1), X(:,2), 36, 'b', 'filled'); % Training points
 hold on;
 scatter(Y(:,1), Y(:,2), 36, 'r', 'filled'); % Query points
 for i = 1:size(Y,1)
     query = Y(i,:);
     neighbors = X(idx(i,:), :);
     for j = 1:K
         plot([query(1), neighbors(j,1)], [query(2), neighbors(j,2)], 'k-');
     end
 end
 hold off;
 title('K Nearest Neighbors with ExhaustiveSearcher');
 xlabel('X1');
 ylabel('X2');

 r = 0.15;
 [idx, D] = rangesearch(obj, Y, r);

 disp('For the first query point in rangesearch:');
 disp(['Query point: ', num2str(Y(1,:))]);
 disp('Indices of points within radius:');
 disp(idx{1});
 disp('Distances:');
 disp(D{1});

 figure;
 scatter(X(:,1), X(:,2), 36, 'b', 'filled');
 hold on;
 scatter(Y(:,1), Y(:,2), 36, 'r', 'filled');
 theta = linspace(0, 2*pi, 100);
 for i = 1:size(Y,1)
     center = Y(i,:);
     x_circle = center(1) + r * cos(theta);
     y_circle = center(2) + r * sin(theta);
     plot(x_circle, y_circle, 'g-');
     % Highlight points within radius
     if ~isempty(idx{i})
         in_radius = X(idx{i}, :);
         scatter(in_radius(:,1), in_radius(:,2), 36, 'g', 'filled');
     end
 end
 hold off;
 title('Points within Radius with ExhaustiveSearcher');
 xlabel('X1');
 ylabel('X2');
***** test
 ## Basic constructor with default Euclidean
 X = [1, 2; 3, 4; 5, 6];
 obj = ExhaustiveSearcher (X);
 assert (obj.X, X)
 assert (obj.Distance, "euclidean")
 assert (isempty (obj.DistParameter))
***** test
 ## Minkowski distance with custom P
 X = [1, 2; 3, 4];
 obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 3);
 assert (obj.Distance, "minkowski")
 assert (obj.DistParameter, 3)
***** test
 ## Seuclidean distance with custom Scale
 X = [1, 2; 3, 4; 5, 6];
 S = [1, 2];
 obj = ExhaustiveSearcher (X, "Distance", "seuclidean", "Scale", S);
 assert (obj.Distance, "seuclidean")
 assert (obj.DistParameter, S)
***** test
 ## Mahalanobis distance with custom Cov
 X = [1, 2; 3, 4; 5, 6];
 C = [1, 0; 0, 1];
 obj = ExhaustiveSearcher (X, "Distance", "mahalanobis", "Cov", C);
 assert (obj.Distance, "mahalanobis")
 assert (obj.DistParameter, C)
***** test
 ## knnsearch with Euclidean distance
 X = [1, 2; 3, 4; 5, 6];
 obj = ExhaustiveSearcher (X);
 Y = [2, 3];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (idx, 1)
 assert (D, sqrt(2), 1e-10)
***** test
 ## knnsearch with Cityblock distance
 X = [0, 0; 1, 1; 2, 2];
 obj = ExhaustiveSearcher (X, "Distance", "cityblock");
 Y = [1, 0];
 [idx, D] = knnsearch (obj, Y, "K", 1);
 assert (idx, 1)
 assert (D, 1, 1e-10)
***** test
 ## knnsearch with Chebychev distance
 X = [1, 1; 2, 3; 4, 2];
 obj = ExhaustiveSearcher (X, "Distance", "chebychev");
 Y = [2, 2];
 [idx, D] = knnsearch (obj, Y);
 assert (idx, 1)
 assert (D, 1, 1e-10)
***** test
 ## knnsearch with Cosine distance
 X = [1, 0; 0, 1; 1, 1];
 obj = ExhaustiveSearcher (X, "Distance", "cosine");
 Y = [1, 0.5];
 [idx, D] = knnsearch (obj, Y);
 assert (idx, 3)
 assert (D < 0.1, true)
***** test
 ## knnsearch with Minkowski P=1 (Manhattan)
 X = [0, 0; 1, 0; 0, 1];
 obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 1);
 Y = [0.5, 0.5];
 [idx, D] = knnsearch (obj, Y, "K", 2, "IncludeTies", true);
 assert (iscell (idx))
 assert (idx{1}, [1, 2, 3])
 assert (D{1}, [1, 1, 1], 1e-10)
***** test
 ## rangesearch with Seuclidean
 X = [1, 1; 2, 2; 3, 3];
 S = [1, 1];
 obj = ExhaustiveSearcher (X, "Distance", "seuclidean", "Scale", S);
 Y = [0, 0];
 [idx, D] = rangesearch (obj, Y, 2);
 assert (idx{1}, [1])
 assert (D{1}, [sqrt(2)], 1e-10)
***** test
 ## rangesearch with Mahalanobis
 X = [1, 1; 2, 2; 3, 3];
 C = [1, 0; 0, 1];
 obj = ExhaustiveSearcher (X, "Distance", "mahalanobis", "Cov", C);
 Y = [0, 0];
 [idx, D] = rangesearch (obj, Y, 3, "SortIndices", false);
 assert (idx{1}, [1, 2])
 assert (D{1}, [sqrt(2), sqrt(8)], 1e-10)
***** test
 ## rangesearch with Hamming distance
 X = [0, 1; 1, 0; 1, 1];
 obj = ExhaustiveSearcher (X, "Distance", "hamming");
 Y = [0, 0];
 [idx, D] = rangesearch (obj, Y, 0.5);
 assert (idx{1}, [1, 2])
 assert (D{1}, [0.5, 0.5], 1e-10)
***** test
 ## Custom distance function
 X = [1, 2; 3, 4];
 custom_dist = @(x, y) sum(abs(x - y));
 obj = ExhaustiveSearcher (X, "Distance", custom_dist);
 Y = [2, 3];
 [idx, D] = knnsearch (obj, Y);
 assert (idx, 1)
 assert (D, 2, 1e-10)
***** test
 ## IncludeTies returns all tied neighbors
 X = [0; 1; 2];
 obj = ExhaustiveSearcher (X);
 Y = 1;
 [idx, D] = knnsearch (obj, Y, "K", 2, "IncludeTies", true);
 assert (idx{1}, [2, 1, 3])
 assert (D{1}, [0, 1, 1])
***** test
 ## Custom distance function with vectorized output
 X = [1, 2; 3, 4];
 f = @(x, y) sum(abs(x - y), 2);
 obj = ExhaustiveSearcher (X, "Distance", f);
 Y = [2, 3];
 [idx, D] = knnsearch (obj, Y);
 assert (idx, 1)
 assert (D, 2)
***** test
 ## Euclidean with high-dimensional data
 X = [1, 2, 3; 4, 5, 6; 7, 8, 9; 10, 11, 12];
 obj = ExhaustiveSearcher (X);
 Y = [5, 6, 7];
 [idx, D] = knnsearch (obj, Y);
 assert (idx, 2)
 assert (D, sqrt(3), 1e-10)
***** test
 ## Minkowski P=3 with scaled data
 X = [0, 1; 2, 3; 4, 5] * 10;
 obj = ExhaustiveSearcher (X, "Distance", "minkowski", "P", 3);
 Y = [20, 30];
 [idx, D] = knnsearch (obj, Y);
 assert (idx, 2)
 assert (D, 0, 1e-10)
***** test
 ## Seuclidean with custom scales on diverse data
 X = [1, 10; 2, 20; 3, 30];
 S = [1, 5];
 obj = ExhaustiveSearcher (X, "Distance", "seuclidean", "Scale", S);
 Y = [1.5, 15];
 [idx, D] = knnsearch (obj, Y);
 assert (idx, 1)
 assert (D, sqrt((0.5/1)^2 + (5/5)^2), 1e-10)
***** test
 ## Mahalanobis with correlated data
 X = [1, 1; 2, 1.5; 3, 2];
 C = [1, 0.5; 0.5, 1];
 obj = ExhaustiveSearcher (X, "Distance", "mahalanobis", "Cov", C);
 Y = [2, 1.5];
 [idx, D] = knnsearch (obj, Y);
 assert (idx, 2)
 assert (D, 0, 1e-10)
***** test
 ## Cityblock with sparse data
 X = [0, 0, 1; 1, 0, 0; 0, 1, 0];
 obj = ExhaustiveSearcher (X, "Distance", "cityblock");
 Y = [0, 0, 0];
 [idx, D] = rangesearch (obj, Y, 1);
 assert (idx{1}, [1, 2, 3])
 assert (D{1}, [1, 1, 1], 1e-10)
***** test
 ## Chebychev with extreme values
 X = [0, 100; 50, 50; 100, 0];
 obj = ExhaustiveSearcher (X, "Distance", "chebychev");
 Y = [60, 60];
 [idx, D] = knnsearch (obj, Y);
 assert (idx, 2)
 assert (D, 10, 1e-10)
***** test
 ## Cosine with normalized data
 X = [1, 0; 0, 1; 1/sqrt(2), 1/sqrt(2)];
 obj = ExhaustiveSearcher (X, "Distance", "cosine");
 Y = [1, 1];
 [idx, D] = knnsearch (obj, Y);
 assert (idx, 3)
 assert (D < 0.1, true)
***** test
 ## Correlation with time-series-like data
 X = [1, 2, 3; 2, 4, 6; 1, 1, 1];
 obj = ExhaustiveSearcher (X, "Distance", "correlation");
 Y = [1.5, 3, 4.5];
 [idx, D] = knnsearch (obj, Y);
 assert (idx, 1)
 assert (D < 0.1, true)
***** test
 ## Spearman with ranked data
 X = [1, 2, 3; 3, 2, 1; 2, 1, 3];
 obj = ExhaustiveSearcher (X, "Distance", "spearman");
 Y = [1, 2, 3];
 [idx, D] = knnsearch (obj, Y);
 assert (idx, 1)
 assert (D, 0, 1e-10)
***** test
 ## Jaccard with binary sparse data
 X = [1, 0, 0; 0, 1, 0; 1, 1, 0];
 obj = ExhaustiveSearcher (X, "Distance", "jaccard");
 Y = [1, 0, 0];
 [idx, D] = knnsearch (obj, Y);
 assert (idx, 1)
 assert (D, 0, 1e-10)
***** test
 obj = ExhaustiveSearcher (ones(3,2));
 assert (obj.X, ones(3,2))
 assert (obj.Distance, "euclidean")
 assert (isempty (obj.DistParameter))
***** test
 obj = ExhaustiveSearcher (ones(3,2));
 obj.Distance = "minkowski";
 assert (obj.Distance, "minkowski")
***** test
 obj = ExhaustiveSearcher (ones(3,2), "Distance", "minkowski");
 obj.DistParameter = 3;
 assert (obj.DistParameter, 3)
***** test
 obj = ExhaustiveSearcher (ones(3,2), "Distance", "seuclidean");
 obj.DistParameter = [1, 2];
 assert (obj.DistParameter, [1, 2])
***** test
 obj = ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis");
 obj.DistParameter = eye(2);
 assert (obj.DistParameter, eye(2))
***** error<ExhaustiveSearcher: too few input arguments.> ...
 ExhaustiveSearcher ()
***** error<ExhaustiveSearcher: Name-Value arguments must be in pairs.> ...
 ExhaustiveSearcher (ones(3,2), "Distance")
***** error<ExhaustiveSearcher: X must be a finite numeric matrix.> ...
 ExhaustiveSearcher ("abc")
***** error<ExhaustiveSearcher: X must be a finite numeric matrix.> ...
 ExhaustiveSearcher ([1; Inf; 3])
***** error<ExhaustiveSearcher: invalid parameter name: 'foo'.> ...
 ExhaustiveSearcher (ones(3,2), "foo", "bar")
***** error<ExhaustiveSearcher: unsupported distance metric 'invalid'.> ...
 ExhaustiveSearcher (ones(3,2), "Distance", "invalid")
***** error<ExhaustiveSearcher: invalid distance function handle.> ...
 ExhaustiveSearcher (ones(3,2), "Distance", @(x) x)
***** error<ExhaustiveSearcher: Distance must be a string or function handle.> ...
 ExhaustiveSearcher (ones(3,2), "Distance", 1)
***** error<ExhaustiveSearcher: P must be a positive finite scalar.> ...
 ExhaustiveSearcher (ones(3,2), "Distance", "minkowski", "P", -1)
***** error<ExhaustiveSearcher: Scale must be a nonnegative vector matching X columns.> ...
 ExhaustiveSearcher (ones(3,2), "Distance", "seuclidean", "Scale", [-1, 1])
***** error<ExhaustiveSearcher: Cov must be a square matrix matching X columns.> ...
 ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", ones(3,3))
***** error<ExhaustiveSearcher: Cov must be positive definite.> ...
 ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis", "Cov", -eye(2))
***** error<ExhaustiveSearcher.knnsearch: too few input arguments.> ...
 knnsearch (ExhaustiveSearcher (ones(3,2)))
***** error<ExhaustiveSearcher.knnsearch: Name-Value arguments must be in pairs.> ...
 knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "IncludeTies")
***** error<ExhaustiveSearcher.knnsearch: Y must be a finite numeric matrix.> ...
 knnsearch (ExhaustiveSearcher (ones(3,2)), "abc")
***** error<ExhaustiveSearcher.knnsearch: number of columns in X and Y must match.> ...
 knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,3))
***** error<ExhaustiveSearcher.knnsearch: K must be a positive integer.> ...
 knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "K", 0)
***** error<ExhaustiveSearcher.knnsearch: invalid parameter name: 'foo'.> ...
 knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "foo", "bar")
***** error<ExhaustiveSearcher.knnsearch: IncludeTies must be a logical scalar.> ...
 knnsearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), "IncludeTies", 1)
***** error<ExhaustiveSearcher.rangesearch: too few input arguments.> ...
 rangesearch (ExhaustiveSearcher (ones(3,2)))
***** error<ExhaustiveSearcher.rangesearch: Name-Value arguments must be in pairs.> ...
 rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), 1, "SortIndices")
***** error<ExhaustiveSearcher.rangesearch: Y must be a finite numeric matrix.> ...
 rangesearch (ExhaustiveSearcher (ones(3,2)), "abc", 1)
***** error<ExhaustiveSearcher.rangesearch: number of columns in X and Y must match.> ...
 rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,3), 1)
***** error<ExhaustiveSearcher.rangesearch: R must be a nonnegative finite scalar.> ...
 rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), -1)
***** error<ExhaustiveSearcher.rangesearch: invalid parameter name: 'foo'.> ...
 rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), 1, "foo", "bar")
***** error<ExhaustiveSearcher.rangesearch: SortIndices must be a logical scalar.> ...
 rangesearch (ExhaustiveSearcher (ones(3,2)), ones(3,2), 1, "SortIndices", 1)
***** error<ExhaustiveSearcher.subsref: \(\) indexing not supported.> ...
 obj = ExhaustiveSearcher (ones(3,2)); obj(1)
***** error<ExhaustiveSearcher.subsref: {} indexing not supported.> ...
 obj = ExhaustiveSearcher (ones(3,2)); obj{1}
***** error<dynamic structure field names must be strings> ...
 obj = ExhaustiveSearcher (ones(3,2)); obj.(1)
***** error<ExhaustiveSearcher.subsref: unrecognized property: 'invalid'> ...
 obj = ExhaustiveSearcher (ones(3,2)); obj.invalid
***** error<ExhaustiveSearcher.subsasgn: \(\) indexing not supported.> ...
 obj = ExhaustiveSearcher (ones(3,2)); obj(1) = 1
***** error<ExhaustiveSearcher.subsasgn: {} indexing not supported.> ...
 obj = ExhaustiveSearcher (ones(3,2)); obj{1} = 1
***** error<ExhaustiveSearcher.subsasgn: chained subscripts not allowed.> ...
 obj = ExhaustiveSearcher (ones(3,2)); obj.X.Y = 1
***** error<dynamic structure field names must be strings> ...
 obj = ExhaustiveSearcher (ones(3,2)); obj.(1) = 1
***** error<ExhaustiveSearcher.subsasgn: X is read-only and cannot be modified.> ...
 obj = ExhaustiveSearcher (ones(3,2)); obj.X = 1
***** error<ExhaustiveSearcher.subsasgn: unsupported distance metric 'invalid'.> ...
 obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = "invalid"
***** error<ExhaustiveSearcher.subsasgn: invalid distance function handle.> ...
 obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = @(x) x
***** error<ExhaustiveSearcher.subsasgn: custom distance function output invalid.> ...
 obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = @(x, y) [1; 1]
***** error<ExhaustiveSearcher.subsasgn: Distance must be a string or function handle.> ...
 obj = ExhaustiveSearcher (ones(3,2)); obj.Distance = 1
***** error<ExhaustiveSearcher.subsasgn: DistParameter must be a positive finite scalar for minkowski.> ...
 obj = ExhaustiveSearcher (ones(3,2), "Distance", "minkowski"); obj.DistParameter = -1
***** error<ExhaustiveSearcher.subsasgn: DistParameter must be a nonnegative vector matching X columns.> ...
 obj = ExhaustiveSearcher (ones(3,2), "Distance", "seuclidean"); obj.DistParameter = [-1, 1]
***** error<ExhaustiveSearcher.subsasgn: DistParameter must be a square matrix matching X columns.> ...
 obj = ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = ones(3,3)
***** error<ExhaustiveSearcher.subsasgn: DistParameter must be positive definite for mahalanobis.> ...
 obj = ExhaustiveSearcher (ones(3,2), "Distance", "mahalanobis"); obj.DistParameter = -eye(2)
***** error<ExhaustiveSearcher.subsasgn: DistParameter must be empty for this distance metric.> ...
 obj = ExhaustiveSearcher (ones(3,2), "Distance", "euclidean"); obj.DistParameter = 1
***** error<ExhaustiveSearcher.subsasgn: unrecognized property: 'invalid'> ...
 obj = ExhaustiveSearcher (ones(3,2)); obj.invalid = 1
75 tests, 75 passed, 0 known failure, 0 skipped
[inst/rmmissing.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/rmmissing.m
***** assert (rmmissing ([1, NaN, 3]), [1, 3])
***** assert (rmmissing ('abcd f'), 'abcd f')
***** assert (rmmissing ({'xxx', '', 'xyz'}), {'xxx', 'xyz'})
***** assert (rmmissing ({'xxx', ''; 'xyz', 'yyy'}), {'xyz', 'yyy'})
***** assert (rmmissing ({'xxx', ''; 'xyz', 'yyy'}, 2), {'xxx'; 'xyz'})
***** assert (rmmissing ([1, 2; NaN, 2]), [1, 2])
***** assert (rmmissing ([1, 2; NaN, 2], 2), [2, 2]')
***** assert (rmmissing ([1, 2; NaN, 4; NaN, NaN],"MinNumMissing", 2), [1, 2; NaN, 4])
***** test
 x = [1:6];
 x([2,4]) = NaN;
 [~, idx] = rmmissing (x);
 assert (idx, logical ([0, 1, 0, 1, 0, 0]));
 assert (class(idx), 'logical');
 x = reshape (x, [2, 3]);
 [~, idx] = rmmissing (x);
 assert (idx, logical ([0; 1]));
 assert (class(idx), 'logical');
 [~, idx] = rmmissing (x, 2);
 assert (idx, logical ([1, 1, 0]));
 assert (class(idx), 'logical');
 [~, idx] = rmmissing (x, 1, "MinNumMissing", 2);
 assert (idx, logical ([0; 1]));
 assert (class(idx), 'logical');
 [~, idx] = rmmissing (x, 2, "MinNumMissing", 2);
 assert (idx, logical ([0, 0, 0]));
 assert (class(idx), 'logical');
***** assert (rmmissing (single ([1, 2, NaN; 3, 4, 5])), single ([3, 4, 5]))
***** assert (rmmissing (logical (ones (3))), logical (ones (3)))
***** assert (rmmissing (int32 (ones (3))), int32 (ones (3)))
***** assert (rmmissing (uint32 (ones (3))), uint32 (ones (3)))
***** assert (rmmissing ({1, 2, 3}), {1, 2, 3})
***** assert (rmmissing ([struct, struct, struct]), [struct, struct, struct])
***** assert (rmmissing ([]), [])
***** assert (rmmissing (ones (1, 0)), ones (1, 0))
***** assert (rmmissing (ones (1, 0), 1), ones (1, 0))
***** assert (rmmissing (ones (1, 0), 2), ones (1, 0))
***** assert (rmmissing (ones (0, 1)), ones (0, 1))
***** assert (rmmissing (ones (0, 1), 1), ones (0, 1))
***** assert (rmmissing (ones (0, 1), 2), ones (0, 1))
***** error <rmmissing: A must be a matrix; no more than 2 dimensions allowed.> ...
       rmmissing (ones (0, 1, 2))
***** error rmmissing ()
***** error <rmmissing: A must be a matrix; no more than 2 dimensions allowed.> ...
       rmmissing (ones(2, 2, 2))
***** error <rmmissing: 'MinNumMissing' must be a positive integer value.> ...
       rmmissing (ones(2, 2), 'MinNumMissing', 0)
***** error <rmmissing: 'MinNumMissing' must be a positive integer value.> ...
       rmmissing ([1, 2; 3, 4], 2, "MinNumMissing", -2)
***** error <rmmissing: 'MinNumMissing' must be a positive integer value.> ...
       rmmissing ([1, 2; 3, 4], "MinNumMissing", 3.8)
***** error <rmmissing: 'MinNumMissing' must be a positive integer value.> ...
       rmmissing ([1, 2; 3, 4], "MinNumMissing", [1, 2, 3])
***** error <rmmissing: 'MinNumMissing' must be a positive integer value.> ...
       rmmissing ([1, 2; 3, 4], "MinNumMissing", 'xxx')
***** error <rmmissing: 'MissingLocations' must be a logical matrix of the same size as input A.> ...
       rmmissing ([1, 2; 3, 4], 'MissingLocations', false ([1, 1, 1]))
***** error <rmmissing: specified DIM must be either 1 or 2.> rmmissing ([1, 2; 3, 4], 5)
***** error <rmmissing: too many input arguments.> rmmissing ([1, 2; 3, 4], 'XXX', 1)
33 tests, 33 passed, 0 known failure, 0 skipped
[inst/standardizeMissing.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/standardizeMissing.m
***** assert (standardizeMissing (1, 1), NaN)
***** assert (standardizeMissing (1, 0), 1)
***** assert (standardizeMissing (eye(2), 1), [NaN 0;0 NaN])
***** assert (standardizeMissing ([1:3;4:6], [2 3 4 5]), [1, NaN, NaN; NaN, NaN, 6])
***** assert (standardizeMissing (cat (3,1,2,3,4), 3), cat (3,1,2,NaN,4))
***** assert (standardizeMissing ('foo', 'a'), 'foo')
***** assert (standardizeMissing ('foo', 'f'), 'foo')
***** assert (standardizeMissing ('foo', 'o'), 'foo')
***** assert (standardizeMissing ('foo', 'oo'), 'foo')
***** assert (standardizeMissing ({'foo'}, 'f'), {'foo'})
***** assert (standardizeMissing ({'foo'}, {'f'}), {'foo'})
***** assert (standardizeMissing ({'foo'}, 'test'), {'foo'})
***** assert (standardizeMissing ({'foo'}, {'test'}), {'foo'})
***** assert (standardizeMissing ({'foo'}, 'foo'), {''})
***** assert (standardizeMissing ({'foo'}, {'foo'}), {''})
***** assert (standardizeMissing (['foo';'bar'], 'oar'), ['foo';'bar'])
***** assert (standardizeMissing (['foo';'bar'], ['o';'a';'r']), ['foo';'bar'])
***** assert (standardizeMissing (['foo';'bar'], ['o ';'ar']), ['foo';'bar'])
***** assert (standardizeMissing ({'foo','bar'}, 'foo'), {'','bar'})
***** assert (standardizeMissing ({'foo','bar'}, 'f'), {'foo','bar'})
***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
***** assert (standardizeMissing ({'foo'}, {'f', 'oo'}), {'foo'})
***** assert (standardizeMissing ({'foo','bar'}, {'foo'}), {'','bar'})
***** assert (standardizeMissing ({'foo','bar'}, {'foo', 'a'}), {'','bar'})
***** assert (standardizeMissing (double (1), single (1)), double (NaN))
***** assert (standardizeMissing (single (1), single (1)), single (NaN))
***** assert (standardizeMissing (single (1), double (1)), single (NaN))
***** assert (standardizeMissing (single (1), uint8 (1)), single (NaN))
***** assert (standardizeMissing (double (1), int32 (1)), double (NaN))
***** assert (standardizeMissing (true, true), true)
***** assert (standardizeMissing (true, 1), true)
***** assert (standardizeMissing (int32 (1), int32 (1)), int32 (1))
***** assert (standardizeMissing (int32 (1), 1), int32 (1))
***** assert (standardizeMissing (uint32 (1), uint32 (1)), uint32 (1))
***** assert (standardizeMissing (uint32 (1), 1), uint32 (1))
***** assert (standardizeMissing ({'abc', 1}, 1), {'abc', 1})
***** assert (standardizeMissing (struct ('a','b'), 1), struct ('a','b'))
***** assert (double (standardizeMissing (categorical (1), categorical (1))), NaN)
***** assert (double (standardizeMissing (categorical (1), '1')), NaN)
***** assert (class (standardizeMissing (categorical (1), categorical (1))), 'categorical')
***** assert (double (standardizeMissing (categorical (1), categorical (2))), 1)
***** assert (double (standardizeMissing (categorical (1), '2')), 1)
***** assert (class (standardizeMissing (categorical (1), categorical (2))), 'categorical')
***** assert (isnat (standardizeMissing (datetime ('today'), datetime ('today'))), true)
__datetime__: TZDB error: Could not get current timezone
Falling back to UTC.
__datetime__: TZDB error: Could not get current timezone
Falling back to UTC.
__datetime__: TZDB error: Could not get current timezone
Falling back to UTC.
__datetime__: TZDB error: Could not get current timezone
Falling back to UTC.
***** assert (isnat (standardizeMissing (datetime ('today'), datetime ('yesterday'))), false)
__datetime__: TZDB error: Could not get current timezone
Falling back to UTC.
__datetime__: TZDB error: Could not get current timezone
Falling back to UTC.
__datetime__: TZDB error: Could not get current timezone
Falling back to UTC.
__datetime__: TZDB error: Could not get current timezone
Falling back to UTC.
***** assert (days (standardizeMissing (days (1), days (1))), NaN)
***** assert (days (standardizeMissing (days (1), days (2))), 1)
***** assert (cellstr (standardizeMissing (string (1), string (1))), {''})
***** assert (cellstr (standardizeMissing (string (1), string (2))), {'1'})
***** error <Invalid call> standardizeMissing ();
***** error <Invalid call> standardizeMissing (1);
***** error <standardizeMissing: function called with too many inputs> standardizeMissing (1, 2, 3);
***** error <standardizeMissing: incompatible INDICATOR and input data A.> ...
       standardizeMissing ([1, 2, 3], {1});
***** error <standardizeMissing: incompatible INDICATOR and input data A.> ...
       standardizeMissing ([1, 2, 3], 'a');
***** error <standardizeMissing: incompatible INDICATOR and input data A.> ...
       standardizeMissing ([1, 2, 3], struct ('a', 1));
***** error <standardizeMissing: incompatible INDICATOR and input data A.> ...
       standardizeMissing (categorical (1), 1);
***** error <standardizeMissing: incompatible INDICATOR and input data A.> ...
       standardizeMissing ({'foo'}, string ('foo'));
***** error <standardizeMissing: character INDICATOR must be a row vector.> ...
       standardizeMissing ({'foo'}, ['a';'b']);
58 tests, 58 passed, 0 known failure, 0 skipped
[inst/kmeans.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/kmeans.m
***** demo
 ## Generate a two-cluster problem
 randn ("seed", 31)  # for reproducibility
 C1 = randn (100, 2) + 1;
 randn ("seed", 32)  # for reproducibility
 C2 = randn (100, 2) - 1;
 data = [C1; C2];

 ## Perform clustering
 rand ("seed", 1)  # for reproducibility
 [idx, centers] = kmeans (data, 2);

 ## Plot the result
 figure;
 plot (data (idx==1, 1), data (idx==1, 2), "ro");
 hold on;
 plot (data (idx==2, 1), data (idx==2, 2), "bs");
 plot (centers (:, 1), centers (:, 2), "kv", "markersize", 10);
 title ("A simple two-clusters example");
 hold off;
***** demo
 ## Cluster data using k-means clustering, then plot the cluster regions
 ## Load Fisher's iris data set and use the petal lengths and widths as
 ## predictors

 load fisheriris
 X = meas(:,3:4);

 plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
 title ("Fisher's Iris Data");
 xlabel ("Petal Lengths (cm)");
 ylabel ("Petal Widths (cm)");

 ## Cluster the data. Specify k = 3 clusters
 rand ("seed", 1)  # for reproducibility
 [idx, C] = kmeans (X, 3);
 x1 = min (X(:,1)):0.01:max (X(:,1));
 x2 = min (X(:,2)):0.01:max (X(:,2));
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];

 idx2Region = kmeans (XGrid, 3, "MaxIter", 10, "Start", C);
 figure;
 gscatter (XGrid(:,1), XGrid(:,2), idx2Region, ...
           [0, 0.75, 0.75; 0.75, 0, 0.75; 0.75, 0.75, 0], "..");
 hold on;
 plot (X(:,1), X(:,2), "k*", "MarkerSize", 5);
 title ("Fisher's Iris Data");
 xlabel ("Petal Lengths (cm)");
 ylabel ("Petal Widths (cm)");
 legend ("Region 1", "Region 2", "Region 3", "Data", "Location", "SouthEast");
 hold off
***** demo
 ## Partition Data into Two Clusters

 randn ("seed", 1)  # for reproducibility
 r1 = randn (100, 2) * 0.75 + ones (100, 2);
 randn ("seed", 2)  # for reproducibility
 r2 = randn (100, 2) * 0.5 - ones (100, 2);
 X = [r1; r2];

 plot (X(:,1), X(:,2), ".");
 title ("Randomly Generated Data");
 rand ("seed", 1)  # for reproducibility
 [idx, C] = kmeans (X, 2, "Distance", "cityblock", ...
                          "Replicates", 5, "Display", "final");
 figure;
 plot (X(idx==1,1), X(idx==1,2), "r.", "MarkerSize", 12);
 hold on
 plot(X(idx==2,1), X(idx==2,2), "b.", "MarkerSize", 12);
 plot (C(:,1), C(:,2), "kx", "MarkerSize", 15, "LineWidth", 3);
 legend ("Cluster 1", "Cluster 2", "Centroids", "Location", "NorthWest");
 title ("Cluster Assignments and Centroids");
 hold off
***** demo
 ## Assign New Data to Existing Clusters

 ## Generate a training data set using three distributions
 randn ("seed", 5)  # for reproducibility
 r1 = randn (100, 2) * 0.75 + ones (100, 2);
 randn ("seed", 7)  # for reproducibility
 r2 = randn (100, 2) * 0.5 - ones (100, 2);
 randn ("seed", 9)  # for reproducibility
 r3 = randn (100, 2) * 0.75;
 X = [r1; r2; r3];

 ## Partition the training data into three clusters by using kmeans

 rand ("seed", 1)  # for reproducibility
 [idx, C] = kmeans (X, 3);

 ## Plot the clusters and the cluster centroids

 gscatter (X(:,1), X(:,2), idx, "bgm", "***");
 hold on
 plot (C(:,1), C(:,2), "kx");
 legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid")

 ## Generate a test data set
 randn ("seed", 25)  # for reproducibility
 r1 = randn (100, 2) * 0.75 + ones (100, 2);
 randn ("seed", 27)  # for reproducibility
 r2 = randn (100, 2) * 0.5 - ones (100, 2);
 randn ("seed", 29)  # for reproducibility
 r3 = randn (100, 2) * 0.75;
 Xtest = [r1; r2; r3];

 ## Classify the test data set using the existing clusters
 ## Find the nearest centroid from each test data point by using pdist2

 D = pdist2 (C, Xtest, "euclidean");
 [group, ~] = find (D == min (D));

 ## Plot the test data and label the test data using idx_test with gscatter

 gscatter (Xtest(:,1), Xtest(:,2), group, "bgm", "ooo");
 box on;
 legend ("Cluster 1", "Cluster 2", "Cluster 3", "Cluster Centroid", ...
         "Data classified to Cluster 1", "Data classified to Cluster 2", ...
         "Data classified to Cluster 3", "Location", "NorthWest");
 title ("Assign New Data to Existing Clusters");
***** test
 samples = 4;
 dims = 3;
 k = 2;
 [cls, c, d, z] = kmeans (rand (samples,dims), k, "start", rand (k,dims, 5),
                          "emptyAction", "singleton");
 assert (size (cls), [samples, 1]);
 assert (size (c), [k, dims]);
 assert (size (d), [k, 1]);
 assert (size (z), [samples, k]);
***** test
 samples = 4;
 dims = 3;
 k = 2;
 [cls, c, d, z] = kmeans (rand (samples,dims), [], "start", rand (k,dims, 5),
                          "emptyAction", "singleton");
 assert (size (cls), [samples, 1]);
 assert (size (c), [k, dims]);
 assert (size (d), [k, 1]);
 assert (size (z), [samples, k]);
***** test
 [cls, c] = kmeans ([1 0; 2 0], 2, "start", [8,0;0,8], "emptyaction", "drop");
 assert (cls, [1; 1]);
 assert (c, [1.5, 0; NA, NA]);
***** test
 kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 5,
         "emptyAction", "singleton");
***** test
 kmeans (rand (3,4), 2, "start", "sample", "emptyAction", "singleton");
***** test
 kmeans (rand (3,4), 2, "start", "plus", "emptyAction", "singleton");
***** test
 kmeans (rand (3,4), 2, "start", "cluster", "emptyAction", "singleton");
***** test
 kmeans (rand (3,4), 2, "start", "uniform", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "sqeuclidean", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "cityblock", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "cosine", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "correlation", "emptyAction", "singleton");
***** test
 kmeans (rand (4,3), 2, "distance", "hamming", "emptyAction", "singleton");
***** test
 kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "singleton");
***** error kmeans (rand (3,2), 4);
***** error kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "panic");
***** error kmeans (rand (4,3), 2, "start", rand (2,3, 5), "replicates", 1);
***** error kmeans (rand (4,3), 2, "start", rand (2,2));
***** error kmeans (rand (4,3), 2, "distance", "manhattan");
***** error kmeans (rand (3,4), 2, "start", "normal");
***** error kmeans (rand (4,3), 2, "replicates", i);
***** error kmeans (rand (4,3), 2, "replicates", -1);
***** error kmeans (rand (4,3), 2, "replicates", []);
***** error kmeans (rand (4,3), 2, "replicates", [1 2]);
***** error kmeans (rand (4,3), 2, "replicates", "one");
***** error kmeans (rand (4,3), 2, "MAXITER", i);
***** error kmeans (rand (4,3), 2, "MaxIter", -1);
***** error kmeans (rand (4,3), 2, "maxiter", []);
***** error kmeans (rand (4,3), 2, "maxiter", [1 2]);
***** error kmeans (rand (4,3), 2, "maxiter", "one");
***** error <empty cluster created> kmeans ([1 0; 1.1 0], 2, "start", eye(2), "emptyaction", "error");
31 tests, 31 passed, 0 known failure, 0 skipped
[inst/evalclusters.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/evalclusters.m
***** demo
 load fisheriris;
 eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6])
 plot (eva)
***** error evalclusters ()
***** error evalclusters ([1 1;0 1])
***** error evalclusters ([1 1;0 1], "kmeans")
***** error <evalclusters: X must be a numeric matrix.> ...
 evalclusters ("abc", "kmeans", "gap")
***** error <unknown clustering*> evalclusters ([1 1;0 1], "xxx", "gap")
***** error <invalid matrix*> evalclusters ([1 1;0 1], [1 2], "gap")
***** error <invalid argument*> evalclusters ([1 1;0 1], 1.2, "gap")
***** error <invalid criterion*> evalclusters ([1 1;0 1], [1; 2], 123)
***** error <unknown criterion*> evalclusters ([1 1;0 1], [1; 2], "xxx")
***** error <'KList' can be empty*> evalclusters ([1 1;0 1], "kmeans", "gap")
***** error <invalid parameter*> evalclusters ([1 1;0 1], [1; 2], "gap", 1)
***** error <invalid property*> evalclusters ([1 1;0 1], [1; 2], "gap", 1, 1)
***** error <unknown property*> evalclusters ([1 1;0 1], [1; 2], "gap", "xxx", 1)
***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [-1 0])
***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 .5])
***** error <'KList'*> evalclusters ([1 1;0 1], [1; 2], "gap", "KList", [1 1; 1 1])
***** error <unknown distance*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
                                        "distance", "a")
***** error <distance metric*> evalclusters ([1 1;0 1], [1; 2], "daviesbouldin", ...
                                       "distance", "a")
***** error <cluster prior*> evalclusters ([1 1;0 1], [1; 2], "gap", ...
                                     "clusterpriors", "equal")
***** error <invalid cluster prior*> evalclusters ([1 1;0 1], [1; 2], ...
                                         "silhouette", "clusterpriors", "xxx")
***** error <'clust' must be a clustering*> evalclusters ([1 1;0 1], [1; 2], "gap")
***** test
 load fisheriris;
 eva = evalclusters (meas, "kmeans", "calinskiharabasz", "KList", [1:6]);
 assert (isa (eva, "CalinskiHarabaszEvaluation"));
 assert (eva.NumObservations, 150);
 assert (eva.OptimalK, 3);
 assert (eva.InspectedK, [1 2 3 4 5 6]);
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/mahal.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/mahal.m
***** error mahal ()
***** error mahal (1, 2, 3)
***** error mahal ("A", "B")
***** error <must be numeric> mahal ([1, 2], ["A", "B"])
***** error mahal (ones (2, 2, 2))
***** error <must be 2-D matrices> mahal (ones (2, 2), ones (2, 2, 2))
***** error <same number of columns> mahal (ones (2, 2), ones (2, 3))
***** test
 X = [1 0; 0 1; 1 1; 0 0];
 assert (mahal (X, X), [1.5; 1.5; 1.5; 1.5], 10*eps)
 assert (mahal (X, X+1), [7.5; 7.5; 1.5; 13.5], 10*eps)
***** assert (mahal ([true; true], [false; true]), [0.5; 0.5], eps)
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/x2fx.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/x2fx.m
***** test
 X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
 D = x2fx(X,'quadratic');
 assert (D(1,:), [1, 1, 10, 10, 1, 100]);
 assert (D(2,:), [1, 2, 20, 40, 4, 400]);
***** test
 X = [1, 10; 2, 20; 3, 10; 4, 20; 5, 15; 6, 15];
 model = [0, 0; 1, 0; 0, 1; 1, 1; 2, 0];
 D = x2fx(X,model);
 assert (D(1,:), [1, 1, 10, 10, 1]);
 assert (D(2,:), [1, 2, 20, 40, 4]);
 assert (D(4,:), [1, 4, 20, 80, 16]);
***** test
 x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
 D = x2fx (x, 'linear');
 assert (D, [1, 1, 2, 3; 1, 2, 3, 4;, 1, 3, 4, 5]);
 D = x2fx (x, 'interaction');
 assert (D(1,:), [1, 1, 2, 3, 2, 3, 6]);
 assert (D(2,:), [1, 2, 3, 4, 6, 8, 12]);
 assert (D(3,:), [1, 3, 4, 5, 12, 15, 20]);
 D = x2fx (x, 'quadratic');
 assert (D(1,:), [1, 1, 2, 3, 2, 3, 6, 1, 4, 9]);
 assert (D(2,:), [1, 2, 3, 4, 6, 8, 12, 4, 9, 16]);
 assert (D(3,:), [1, 3, 4, 5, 12, 15, 20, 9, 16, 25]);
 D = x2fx (x, 'purequadratic');
 assert (D(1,:), [1, 1, 2, 3, 1, 4, 9]);
 assert (D(2,:), [1, 2, 3, 4, 4, 9, 16]);
 assert (D(3,:), [1, 3, 4, 5, 9, 16, 25]);
***** test
 x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
 D = x2fx (x, [0, 0, 1; 1, 0, 2]);
 assert (D, [3, 9; 4, 32; 5, 75]);
***** test
 x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
 D = x2fx (x, 'linear', [1, 3]);
 assert (D, [1, 1, 0, 2, 1, 0; 1, 0, 1, 3, 0, 1; 1, 0, 0, 4, 0, 0]);
***** test
 x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
 D = x2fx (x, 'quadratic', [1, 3]);
 assert (D(1,:), [1, 1, 0, 2, 1, 0, 2, 0, 1, 0, 0, 0, 2, 0, 4]);
 assert (D(2,:), [1, 0, 1, 3, 0, 1, 0, 3, 0, 0, 0, 1, 0, 3, 9]);
 assert (D(3,:), [1, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16]);
***** test
 x = [1, 2, 3; 2, 3, 4; 3, 4, 5];
 D = x2fx (x, 'cos');
 assert (D(1,:), [0.5403, -0.4161, -0.9900], 1e-4);
 assert (D(2,:), [-0.4161, -0.9900, -0.6536], 1e-4);
 assert (D(3,:), [-0.9900, -0.6536, 0.2837], 1e-4);
***** error <x2fx: category index exceeds number of columns in X.> ...
 x2fx ([1, 2, 3; 2, 3, 4], 'quadratic', [1, 4])
***** error <x2fx: unrecognized function 'cosine'.> ...
 D = x2fx ([1, 2, 3; 2, 3, 4; 3, 4, 5], 'cosine')
***** error <x2fx: wrong number of columns between X and MODEL.> ...
 x2fx ([1, 10; 2, 20; 3, 10], [0; 1]);
***** error <x2fx: wrong number of columns between X and MODEL.> ...
 x2fx ([1, 10, 15; 2, 20, 40; 3, 10, 25], [0, 0; 1, 0; 0, 1; 1, 1; 2, 0]);
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/glmval.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/glmval.m
***** demo
 x = [210, 230, 250, 270, 290, 310, 330, 350, 370, 390, 410, 430]';
 n = [48, 42, 31, 34, 31, 21, 23, 23, 21, 16, 17, 21]';
 y = [1, 2, 0, 3, 8, 8, 14, 17, 19, 15, 17, 21]';
 b = glmfit (x, [y n], "binomial", "Link", "probit");
 yfit = glmval (b, x, "probit", "Size", n);
 plot (x, y./n, 'o', x, yfit ./ n, '-')
***** error <glmval: too few input arguments.> glmval ()
***** error <glmval: too few input arguments.> glmval (1)
***** error <glmval: too few input arguments.> glmval (1, 2)
***** error <glmval: B must be a numeric vector of coefficient estimates.> ...
 glmval ("asd", [1; 1; 1], 'probit')
***** error <glmval: B must be a numeric vector of coefficient estimates.> ...
 glmval ([], [1; 1; 1], 'probit')
***** error <glmval: X must be a numeric matrix.> ...
 glmval ([0.1; 0.3; 0.4], [], 'probit')
***** error <glmval: X must be a numeric matrix.> ...
 glmval ([0.1; 0.3; 0.4], "asd", 'probit')
***** error <glmval: structure with custom link functions must be a scalar.> ...
 glmval (rand (3,1), rand (5,2), struct ("Link", {1, 2}))
***** error <glmval: structure with custom link functions requires the fields 'Link', 'Derivative', and 'Inverse'.> ...
 glmval (rand (3,1), rand (5,2), struct ("Link", "norminv"))
***** error <glmval: bad 'Link' function in custom link function structure.> ...
 glmval (rand (3,1), rand (5,2), struct ("Link", "some", "Derivative", @(x)x, "Inverse", "normcdf"))
***** error <glmval: bad 'Link' function in custom link function structure.> ...
 glmval (rand (3,1), rand (5,2), struct ("Link", 1, "Derivative", @(x)x, "Inverse", "normcdf"))
***** error <glmval: custom 'Link' function must return an output of the same size as input.> ...
 glmval (rand (3,1), rand (5,2), struct ("Link", @(x) [x, x], "Derivative", @(x)x, "Inverse", "normcdf"))
***** error <glmval: invalid custom 'Link' function.> ...
 glmval (rand (3,1), rand (5,2), struct ("Link", "what", "Derivative", @(x)x, "Inverse", "normcdf"))
***** error <glmval: bad 'Derivative' function in custom link function structure.> ...
 glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "some", "Inverse", "normcdf"))
***** error <glmval: bad 'Derivative' function in custom link function structure.> ...
 glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", 1, "Inverse", "normcdf"))
***** error <glmval: custom 'Derivative' function must return an output of the same size as input.> ...
 glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", @(x) [x, x], "Inverse", "normcdf"))
***** error <glmval: invalid custom 'Derivative' function.> ...
 glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "what", "Inverse", "normcdf"))
***** error <glmval: bad 'Inverse' function in custom link function structure.> ...
 glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "some"))
***** error <glmval: bad 'Inverse' function in custom link function structure.> ...
 glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", 1))
***** error <glmval: custom 'Inverse' function must return an output of the same size as input.> ...
 glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", @(x) [x, x]))
***** error <glmval: invalid custom 'Inverse' function.> ...
 glmval (rand (3,1), rand (5,2), struct ("Link", @(x)x, "Derivative", "normcdf", "Inverse", "what"))
***** error <glmval: cell array with custom link functions must have three elements.> ...
 glmval (rand (3,1), rand (5,2), {'log'})
***** error <glmval: cell array with custom link functions must have three elements.> ...
 glmval (rand (3,1), rand (5,2), {'log', 'hijy'})
***** error <glmval: cell array with custom link functions must have three elements.> ...
 glmval (rand (3,1), rand (5,2), {1, 2, 3, 4})
***** error <glmval: bad 'Link' function in custom link function cell array.> ...
 glmval (rand (3,1), rand (5,2), {"log", "dfv", "dfgvd"})
***** error <glmval: custom 'Link' function must return an output of the same size as input.> ...
 glmval (rand (3,1), rand (5,2), {@(x) [x, x], "dfv", "dfgvd"})
***** error <glmval: invalid custom 'Link' function.> ...
 glmval (rand (3,1), rand (5,2), {@(x) what (x), "dfv", "dfgvd"})
***** error <glmval: bad 'Derivative' function in custom link function cell array.> ...
 glmval (rand (3,1), rand (5,2), {@(x) x, "dfv", "dfgvd"})
***** error <glmval: custom 'Derivative' function must return an output of the same size as input.> ...
 glmval (rand (3,1), rand (5,2), {@(x) x, @(x) [x, x], "dfgvd"})
***** error <glmval: invalid custom 'Derivative' function.> ...
 glmval (rand (3,1), rand (5,2), {@(x) x, @(x) what (x), "dfgvd"})
***** error <glmval: bad 'Inverse' function in custom link function cell array.> ...
 glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, "dfgvd"})
***** error <glmval: custom 'Inverse' function must return an output of the same size as input.> ...
 glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, @(x) [x, x]})
***** error <glmval: invalid custom 'Inverse' function.> ...
 glmval (rand (3,1), rand (5,2), {@(x) x, @(x) x, @(x) what (x)})
***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
 glmval (rand (3,1), rand (5,2), NaN)
***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
 glmval (rand (3,1), rand (5,2), [1, 2])
***** error <glmval: numeric input for custom link function must be a finite real scalar value.> ...
 glmval (rand (3,1), rand (5,2), [1i])
***** error <glmval: canonical link function name must be a character vector.> ...
 glmval (rand (3,1), rand (5,2), ["log"; "log1"])
***** error <glmval: canonical link function 'somelinkfunction' is not supported.> ...
 glmval (rand (3,1), rand (5,2), 'somelinkfunction')
***** error <glmval: invalid value for custom link function.> ...
 glmval (rand (3,1), rand (5,2), true)
***** error <glmval: invalid 'stats' structure.> ...
 glmval (rand (3,1), rand (5,2), 'probit', struct ("s", 1))
***** error <glmval: Name-Value arguments must be in pairs.> ...
 glmval (rand (3,1), rand (5,2), 'probit', 'confidence')
***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
 glmval (rand (3,1), rand (5,2), 'probit', 'confidence', 0)
***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
 glmval (rand (3,1), rand (5,2), 'probit', 'confidence', 1.2)
***** error <glmval: 'Confidence' must be a scalar between 0 and 1.> ...
 glmval (rand (3,1), rand (5,2), 'probit', 'confidence', [0.9, 0.95])
***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
 glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', 1)
***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
 glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', 'o')
***** error <glmval: 'Constant' should be either 'on' or 'off'.> ...
 glmval (rand (3, 1), rand (5, 2), 'probit', 'constant', true)
***** error <glmval: 'Offset' must be a numeric vector of the same length as the rows in X.> ...
 glmval (rand (3, 1), rand (5, 2), 'probit', 'offset', [1; 2; 3; 4])
***** error <glmval: 'Offset' must be a numeric vector of the same length as the rows in X.> ...
 glmval (rand (3, 1), rand (5, 2), 'probit', 'offset', 'asdfg')
***** error <glmval: 'simultaneous' must be a boolean scalar.> ...
 glmval (rand (3, 1), rand (5, 2), 'probit', 'simultaneous', 'asdfg')
***** error <glmval: 'simultaneous' must be a boolean scalar.> ...
 glmval (rand (3, 1), rand (5, 2), 'probit', 'simultaneous', [true, false])
***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
 glmval (rand (3, 1), rand (5, 2), 'probit', 'size', "asd")
***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
 glmval (rand (3, 1), rand (5, 2), 'probit', 'size', [2, 3, 4])
***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
 glmval (rand (3, 1), rand (5, 2), 'probit', 'size', [2; 3; 4])
***** error <glmval: 'size' must be a scalar or a vector with one value for each row of X.> ...
 glmval (rand (3, 1), rand (5, 2), 'probit', 'size', ones (3))
***** error <glmval: unknown parameter name.> ...
 glmval (rand (3, 1), rand (5, 2), 'probit', 'someparam', 4)
***** error <glmval: cannot compute confidence intervals without STATS structure.> ...
 [y,lo,hi] = glmval (rand (3, 1), rand (5, 2), 'probit')
57 tests, 57 passed, 0 known failure, 0 skipped
[inst/barttest.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/barttest.m
***** error<barttest: invalid number of input arguments.> barttest ()
***** error<barttest: NaN values in input are not allowed.> barttest ([2,NaN;3,4])
***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), "alpha")
***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 0)
***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), 1.2)
***** error<barttest: wrong value for alpha.> barttest (ones (30, 4), [0.2, 0.05])
***** error<barttest: not enough data in X.> barttest (ones (30, 1))
***** error<barttest: not enough data in X.> barttest (ones (30, 1), 0.05)
***** test
 x = [2, 3, 4, 5, 6, 7, 8, 9; 1, 2, 3, 4, 5, 6, 7, 8]';
 [ndim, pval, chisq] = barttest (x);
 assert (ndim, 2);
 assert (pval, 0);
 ## assert (chisq, 512.0558, 1e-4); Result differs between octave 6 and 7 ?
***** test
 x = [0.53767,  0.62702,   -0.10224,   -0.25485,   1.4193,   1.5237  ; ...
      1.8339,   1.6452,    -0.24145,   -0.23444,   0.29158,  0.1634  ; ...
     -2.2588,  -2.1351,     0.31286,    0.39396,   0.19781,  0.20995 ; ...
      0.86217,  1.0835,     0.31286,    0.46499,   1.5877,   1.495   ; ...
      0.31877,  0.38454,   -0.86488,   -0.63839,  -0.80447, -0.7536  ; ...
     -1.3077,  -1.1487,    -0.030051,  -0.017629,  0.69662,  0.60497 ; ...
     -0.43359, -0.32672,   -0.16488,   -0.37364,   0.83509,  0.89586 ; ...
      0.34262,  0.29639,    0.62771,    0.51672,  -0.24372, -0.13698 ; ...
      3.5784,   3.5841,     1.0933,     0.93258,   0.21567,  0.455   ; ...
      2.7694,   2.6307,     1.1093,     1.4298,   -1.1658,  -1.1816  ; ...
     -1.3499,  -1.2111,    -0.86365,   -0.94186,  -1.148,   -1.4381  ; ...
      3.0349,   2.8428,     0.077359,   0.18211,   0.10487, -0.014613; ...
      0.7254,   0.56737,   -1.2141,    -1.2291,    0.72225,  0.90612 ; ...
     -0.063055,-0.17662,   -1.1135,    -0.97701,   2.5855,   2.4084  ; ...
      0.71474,  0.29225,   -0.0068493, -0.11468,  -0.66689, -0.52466 ; ...
     -0.20497, -7.8874e-06, 1.5326,     1.3195,    0.18733,  0.20296 ; ...
     -0.12414, -0.077029,  -0.76967,   -0.96262,  -0.082494, 0.121   ; ...
      1.4897,   1.3683,     0.37138,    0.43653,  -1.933,   -2.1903  ; ...
      1.409,    1.5882,    -0.22558,   -0.24835,  -0.43897, -0.46247 ; ...
      1.4172,   1.1616,     1.1174,     1.0785,   -1.7947,  -1.9471  ];
 [ndim, pval, chisq] = barttest (x);
 assert (ndim, 3);
 assert (pval, [0; 0; 0; 0.52063; 0.34314], 1e-5);
 chisq_out = [251.6802; 210.2670; 153.1773; 4.2026; 2.1392];
 assert (chisq, chisq_out, 1e-4);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/fitcknn.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/fitcknn.m
***** demo
 ## Train a k-nearest neighbor classifier for k = 10
 ## and plot the decision boundaries.

 load fisheriris
 idx = ! strcmp (species, "setosa");
 X = meas(idx,3:4);
 Y = cast (strcmpi (species(idx), "virginica"), "double");
 obj = fitcknn (X, Y, "Standardize", 1, "NumNeighbors", 10, "NSMethod", "exhaustive")
 x1 = [min(X(:,1)):0.03:max(X(:,1))];
 x2 = [min(X(:,2)):0.02:max(X(:,2))];
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];
 pred = predict (obj, XGrid);
 gidx = logical (pred);

 figure
 scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
 hold on
 scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
 plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
 xlabel ("Petal length (cm)");
 ylabel ("Petal width (cm)");
 title ("5-Nearest Neighbor Classifier Decision Boundary");
 legend ({"Versicolor Region", "Virginica Region", ...
         "Sampled Versicolor", "Sampled Virginica"}, ...
         "location", "northwest")
 axis tight
 hold off
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = fitcknn (x, y, "NumNeighbors" ,k);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = ones (4, 11);
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = fitcknn (x, y, "NumNeighbors" ,k);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = fitcknn (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = fitcknn (x, y, "NumNeighbors" ,k, "Distance", "hamming");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 weights = ones (4,1);
 a = fitcknn (x, y, "Standardize", 1);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.Standardize}, {true})
 assert ({a.Sigma}, {std(x, [], 1)})
 assert ({a.Mu}, {[3.75, 4.25, 4.75]})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 weights = ones (4,1);
 a = fitcknn (x, y, "Standardize", false);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.Standardize}, {false})
 assert ({a.Sigma}, {[]})
 assert ({a.Mu}, {[]})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 s = ones (1, 3);
 a = fitcknn (x, y, "Scale" , s, "Distance", "seuclidean");
 assert (class (a), "ClassificationKNN");
 assert ({a.DistParameter}, {s})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski");
 assert (class (a), "ClassificationKNN");
 assert (a.DistParameter, 5)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "Exponent" , 5, "Distance", "minkowski", ...
                    "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert (a.DistParameter, 5)
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "BucketSize" , 20, "distance", "mahalanobis");
 assert (class (a), "ClassificationKNN");
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
 assert ({a.BucketSize}, {20})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "IncludeTies", true);
 assert (class (a), "ClassificationKNN");
 assert (a.IncludeTies, true);
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y);
 assert (class (a), "ClassificationKNN");
 assert (a.IncludeTies, false);
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y);
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, [0.5; 0.5])
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 prior = [0.5; 0.5];
 a = fitcknn (x, y, "Prior", "empirical");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "a"; "b"];
 prior = [0.75; 0.25];
 a = fitcknn (x, y, "Prior", "empirical");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "a"; "b"];
 prior = [0.5; 0.5];
 a = fitcknn (x, y, "Prior", "uniform");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 cost = eye (2);
 a = fitcknn (x, y, "Cost", cost);
 assert (class (a), "ClassificationKNN")
 assert (a.Cost, [1, 0; 0, 1])
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 cost = eye (2);
 a = fitcknn (x, y, "Cost", cost, "Distance", "hamming" );
 assert (class (a), "ClassificationKNN")
 assert (a.Cost, [1, 0; 0, 1])
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 status = warning;
 warning ('off');
 rand ("seed", 23);
 a = fitcknn (x, y, "NSMethod", "exhaustive", "CrossVal", "on");
 warning (status);
 assert (class (a), "ClassificationPartitionedModel");
 assert ({a.X, a.Y, a.Trained{1}.NumNeighbors}, {x, y, 1})
 assert (a.ModelParameters.NSMethod, "exhaustive")
 assert (a.ModelParameters.Distance, "euclidean")
 assert ({a.Trained{1}.BucketSize}, {50})
***** error<fitcknn: too few arguments.> fitcknn ()
***** error<fitcknn: too few arguments.> fitcknn (ones (4,1))
***** error<fitcknn: Name-Value arguments must be in pairs.>
 fitcknn (ones (4,2), ones (4, 1), "K")
***** error<fitcknn: number of rows in X and Y must be equal.>
 fitcknn (ones (4,2), ones (3, 1))
***** error<fitcknn: number of rows in X and Y must be equal.>
 fitcknn (ones (4,2), ones (3, 1), "K", 2)
***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
 fitcknn (ones (4,2), ones (4, 1), "CrossVal", 2)
***** error <fitcknn: 'CrossVal' must be either 'off' or 'on'.>
 fitcknn (ones (4,2), ones (4, 1), "CrossVal", 'a')
***** error <fitcknn: You can use only one cross-validation name-value pair argument> ...
 fitcknn (ones (4,2), ones (4, 1), "KFold", 10, "Holdout", 0.3)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/fillmissing.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/fillmissing.m
***** assert (fillmissing ([1, 2, 3], "constant", 99), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], "constant", 99), [1, 2, 99])
***** assert (fillmissing ([NaN, 2, NaN], "constant", 99), [99, 2, 99])
***** assert (fillmissing ([1, 2, 3]', "constant", 99), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN]', "constant", 99), [1, 2, 99]')
***** assert (fillmissing ([1, 2, 3; 4, 5, 6], "constant", 99), [1, 2, 3; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "constant", 99), [1, 2, 99; 4, 99, 6])
***** assert (fillmissing ([NaN, 2, NaN; 4, NaN, 6], "constant", [97, 98, 99]), [97, 2, 99; 4, 98, 6])
***** test
 x = cat (3, [1, 2, NaN; 4, NaN, 6], [NaN, 2, 3; 4, 5, NaN]);
 y = cat (3, [1, 2, 99; 4, 99, 6], [99, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", 99), y);
 y = cat (3, [1, 2, 96; 4, 95, 6], [97, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", [94:99]), y);
 assert (fillmissing (x, "constant", [94:99]'), y);
 assert (fillmissing (x, "constant", permute ([94:99], [1 3 2])), y);
 assert (fillmissing (x, "constant", [94, 96, 98; 95, 97, 99]), y);
 assert (fillmissing (x, "constant", [94:99], 1), y);
 y = cat (3, [1, 2, 96; 4, 97, 6], [98, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", [96:99], 2), y);
 y = cat (3, [1, 2, 98; 4, 97, 6], [94, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", [94:99], 3), y);
 y = cat (3, [1, 2, 92; 4, 91, 6], [94, 2, 3; 4, 5, 99]);
 assert (fillmissing (x, "constant", [88:99], 99), y);
***** test
 x = reshape ([1:24], 4, 3, 2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 y = x;
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
 assert (fillmissing (x, "constant", [94:99], 1), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
 assert (fillmissing (x, "constant", [92:99], 2), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
 assert (fillmissing (x, "constant", [88:99], 3), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
 assert (fillmissing (x, "constant", [76:99], 99), y);
***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", 88), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", 88), [1, 99, 3])
***** assert (fillmissing ([1, 2, NaN], "constant", 99, "endvalues", 88), [1, 2, 88])
***** assert (fillmissing ([NaN, 2, 3], "constant", 99, "endvalues", 88), [88, 2, 3])
***** assert (fillmissing ([NaN, NaN, 3], "constant", 99, "endvalues", 88), [88, 88, 3])
***** assert (fillmissing ([1, NaN, NaN], "constant", 99, "endvalues", 88), [1, 88, 88])
***** assert (fillmissing ([NaN, 2, NaN], "constant", 99, "endvalues", 88), [88, 2, 88])
***** assert (fillmissing ([NaN, 2, NaN]', "constant", 99, "endvalues", 88), [88, 2, 88]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 3, 99, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [1, 99, 99, 99, 5])
***** assert (fillmissing ([NaN, NaN, NaN, NaN, 5], "constant", 99, "endvalues", 88), [88, 88, 88, 88, 5])
***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, "endvalues", 88), [1, 99, 3, 4, 88])
***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", 88), [1, 88, 3, 4, 88])
***** assert (fillmissing ([1, NaN, 3, 4, NaN], "constant", 99, 1, "endvalues", "extrap"), [1, 99, 3, 4, 99])
***** test
 x = reshape ([1:24], 3, 4, 2);
 y = x;
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 88;
 y([8]) = 99;
 assert (fillmissing (x, "constant", 99, "endvalues", 88), y);
 assert (fillmissing (x, "constant", 99, 1, "endvalues", 88), y);
 y = x;
 y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 88;
 y([6, 18, 20, 21]) = 99;
 assert (fillmissing (x, "constant", 99, 2, "endvalues", 88), y);
 y(y == 99) = 88;
 assert (fillmissing (x, "constant", 99, 3, "endvalues", 88), y);
 assert (fillmissing (x, "constant", 99, 4, "endvalues", 88), y);
 assert (fillmissing (x, "constant", 99, 99, "endvalues", 88), y);
 y([8]) = 94;
 assert (fillmissing (x, "constant", [92:99], 1, "endvalues", 88), y);
 y([6, 8, 18, 20, 21]) = [96, 88, 99, 98, 99];
 assert (fillmissing (x, "constant", [94:99], 2, "endvalues", 88), y);
 y = x;
 y(isnan (y)) = 88;
 assert (fillmissing (x, "constant", [88:99], 3, "endvalues", 88), y);
 y = x;
 y(isnan (y)) = [82, 82, 83, 83, 94, 85, 86, 87, 87, 88, 88, 88, 89];
 assert (fillmissing (x, "constant", [92:99], 1, "endvalues", [82:89]), y);
 y = x;
 y(isnan (y)) = [84, 85, 85, 96, 85, 84, 87, 87, 99, 87, 98, 99, 87];
 assert (fillmissing (x, "constant", [94:99], 2, "endvalues", [84:89]), y);
 y = x;
 y(isnan (y)) = [68, 69, 72, 73, 75, 77, 68, 71, 73, 74, 75, 76, 77];
 assert (fillmissing (x, "constant", [88:99], 3, "endvalues", [68:79]), y);
 assert (fillmissing (x, "constant", [88:93; 94:99]', 3, "endvalues", [68:73; 74:79]'), y)
***** test
 x = reshape ([1:24],4,3,2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 y = x;
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [94, 95, 95, 96, 96, 97, 97, 98, 99, 99];
 assert (fillmissing (x, "constant", [94:99], 1), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [92, 93, 94, 92, 95, 97, 99, 98, 97, 98];
 assert (fillmissing (x, "constant", [92:99], 2), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [88, 93, 94, 96, 99, 89, 91, 94, 97, 98];
 assert (fillmissing (x, "constant", [88:99], 3), y);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [76, 81, 82, 84, 87, 89, 91, 94, 97, 98];
 assert (fillmissing (x, "constant", [76:99], 99), y);
***** assert (fillmissing ([1, 2, 3], "previous"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "next"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "previous"), [1, 2, 3]')
***** assert (fillmissing ([1, 2, 3]', "next"), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "previous"), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "next"), [1, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], "previous"), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "next"), [2, 2, NaN])
***** assert (fillmissing ([1, NaN, 3], "previous"), [1, 1, 3])
***** assert (fillmissing ([1, NaN, 3], "next"), [1, 3, 3])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 1), [1, 2, NaN; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 2), [1, 2, 2; 4, 4, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "previous", 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 1), [1, 2, 6; 4, NaN, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 2), [1, 2, NaN; 4, 6, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "next", 3), [1, 2, NaN; 4, NaN, 6])
***** test
 x = reshape ([1:24], 4, 3, 2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 y = x;
 y([1, 6, 7, 9, 14, 19, 22, 23]) = [2, 8, 8, 10, 15, 20, 24, 24];
 assert (fillmissing (x, "next", 1), y);
 y = x;
 y([1, 6, 7, 14, 16]) = [5, 10, 11, 18, 20];
 assert (fillmissing (x, "next", 2), y);
 y = x;
 y([1, 6, 9, 12]) = [13, 18, 21, 24];
 assert (fillmissing (x, "next", 3), y);
 assert (fillmissing (x, "next", 99), x);
 y = x;
 y([6, 7, 12, 14, 16, 19, 22, 23]) = [5, 5, 11, 13, 15, 18, 21, 21];
 assert (fillmissing (x, "previous", 1), y);
 y = x;
 y([6, 7, 9, 12, 19, 22, 23]) = [2, 3, 5, 8, 15, 18, 15];
 assert (fillmissing (x, "previous", 2), y);
 y = x;
 y([14, 16, 22, 23]) = [2, 4, 10, 11];
 assert (fillmissing (x, "previous", 3), y);
 assert (fillmissing (x, "previous", 99), x);
***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "previous"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "next"), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "previous"), [1, 0, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "next"), [1, 0, 3])
***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "previous"), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "next"), [1, 2, NaN])
***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "previous"), [1, 1, 1])
***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "next"), [1, NaN, NaN])
***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "previous"), [NaN, 2, 3])
***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "next"), [2, 2, 3])
***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "previous"), [NaN, NaN, 3])
***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "next"), [3, 3, 3])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "previous"), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "next"), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "next"), [2, 2, 0, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "previous"), [NaN, 2, 0, 4, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "next"), [2, 2, 0, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "previous"), [NaN, 2, NaN, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "next"), [NaN, 2, NaN, 4, NaN])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([5, 6, 8, 18]) = [4, 4, 0, 17];
 assert (fillmissing (x, "constant", 0, "endvalues", "previous"), y);
 assert (fillmissing (x, "constant", 0, 1, "endvalues", "previous"), y);
 y = x;
 y([6, 10, 18, 20, 21]) = [0, 7, 0, 0, 0];
 assert (fillmissing (x, "constant", 0, 2, "endvalues", "previous"), y);
 y = x;
 y([16, 19, 21]) = [4, 7, 9];
 assert (fillmissing (x, "constant", 0, 3, "endvalues", "previous"), y);
 assert (fillmissing (x, "constant", 0, 4, "endvalues", "previous"), x);
 assert (fillmissing (x, "constant", 0, 99, "endvalues", "previous"), x);
 y = x;
 y([1, 2, 8, 10, 13, 16, 22]) = [3, 3, 0, 11, 14, 17, 23];
 assert (fillmissing (x, "constant", 0, "endvalues", "next"), y);
 assert (fillmissing (x, "constant", 0, 1, "endvalues", "next"), y);
 y = x;
 y([1, 2, 5, 6, 8, 18, 20, 21]) = [4, 11, 11, 0, 11, 0, 0, 0];
 assert (fillmissing (x, "constant", 0, 2, "endvalues", "next"), y);
 y = x;
 y([2, 5]) = [14, 17];
 assert (fillmissing (x, "constant", 0, 3, "endvalues", "next"), y);
 assert (fillmissing (x, "constant", 0, 4, "endvalues", "next"), x);
 assert (fillmissing (x, "constant", 0, 99, "endvalues", "next"), x);
***** assert (fillmissing ([1, 2, 3], "nearest"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "nearest"), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "nearest"), [1, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "nearest"), [2, 2, 2])
***** assert (fillmissing ([1, NaN, 3], "nearest"), [1, 3, 3])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 1), [1, 2, 6; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 2), [1, 2, 2; 4, 6, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "nearest", 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest"), [1, 3, 3, 5, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0, 1, 2, 3, 4]), [1, 3, 3, 5, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "nearest", "samplepoints", [0.5, 1, 2, 3, 5]), [1, 1, 3, 3, 5])
***** test
 x = reshape ([1:24], 4, 3, 2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 y = x;
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [2, 5, 8, 10, 11, 15, 15, 20, 21, 24];
 assert (fillmissing (x, "nearest", 1), y);
 y = x;
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = [5, 10, 11, 5, 8, 18, 20, 15, 18, 15];
 assert (fillmissing (x, "nearest", 2), y);
 y = x;
 y([1, 6, 9, 12, 14, 16, 22, 23]) = [13, 18, 21, 24, 2, 4, 10, 11];
 assert (fillmissing (x, "nearest", 3), y);
 assert (fillmissing (x, "nearest", 99), x);
***** assert (fillmissing ([1, 2, 3], "constant", 0, "endvalues", "nearest"), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "endvalues", "nearest"), [1 0 3])
***** assert (fillmissing ([1, 2, NaN], "constant", 0, "endvalues", "nearest"), [1, 2, 2])
***** assert (fillmissing ([1, NaN, NaN], "constant", 0, "endvalues", "nearest"), [1, 1, 1])
***** assert (fillmissing ([NaN, 2, 3], "constant", 0, "endvalues", "nearest"), [2, 2, 3])
***** assert (fillmissing ([NaN, NaN, 3], "constant", 0, "endvalues", "nearest"), [3, 3, 3])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "endvalues", "nearest"), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, "endvalues", "nearest"), [2, 2, 0, 4, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 1, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 2, "endvalues", "nearest"), [2, 2, 0, 4, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 0, 3, "endvalues", "nearest"), [NaN, 2, NaN, 4, NaN])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 0, 11, 14, 17, 17, 23];
 assert (fillmissing (x, "constant", 0, "endvalues", "nearest"), y);
 assert (fillmissing (x, "constant", 0, 1, "endvalues", "nearest"), y);
 y = x;
 y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 0, 11, 7, 0, 0, 0];
 assert (fillmissing (x, "constant", 0, 2, "endvalues", "nearest"), y);
 y = x;
 y([2, 5, 16, 19, 21]) = [14, 17, 4, 7, 9];
 assert (fillmissing (x, "constant", 0, 3, "endvalues", "nearest"), y);
 assert (fillmissing (x, "constant", 0, 99, "endvalues", "nearest"), x);
***** assert (fillmissing ([1, 2, 3], "linear"), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "linear"), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "linear"), [1, 2, 3])
***** assert (fillmissing ([NaN, 2, NaN], "linear"), [NaN, 2, NaN])
***** assert (fillmissing ([1, NaN, 3], "linear"), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 1), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 2), [1, 2, 3; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "linear", 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear"), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1, 2, 3, 4]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "linear", "samplepoints", [0, 1.5, 2, 5, 14]), [1, 2.5, 3, 3.5, 5], eps)
***** test
 x = reshape ([1:24], 4, 3, 2);
 x([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 assert (fillmissing (x, "linear", 1), reshape ([1:24], 4, 3, 2));
 y = reshape ([1:24], 4, 3, 2);
 y([1, 9, 14, 19, 22, 23]) = NaN;
 assert (fillmissing (x, "linear", 2), y);
 y = reshape ([1:24], 4, 3, 2);
 y([1, 6, 7, 9, 12, 14, 16, 19, 22, 23]) = NaN;
 assert (fillmissing (x, "linear", 3), y);
 assert (fillmissing (x, "linear", 99), x);
***** assert (fillmissing ([1, 2, 3], "linear", "endvalues", 0), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "linear", "endvalues", 0), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], "linear", "endvalues", 0), [1, 2, 0])
***** assert (fillmissing ([1, NaN, NaN], "linear", "endvalues", 0), [1, 0, 0])
***** assert (fillmissing ([NaN, 2, 3], "linear", "endvalues", 0), [0, 2, 3])
***** assert (fillmissing ([NaN, NaN, 3], "linear", "endvalues", 0), [0, 0, 3])
***** assert (fillmissing ([NaN, NaN, NaN], "linear", "endvalues", 0), [0, 0, 0])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", "endvalues", 0), [0, 2, 3, 4, 0])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 1, "endvalues", 0), [0, 2, 0, 4, 0])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 2, "endvalues", 0), [0, 2, 3, 4, 0])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "linear", 3, "endvalues", 0), [0, 2, 0, 4, 0])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 0;
 y(8) = 8;
 assert (fillmissing (x, "linear", "endvalues", 0), y);
 assert (fillmissing (x, "linear", 1, "endvalues", 0), y);
 y = x;
 y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 0;
 y([6, 18, 20, 21]) = [6, 18, 20, 21];
 assert (fillmissing (x, "linear", 2, "endvalues", 0), y);
 y = x;
 y(isnan(y)) = 0;
 assert (fillmissing (x, "linear", 3, "endvalues", 0), y);
 assert (fillmissing (x, "linear", 99, "endvalues", 0), y);
***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "linear"), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "linear"), [1, 99, 3])
***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "linear"), [1, 99, 3, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear"), [1, 2, 99, 4, 5])
***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "linear"), [NaN, 2, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "linear", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 6, 10, 18, 20, 21]) = [2.5, 5, 8.5, 17.25, 21, 21.75];
 assert (fillmissing (x, "linear", 2, "samplepoints", [2 4 8 10]), y, eps);
 y([1, 6, 10, 18, 20, 21]) = [2.5, 4.5, 8.5, 17.25, 21.5, 21.75];
 assert (fillmissing (x, "spline", 2, "samplepoints", [2, 4, 8, 10]), y, eps);
 y([1, 6, 10, 18, 20, 21]) = [2.5, 4.559386973180077, 8.5, 17.25, 21.440613026819925, 21.75];
 assert (fillmissing (x, "pchip", 2, "samplepoints", [2, 4, 8, 10]), y, 10*eps);
***** test <60965>
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 6, 10, 18, 20, 21]) = [2.5, 4.609523809523809, 8.5, 17.25, 21.390476190476186, 21.75];
 assert (fillmissing (x, "makima", 2, "samplepoints", [2, 4, 8, 10]), y, 1e-14);
***** assert (fillmissing ([1, 2, 3], "constant", 99, "endvalues", "spline"), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 99, "endvalues", "spline"), [1, 99, 3])
***** assert (fillmissing ([1, NaN, 3, NaN], "constant", 99, "endvalues", "spline"), [1, 99, 3, 4])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline"), [1, 2, 99, 4, 5])
***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 99, "endvalues", "spline"), [NaN, 2, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 99, 4, 5])
***** assert (fillmissing ([NaN, 2, NaN, 4, NaN], "constant", 99, "endvalues", "spline", "samplepoints", [0, 2, 3, 4, 10]), [0, 2, 99, 4, 10])
***** assert (fillmissing ([1, 2, 3], "movmean", 1), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], "movmean", 1), [1, 2, NaN])
***** assert (fillmissing ([1, 2, 3], "movmean", 2), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "movmean", [1, 0]), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "movmean", 2), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "movmean", 2), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "movmean", [1, 0]'), [1, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmean", 2), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmean", [1, 0]), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1]), [2, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], "movmean", [0, 1.1]), [2, 2, NaN])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", [3, 0]), [1, 1, 3, 2, 5])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 1), [1, 2, 6; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 2), [1, 2, 2; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmean", 3, 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99), [1, 3, 3, 3, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 1), [1, NaN, 3, NaN, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 1), [1, 3, 3, 3, 5]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmean", 99, 2), [1, 3, 3, 3, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmean", 99, 2), [1, NaN, 3, NaN, 5]')
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1 2, 3, 4, 4.5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", 3, "samplepoints", [1.5, 2, 3, 4, 4.5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1.5, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 4.5]), [1, 1, 5, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmean", [1.5, 1.5], "samplepoints", [1.5, 2 3, 4, 4.5]), [1, 1, 3, 5, 5])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
 assert (fillmissing (x, "movmean", 3), y);
 assert (fillmissing (x, "movmean", [1, 1]), y);
 assert (fillmissing (x, "movmean", 3, "endvalues", "extrap"), y);
 assert (fillmissing (x, "movmean", 3, "samplepoints", [1, 2, 3]), y);
 y = x;
 y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
 assert (fillmissing (x, "movmean", 3, 2), y);
 assert (fillmissing (x, "movmean", [1, 1], 2), y);
 assert (fillmissing (x, "movmean", 3, 2, "endvalues", "extrap"), y);
 assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
 y([1, 18]) = NaN;
 y(6) = 9;
 assert (fillmissing (x, "movmean", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
 y = x;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
 y(8) = 8;
 assert (fillmissing (x, "movmean", 3, "endvalues", 99), y);
 y = x;
 y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
 y([6, 18, 20, 21]) = [6, 15, 20, 24];
 assert (fillmissing (x, "movmean", 3, 2, "endvalues", 99), y);
***** assert (fillmissing ([1, 2, 3], "movmedian", 1), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], "movmedian", 1), [1, 2, NaN])
***** assert (fillmissing ([1, 2, 3], "movmedian", 2), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "movmedian", [1, 0]), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', "movmedian", 2), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], "movmedian", 2), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]), [1, 2, 2])
***** assert (fillmissing ([1, 2, NaN], "movmedian", [1, 0]'), [1, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmedian", 2), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [1, 0]), [NaN, 2, 2])
***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1]), [2, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], "movmedian", [0, 1.1]), [2, 2, NaN])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", [3, 0]), [1, 1, 3, 2, 5])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 1), [1, 2, 6; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 2), [1, 2, 2; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], "movmedian", 3, 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99), [1, 3, 3, 3, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 1), [1, NaN, 3, NaN, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 1), [1, 3, 3, 3, 5]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "movmedian", 99, 2), [1, 3, 3, 3, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "movmedian", 99, 2), [1, NaN, 3, NaN, 5]')
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 4.0001, "samplepoints", [1, 2, 3, 4, 5]), [1, 1, 3, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1 2 3 4 4.5]), [1, 1, NaN, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", 3, "samplepoints", [1.5 2 3 4 4.5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1.5 2 3 4 5]), [1, 1, 1, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1 2 3 4 4.5]), [1, 1, 5, 5, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], "movmedian", [1.5, 1.5], "samplepoints", [1.5 2 3 4 4.5]), [1, 1, 3, 5, 5])
***** test
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([2, 5, 8, 10, 13, 16, 18, 22]) = [3, 4, 8, 11, 14, 17, 17, 23];
 assert (fillmissing (x, "movmedian", 3), y);
 assert (fillmissing (x, "movmedian", [1, 1]), y);
 assert (fillmissing (x, "movmedian", 3, "endvalues", "extrap"), y);
 assert (fillmissing (x, "movmedian", 3, "samplepoints", [1, 2, 3]), y);
 y = x;
 y([1, 6, 8, 10, 18, 20, 21]) = [4, 6, 11, 7, 15, 20, 24];
 assert (fillmissing (x, "movmedian", 3, 2), y);
 assert (fillmissing (x, "movmedian", [1, 1], 2), y);
 assert (fillmissing (x, "movmedian", 3, 2, "endvalues", "extrap"), y);
 assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [1, 2, 3, 4]), y);
 y([1,18]) = NaN;
 y(6) = 9;
 assert (fillmissing (x, "movmedian", 3, 2, "samplepoints", [0, 2, 3, 4]), y);
 y = x;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
 y(8) = 8;
 assert (fillmissing (x, "movmedian", 3, "endvalues", 99), y);
 y = x;
 y([1, 2, 5, 8, 10, 13, 16, 19, 22]) = 99;
 y([6, 18, 20, 21]) = [6, 15, 20, 24];
 assert (fillmissing (x, "movmedian", 3, 2, "endvalues", 99), y);
***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 1), [1, 2, NaN])
***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, 2), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], @(x,y,z) x+y+z, [1, 0]), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3]', @(x,y,z) x+y+z, 2), [1, 2, 3]')
***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, 2), [1, 2, 7])
***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [1, 2, 7])
***** assert (fillmissing ([1, 2, NaN], @(x,y,z) x+y+z, [1, 0]'), [1, 2, 7])
***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, 2), [5, 2, 7])
***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [1, 0]), [NaN, 2, 7])
***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1]), [5, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], @(x,y,z) x+y+z, [0, 1.1]), [5, 2, NaN])
***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 2), [1, 2, 7, 12, 3, 4])
***** assert (fillmissing ([1, 2, NaN, NaN, 3, 4], @(x,y,z) x+y+z, 0.5), [1, 2, NaN, NaN, 3, 4])
***** function A = testfcn (x, y, z)
  if (isempty (y))
    A = z;
  elseif (numel (y) == 1)
    A = repelem (x(1), numel(z));
  else
    A = interp1 (y, x, z, "linear", "extrap");
  endif
***** endfunction
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, [3, 0]), [1, 1, 3, NaN, 5])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 1), [1, 2, 6; 4, 2, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 2), [1, 2, 2; 4, 5, 6])
***** assert (fillmissing ([1, 2, NaN; 4, NaN, 6], @testfcn, 3, 3), [1, 2, NaN; 4, NaN, 6])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 1), [1, NaN, 3, NaN, 5]) ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 1), [1, 2, 3, 4, 5]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 2), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 2), [1, NaN, 3, NaN, 5]') ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]'
***** assert (fillmissing ([1, NaN, 3, NaN, 5], @testfcn, 99, 3), [1, NaN, 3, NaN, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', @testfcn, 99, 3), [1, NaN, 3, NaN, 5]')
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 3, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1, 1], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [1.5, 1.5], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 4, "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, [2, 2], "samplepoints", [1, 2, 3, 4, 5]), [1, 2, 3, 4, 5])
***** assert (fillmissing ([1, NaN, NaN, NaN, 5], @testfcn, 3, "samplepoints", [1, 2, 2.5, 3, 3.5]), [1, 2.6, 3.4, 4.2, 5], 10*eps)
***** assert (fillmissing ([NaN, NaN, 3, NaN, 5], @testfcn, 99, 1), [NaN, NaN, 3, NaN, 5]) ##known not-compatible. matlab bug ML2022a: [1, 1, 3, 1, 5]
***** test
***** function A = testfcn (x, y, z)
  if (isempty (y))
    A = z;
  elseif (numel (y) == 1)
    A = repelem (x(1), numel(z));
  else
    A = interp1 (y, x, z, "linear", "extrap");
  endif
***** endfunction
 x = reshape ([1:24], 3, 4, 2);
 x([1, 2, 5, 6, 8, 10, 13, 16, 18, 19, 20, 21, 22]) = NaN;
 y = x;
 y([1, 2, 5, 6, 8, 10, 13, 16, 18, 22]) = [3, 3, 4, 4, 8, 11, 14, 17, 17, 23];
 assert (fillmissing (x, @testfcn, 3), y);
 assert (fillmissing (x, @testfcn, [1, 1]), y);
 assert (fillmissing (x, @testfcn, 3, "endvalues", "extrap"), y);
 assert (fillmissing (x, @testfcn, 3, "samplepoints", [1, 2, 3]), y);
 y= x;
 y(isnan (x)) = 99;
 y(8) = 8;
 assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y)
 y = x;
 y([1, 2, 5, 6, 8, 10, 18, 20, 21]) = [4, 11, 11, 6, 11, 7, 18, 20, 21];
 assert (fillmissing (x, @testfcn, 3, 2), y);
 assert (fillmissing (x, @testfcn, [1, 1], 2), y);
 assert (fillmissing (x, @testfcn, 3, 2, "endvalues", "extrap"), y);
 assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [1, 2, 3, 4]), y);
 y(1) = NaN;
 y([6, 18, 21]) = [9, 24, 24];
 assert (fillmissing (x, @testfcn, 3, 2, "samplepoints", [0, 2, 3, 4]), y);
 y = x;
 y([1, 2, 5, 6, 10, 13, 16, 18, 19, 20, 21, 22]) = 99;
 y(8) = 8;
 assert (fillmissing (x, @testfcn, 3, "endvalues", 99), y);
 y([6, 18, 20, 21]) = [6, 18, 20, 21];
 y(8) = 99;
 assert (fillmissing (x, @testfcn, 3, 2, "endvalues", 99), y);
 y([6, 18, 20, 21]) = 99;
 assert (fillmissing (x, @testfcn, 3, 3, "endvalues", 99), y);
***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 1), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "constant", 0, "maxgap", 99), [1, 2, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1), [1, NaN, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 1.999), [1, NaN, 3])
***** assert (fillmissing ([1, NaN, 3], "constant", 0, "maxgap", 2), [1, 0, 3])
***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 2), [1, NaN, NaN, 4])
***** assert (fillmissing ([1, NaN, NaN, 4], "constant", 0, "maxgap", 3), [1, 0, 0, 4])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2), [1, 0, 3, 0, 5])
***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 0.999), [NaN, 2, NaN])
***** assert (fillmissing ([NaN, 2, NaN], "constant", 0, "maxgap", 1), [0, 2, 0])
***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 1), [0, 2, NaN, NaN])
***** assert (fillmissing ([NaN, 2, NaN, NaN], "constant", 0, "maxgap", 2), [0, 2, 0, 0])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 1), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 3), [NaN, NaN, NaN])
***** assert (fillmissing ([NaN, NaN, NaN], "constant", 0, "maxgap", 999), [NaN, NaN, NaN])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5]', "constant", 0, "maxgap", 2, "samplepoints", [0, 1, 2, 3, 5]), [1, 0, 3, NaN, 5]')
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", 0, "maxgap", 2, "samplepoints", [0, 2, 3, 4, 5]), [1, NaN, 3, 0, 5])
***** assert (fillmissing ([1, NaN, 3, NaN, 5; 1, NaN, 3, NaN, 5], "constant", 0, 2, "maxgap", 2, "samplepoints", [0, 2, 3, 4, 5]), [1, NaN, 3, 0, 5; 1, NaN, 3, 0, 5])
***** test
 x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
 assert (fillmissing (x, "constant", 0, "maxgap", 0.1), x);
 y = x;
 y([4, 7, 12]) = 0;
 assert (fillmissing (x, "constant", 0, "maxgap", 1), y);
 assert (fillmissing (x, "constant", 0, 1, "maxgap", 1), y);
 y = x;
 y([5, 7, 12]) = 0;
 assert (fillmissing (x, "constant", 0, 2, "maxgap", 1), y);
 y = x;
 y([4, 5, 7]) = 0;
 assert (fillmissing (x, "constant", 0, 3, "maxgap", 1), y);
***** test
 x = cat (3, [1, 2, NaN; 4, NaN, NaN], [NaN, 2, 3; 4, 5, NaN]);
 [~, idx] = fillmissing (x, "constant", 0, "maxgap", 1);
 assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
 [~, idx] = fillmissing (x, "constant", 0, 1, "maxgap", 1);
 assert (idx, logical (cat (3, [0, 0, 0; 0, 1, 0], [1, 0, 0; 0, 0, 1])));
 [~, idx] = fillmissing (x, "constant", 0, 2, "maxgap", 1);
 assert (idx, logical (cat (3, [0, 0, 1; 0, 0, 0], [1, 0, 0; 0, 0, 1])));
 [~, idx] = fillmissing (x, "constant", 0, 3, "maxgap", 1);
 assert (idx, logical (cat (3, [0, 0, 1; 0, 1, 0], [1, 0, 0; 0, 0, 0])));
***** test
 x = [NaN, 2, 3];
 [~, idx] = fillmissing (x, "previous");
 assert (idx, logical ([0, 0, 0]));
 [~, idx] = fillmissing (x, "movmean", 1);
 assert (idx, logical ([0, 0, 0]));
 x = [1:3; 4:6; 7:9];
 x([2, 4, 7, 9]) = NaN;
 [~, idx] = fillmissing (x, "linear");
 assert (idx, logical ([0, 1, 0; 1, 0, 0; 0, 0, 0]));
 [~, idx] = fillmissing (x, "movmean", 2);
 assert (idx, logical ([0, 0, 0; 1, 0, 0; 0, 0, 1]));
 [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",2);
 assert (A, [1, 2, 3, 3, NaN]);
 assert (idx, logical ([0, 0, 0, 1, 0]));
 [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmean",3);
 assert (A, [1, 2, 3, 3, NaN]);
 assert (idx, logical ([0, 0, 0, 1, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2);
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, 3, NaN, NaN], "movmedian", 3);
 assert (A, [1, 2, 3, 3, NaN]);
 assert (idx, logical ([0, 0, 0, 1, 0]));
 [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) z, 3);
 assert (A, [1, 2, 1, 4, 1]);
 assert (idx, logical ([0, 1, 0, 1, 0]));
 [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3);
 assert (A, [1, NaN, 1, NaN, 1]);
 assert (idx, logical ([0, 0, 0, 0, 0]));
***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([0, 0, 0])), [1, 2, 3])
***** assert (fillmissing ([1, 2, 3], "constant", 99, "missinglocations", logical ([1, 1, 1])), [99, 99, 99])
***** assert (fillmissing ([1, NaN, 2, 3, NaN], "constant", 99, "missinglocations", logical ([1, 0, 1, 0, 1])), [99, NaN, 99, 3, 99])
***** assert (fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0])), [1, NaN, NaN, NaN, 5])
***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([0, 0, 0, 0; 0, 0, 0, 0])), ["foo "; " bar"])
***** assert (fillmissing (["foo "; " bar"], "constant", "X", "missinglocations", logical ([1, 0, 1, 0; 0, 1, 1, 0])), ["XoX "; " XXr"])
***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([0, 0, 0])), {"foo", "", "bar"})
***** assert (fillmissing ({"foo", "", "bar"}, "constant", "X", "missinglocations", logical ([1, 1, 0])), {"X", "X", "bar"})
***** test
 [~, idx] = fillmissing ([1, NaN, 3, NaN, 5], "constant", NaN);
 assert (idx, logical ([0, 0, 0, 0, 0]));
 [~, idx] = fillmissing ([1 NaN 3 NaN 5], "constant", NaN, "missinglocations", logical ([0, 1, 1, 1, 0]));
 assert (idx, logical ([0, 1, 1, 1, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, 1, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmean", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 2, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, 2, NaN, NaN, NaN], "movmedian", 3.1, "missinglocations", logical ([0, 0, 1, 1, 0]));
 assert (A, [1, 2, 2, NaN, NaN]);
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) ones (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
 assert (A, [1, 1, 1, 1, 1]);
 assert (idx, logical ([0, 1, 0, 1, 1]));
 [A, idx] = fillmissing ([1, NaN, 1, NaN, 1],  @(x,y,z) NaN (size (z)), 3, "missinglocations", logical ([0, 1, 0, 1, 1]));
 assert (A, [1, NaN, 1, NaN, NaN]);
 assert (idx, logical ([0, 0, 0, 0, 0]));
***** test
 [A, idx] = fillmissing ([1, 2, 5], "movmedian", 3, "missinglocations", logical ([0, 1, 0]));
 assert (A, [1, 3, 5]);
 assert (idx, logical ([0, 1, 0]));
***** assert (fillmissing (' foo bar ', "constant", 'X', "missinglocations", logical ([1, 0, 0, 0, 1, 0, 0, 0, 1])), 'XfooXbarX')
***** assert (fillmissing ([' foo'; 'bar '], "constant", 'X', "missinglocations", logical ([1, 0, 0, 0; 0, 0, 0, 1])), ['Xfoo'; 'barX'])
***** assert (fillmissing ([' foo'; 'bar '], "next", "missinglocations", logical ([1, 0, 0, 0; 0, 0, 0, 1])), ['bfoo'; 'bar '])
***** assert (fillmissing ([' foo'; 'bar '], "next", 1, "missinglocations", logical ([1, 0, 0, 0; 0, 0, 0, 1])), ['bfoo'; 'bar '])
***** assert (fillmissing ([' foo'; 'bar '], "previous", "missinglocations", logical ([1, 0, 0, 0; 0, 0, 0, 1])), [' foo'; 'baro'])
***** assert (fillmissing ([' foo'; 'bar '], "previous", 1, "missinglocations", logical ([1, 0, 0, 0; 0, 0, 0, 1])), [' foo'; 'baro'])
***** assert (fillmissing ([' foo'; 'bar '], "nearest", "missinglocations", logical ([1, 0, 0, 0; 0, 0, 0, 1])), ['bfoo'; 'baro'])
***** assert (fillmissing ([' foo'; 'bar '], "nearest", 1, "missinglocations", logical ([1, 0, 0, 0; 0, 0, 0, 1])), ['bfoo'; 'baro'])
***** assert (fillmissing ([' foo'; 'bar '], "next", 2, "missinglocations", logical ([1, 0, 0, 0; 0, 0, 0, 1])), ['ffoo'; 'bar '])
***** assert (fillmissing ([' foo'; 'bar '], "previous", 2, "missinglocations", logical ([1, 0, 0, 0; 0, 0, 0, 1])), [' foo'; 'barr'])
***** assert (fillmissing ([' foo'; 'bar '], "nearest", 2, "missinglocations", logical ([1, 0, 0, 0; 0, 0, 0, 1])), ['ffoo'; 'barr'])
***** assert (fillmissing ([' foo'; 'bar '], "next", 3, "missinglocations", logical ([1, 0, 0, 0; 0, 0, 0, 1])), [' foo'; 'bar '])
***** assert (fillmissing ([' foo'; 'bar '], "previous", 3, "missinglocations", logical ([1, 0, 0, 0; 0, 0, 0, 1])), [' foo'; 'bar '])
***** assert (fillmissing ([' foo'; 'bar '], "nearest", 3, "missinglocations", logical ([1, 0, 0, 0; 0, 0, 0, 1])), [' foo'; 'bar '])
***** assert (fillmissing ({'foo', 'bar'}, "constant", 'a'), {'foo', 'bar'})
***** assert (fillmissing ({'foo', 'bar'}, "constant", {'a'}), {'foo', 'bar'})
***** assert (fillmissing ({'foo', '', 'bar'}, "constant", 'a'), {'foo', 'a', 'bar'})
***** assert (fillmissing ({'foo', '', 'bar'}, "constant", {'a'}), {'foo', 'a', 'bar'})
***** assert (fillmissing ({'foo', '', 'bar'}, "previous"), {'foo', 'foo', 'bar'})
***** assert (fillmissing ({'foo', '', 'bar'}, "next"), {'foo', 'bar', 'bar'})
***** assert (fillmissing ({'foo', '', 'bar'}, "nearest"), {'foo', 'bar', 'bar'})
***** assert (fillmissing ({'foo', '', 'bar'}, "previous", 2), {'foo', 'foo', 'bar'})
***** assert (fillmissing ({'foo', '', 'bar'}, "next", 2), {'foo', 'bar', 'bar'})
***** assert (fillmissing ({'foo', '', 'bar'}, "nearest", 2), {'foo', 'bar', 'bar'})
***** assert (fillmissing ({'foo', '', 'bar'}, "previous", 1), {'foo', '', 'bar'})
***** assert (fillmissing ({'foo', '', 'bar'}, "previous", 1), {'foo', '', 'bar'})
***** assert (fillmissing ({'foo', '', 'bar'}, "next", 1), {'foo', '', 'bar'})
***** assert (fillmissing ({'foo', '', 'bar'}, "nearest", 1), {'foo', '', 'bar'})
***** assert (fillmissing ('abc ', @(x,y,z) x+y+z, 2, "missinglocations", logical ([0, 0, 0, 1])), 'abcj')
***** assert (fillmissing ({'foo', '', 'bar'}, @(x,y,z) x(1), 3), {'foo', 'foo', 'bar'})
***** test
 [A, idx] = fillmissing (' a b c', "constant", ' ', "missinglocations", logical ([1, 0, 1, 0, 1, 0]));
 assert (A, ' a b c');
 assert (idx, logical ([1, 0, 1, 0, 1, 0]));
***** test
 [A, idx] = fillmissing (' a b c', "constant", ' ');
 assert (A, ' a b c');
 assert (idx, logical ([0, 0, 0, 0, 0, 0]));
***** test
 [A, idx] = fillmissing ({'foo', '', 'bar', ''}, "constant", '');
 assert (A, {'foo', '', 'bar', ''});
 assert (idx, logical ([0, 0, 0, 0]));
***** test
 [A, idx] = fillmissing ({'foo', '', 'bar', ''}, "constant", {''});
 assert (A, {'foo', '', 'bar', ''});
 assert (idx, logical ([0, 0, 0, 0]));
***** test
 [A, idx] = fillmissing (' f o o ', @(x,y,z) repelem ('a', numel (z)), 3, "missinglocations", logical ([1, 0, 1, 0, 1, 0, 1]));
 assert (A, 'afaoaoa');
 assert (idx, logical ([1, 0, 1, 0, 1, 0, 1]));
***** test
 [A, idx] = fillmissing (' f o o ', @(x,y,z) repelem (' ', numel (z)), 3);
 assert (A, ' f o o ');
 assert (idx, logical ([0, 0, 0, 0, 0, 0, 0]));
***** test
 [A, idx] = fillmissing ({'', 'foo', ''}, @(x,y,z) repelem ({'a'}, numel (z)), 3);
 assert (A, {"a", "foo", "a"});
 assert (idx, logical ([1, 0, 1]));
***** test
 [A, idx] = fillmissing ({'', 'foo', ''}, @(x,y,z) repelem ({''}, numel (z)), 3);
 assert (A, {'', 'foo', ''});
 assert (idx, logical ([0, 0, 0]));
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", true), logical ([1, 0, 1, 0, 1]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "previous",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([1, 0, 0, 0, 0]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "next",  "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 1]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), 3), logical ([1, 0, 1, 0, 1]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), 3, "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([0, 0, 0, 0, 0]))
***** assert (fillmissing (logical ([1, 0, 1, 0, 1]),  @(x,y,z) false(size(z)), [2, 0], "missinglocations", logical ([1, 0, 1, 0, 1])), logical ([1, 0, 0, 0, 0]))
***** test
 x = logical ([1, 0, 1, 0, 1]);
 [~, idx] = fillmissing (x, "constant", true);
 assert (idx, logical ([0, 0, 0, 0, 0]));
 [~, idx] = fillmissing (x, "constant", false, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "constant", true, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([0, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "next",  "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 0]));
 [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3);
 assert (idx, logical ([0, 0, 0, 0, 0]))
 [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]))
 [~, idx] = fillmissing (x, @(x,y,z) false(size(z)), [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([0, 0, 1, 0, 1]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0), int32 ([1, 2, 3, 4, 5]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([0, 2, 0, 4, 0]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "previous", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([1, 2, 2, 4, 4]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "next", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 5]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), "nearest", "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([2, 2, 4, 4, 4]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, 3), int32 ([1, 2, 3, 4, 5]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, 3, "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([11, 2, 13, 4, 15]))
***** assert (fillmissing (int32 ([1, 2, 3, 4, 5]), @(x,y,z) z+10, [2, 0], "missinglocations", logical ([1, 0, 1, 0, 1])), int32 ([1, 2, 13, 4, 15]))
***** test
 x = int32 ([1, 2, 3, 4, 5]);
 [~, idx] = fillmissing (x, "constant", 0);
 assert (idx, logical ([0, 0, 0, 0, 0]));
 [~, idx] = fillmissing (x, "constant", 0, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "constant", 3, "missinglocations", logical ([0, 0, 1, 0, 0]));
 assert (idx, logical ([0, 0, 1, 0, 0]));
 [~, idx] = fillmissing (x, "previous", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([0, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, "next", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 0]));
 [~, idx] = fillmissing (x, "nearest", "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, @(x,y,z) z+10, 3);
 assert (idx, logical ([0, 0, 0, 0, 0]));
 [~, idx] = fillmissing (x, @(x,y,z) z+10, 3, "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([1, 0, 1, 0, 1]));
 [~, idx] = fillmissing (x, @(x,y,z) z+10, [2 0], "missinglocations", logical ([1, 0, 1, 0, 1]));
 assert (idx, logical ([0, 0, 1, 0, 1]));
***** test
 [A, idx] = fillmissing ([struct, struct], "constant", 1);
 assert (A, [struct, struct])
 assert (idx, [false, false])
***** error <Invalid call> fillmissing ()
***** error <Invalid call> fillmissing (1)
***** error <Invalid call> fillmissing (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
***** error <second input must be a> fillmissing (1, 2)
***** error <unknown fill method 'foo'> fillmissing (1, "foo")
***** error <fill function must accept at least> fillmissing (1, @(x) x, 1)
***** error <fill function must accept at least> fillmissing (1, @(x,y) x+y, 1)
***** error <interpolation methods only valid for numeric> fillmissing ("a b c", "linear")
***** error <interpolation methods only valid for numeric> fillmissing ({"a", "b"}, "linear")
***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ("a b c", "movmean", 2)
***** error <'movmean' and 'movmedian' methods only valid for numeric> fillmissing ({"a", "b"}, "movmean", 2)
***** error <'constant' method must be followed by> fillmissing (1, "constant")
***** error <a numeric fill value cannot be emtpy> fillmissing (1, "constant", [])
***** error <fill value must be the same data type> fillmissing (1, "constant", "a")
***** error <fill value must be the same data type> fillmissing ("a", "constant", 1)
***** error <fill value must be the same data type> fillmissing ("a", "constant", {"foo"})
***** error <fill value must be the same data type> fillmissing ({"foo"}, "constant", 1)
***** error <moving window method must be followed by> fillmissing (1, "movmean")
***** error <moving window method must be followed by> fillmissing (1, "movmedian")
***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, 0)
***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, -1)
***** error <DIM must be a positive scalar> fillmissing (1, "constant", 1, [1, 2])
***** error <properties must be given as> fillmissing (1, "constant", 1, "samplepoints")
***** error <properties must be given as> fillmissing (1, "constant", 1, "foo")
***** error <properties must be given as> fillmissing (1, "constant", 1, 1, "foo")
***** error <invalid parameter name specified> fillmissing (1, "constant", 1, 2, {1}, 4)
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 2])
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [3, 1, 2])
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", [1, 1, 2])
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", "abc")
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 2, "samplepoints", logical ([1, 1, 1]))
***** error <SamplePoints must be a> fillmissing ([1, 2, 3], "constant", 1, 1, "samplepoints", [1, 2, 3])
***** error <EndValues method 'constant' only valid> fillmissing ("foo", "next", "endvalues", 1)
***** error <invalid EndValues method 'foo'> fillmissing (1, "constant", 1, 1, "endvalues", "foo")
***** error <EndValues must be a scalar or a 1 element array> fillmissing ([1, 2, 3], "constant", 1, 2, "endvalues", [1, 2, 3])
***** error <EndValues must be a scalar or a 3 element array> fillmissing ([1, 2, 3], "constant", 1, 1, "endvalues", [1, 2])
***** error <EndValues must be a scalar or a 12 element array> fillmissing (randi(5,4,3,2), "constant", 1, 3, "endvalues", [1, 2])
***** error <EndValues must be numeric or a> fillmissing (1, "constant", 1, 1, "endvalues", {1})
***** error <invalid parameter name 'foo'> fillmissing (1, "constant", 1, 2, "foo", 4)
***** error <MissingLocations option is not compatible with> fillmissing (struct, "constant", 1, "missinglocations", false)
***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "maxgap", 1, "missinglocations", false)
***** error <MissingLocations and MaxGap options> fillmissing (1, "constant", 1, 2, "missinglocations", false, "maxgap", 1)
***** error <the 'replacevalues' option has not> fillmissing (1, "constant", 1, "replacevalues", true)
***** error <the 'datavariables' option has not> fillmissing (1, "constant", 1, "datavariables", "Varname")
***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", 1)
***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", "a")
***** error <MissingLocations must be a> fillmissing (1, "constant", 1, 2, "missinglocations", [true, false])
***** error <MissingLocations cannot be used with method> fillmissing (true, "linear", "missinglocations", true)
***** error <MissingLocations cannot be used with method> fillmissing (int8 (1), "linear", "missinglocations", true)
***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "missinglocations", true, "EndValues", "linear")
***** error <MissingLocations cannot be used with EndValues method> fillmissing (true, "next", "EndValues", "linear", "missinglocations", true)
***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "missinglocations", true, "EndValues", "linear")
***** error <MissingLocations cannot be used with EndValues method> fillmissing (int8 (1), "next", "EndValues", "linear", "missinglocations", true)
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", true)
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", "a")
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", [1, 2])
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", 0)
***** error <MaxGap must be a positive numeric scalar> fillmissing (1, "constant", 1, 2, "maxgap", -1)
***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3])
***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3])
***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3]', "constant", [1, 2, 3], 1)
***** error <fill value 'V' must be a scalar or a 1> fillmissing ([1, 2, 3], "constant", [1, 2, 3], 2)
***** error <fill value 'V' must be a scalar or a 6> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 1)
***** error <fill value 'V' must be a scalar or a 8> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 2)
***** error <fill value 'V' must be a scalar or a 12> fillmissing (randi (5, 4, 3, 2), "constant", [1, 2], 3)
***** error <fill function handle must be followed by> fillmissing (1, @(x,y,z) x+y+z)
***** error <fill function return values must be the same size> fillmissing ([1, NaN, 2], @(x,y,z) [1, 2], 2)
387 tests, 387 passed, 0 known failure, 0 skipped
[inst/fitcgam.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/fitcgam.m
***** demo
 ## Train a GAM classifier for binary classification
 ## using specific data and plot the decision boundaries.

 ## Define specific data
 X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
     6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
 Y = [0; 0; 0; 0; 0; ...
     1; 1; 1; 1; 1];

 ## Train the GAM model
 obj = fitcgam (X, Y, "Interactions", "all");

 ## Create a grid of values for prediction
 x1 = [min(X(:,1)):0.1:max(X(:,1))];
 x2 = [min(X(:,2)):0.1:max(X(:,2))];
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];
 pred = predict (obj, XGrid);

 ## Plot decision boundaries and data points
 predNumeric = str2double (pred);
 gidx = predNumeric > 0.5;

 figure
 scatter(XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
 hold on
 scatter(XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
 plot(X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
 xlabel("Feature 1");
 ylabel("Feature 2");
 title("Generalized Additive Model (GAM) Decision Boundary");
 legend({"Class 1 Region", "Class 0 Region", ...
       "Class 1 Samples", "Class 0 Samples"}, ...
       "location", "northwest")
 axis tight
 hold off
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
 a = fitcgam (x, y, "PredictorNames", PredictorNames);
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
 assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.PredictorNames, PredictorNames)
 assert (a.BaseModel.Intercept, 0)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
 y = [1; 0; 1; 0; 1];
 a = fitcgam (x, y, "interactions", "all");
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
 assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.PredictorNames, {'x1', 'x2'})
 assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
***** test
 load fisheriris
 inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
 X = meas(inds, :);
 Y = species(inds, :)';
 Y = strcmp (Y, 'virginica')';
 a = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
 assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
 assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
 assert (a.ModelwInt.Intercept, 0)
***** error<fitcgam: too few arguments.> fitcgam ()
***** error<fitcgam: too few arguments.> fitcgam (ones (4,1))
***** error<fitcgam: name-value arguments must be in pairs.>
 fitcgam (ones (4,2), ones (4, 1), "K")
***** error<fitcgam: number of rows in X and Y must be equal.>
 fitcgam (ones (4,2), ones (3, 1))
***** error<fitcgam: number of rows in X and Y must be equal.>
 fitcgam (ones (4,2), ones (3, 1), "K", 2)
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/kruskalwallis.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/kruskalwallis.m
***** demo
 x = meshgrid (1:6);
 x = x + normrnd (0, 1, 6, 6);
 kruskalwallis (x, [], 'off');
***** demo
 x = meshgrid (1:6);
 x = x + normrnd (0, 1, 6, 6);
 [p, atab] = kruskalwallis(x);
***** demo
 x = ones (30, 4) .* [-2, 0, 1, 5];
 x = x + normrnd (0, 2, 30, 4);
 group = {"A", "B", "C", "D"};
 kruskalwallis (x, group);
***** test
 data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
         0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
         0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
         1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
         0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
         1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
         0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
         0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
         1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
         0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
 group = [1:10] .* ones (10,10);
 group = group(:);
 [p, tbl] = kruskalwallis (data, group, "off");
 assert (p, 0.048229, 1e-6);
 assert (tbl{2,5}, 17.03124, 1e-5);
 assert (tbl{2,3}, 9, 0);
 assert (tbl{4,2}, 82655.5, 1e-16);
 data = reshape (data, 10, 10);
 [p, tbl, stats] = kruskalwallis (data, [], "off");
 assert (p, 0.048229, 1e-6);
 assert (tbl{2,5}, 17.03124, 1e-5);
 assert (tbl{2,3}, 9, 0);
 assert (tbl{4,2}, 82655.5, 1e-16);
 means = [51.85, 60.45, 37.6, 51.1, 29.5, 54.25, 64.55, 66.7, 53.65, 35.35];
 N = 10 * ones (1, 10);
 assert (stats.meanranks, means, 1e-6);
 assert (length (stats.gnames), 10, 0);
 assert (stats.n, N, 0);
1 test, 1 passed, 0 known failure, 0 skipped
[inst/ttest.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/ttest.m
***** test
 x = 8:0.1:12;
 [h, pval, ci] = ttest (x, 10);
 assert (h, 0)
 assert (pval, 1, 10*eps)
 assert (ci, [9.6219 10.3781], 1E-5)
 [h, pval, ci0] = ttest (x, 0);
 assert (h, 1)
 assert (pval, 0)
 assert (ci0, ci, 2e-15)
 [h, pval, ci] = ttest (x, 10, "tail", "right", "dim", 2, "alpha", 0.05);
 assert (h, 0)
 assert (pval, 0.5, 10*eps)
 assert (ci, [9.68498 Inf], 1E-5)
***** error ttest ([8:0.1:12], 10, "tail", "invalid");
***** error ttest ([8:0.1:12], 10, "tail", 25);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/confusionchart.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/confusionchart.m
***** demo
 close all
 ## Setting the chart properties
 Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
 Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
 confusionchart (Yt, Yp, "Title", ...
   "Demonstration with summaries","Normalization",...
   "absolute","ColumnSummary", "column-normalized","RowSummary",...
   "row-normalized")
***** demo
 close all
 ## Cellstr as inputs
 Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
 Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
 m = confusionmat (Yt, Yp);
 confusionchart (m, {"Positive", "Negative"});
 hold off
***** demo
 close all
 ## Editing the object properties
 Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
 Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
 cm = confusionchart (Yt, Yp);
 cm.Title = "This is an example with a green diagonal";
 cm.DiagonalColor = [0.4660, 0.6740, 0.1880];
 hold off
***** demo
 close all
 ## Confusion chart in a uipanel
 h = uipanel ();
 Yt = {"Positive", "Positive", "Positive", "Negative", "Negative"};
 Yp = {"Positive", "Positive", "Negative", "Negative", "Negative"};
 cm = confusionchart (h, Yt, Yp);
 hold off
***** demo
 close all
 ## Sorting classes
 Yt = [8 5 6 8 5 3 1 6 4 2 5 3 1 4]';
 Yp = [8 5 6 8 5 2 3 4 4 5 5 7 2 6]';
 cm = confusionchart (Yt, Yp, "Title", ...
   "Classes are sorted in ascending order");
 cm = confusionchart (Yt, Yp, "Title", ...
   "Classes are sorted according to clusters");
 sortClasses (cm, "cluster");
***** shared visibility_setting
 visibility_setting = get (0, "DefaultFigureVisible");
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ()", "Invalid call");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 1; 2 2; 3 3])", "invalid argument");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'xxx', 1)", "invalid property");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'XLabel', 1)", "XLabel .* string");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'YLabel', [1 0])", ...
       ".* YLabel .* string");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'Title', .5)", ".* Title .* string");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'FontName', [])", ...
       ".* FontName .* string");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'FontSize', 'b')", ...
       ".* FontSize .* numeric");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'DiagonalColor', 'h')", ...
       ".* DiagonalColor .* color");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'OffDiagonalColor', [])", ...
       ".* OffDiagonalColor .* color");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'Normalization', '')", ...
       ".* invalid .* Normalization");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'ColumnSummary', [])", ...
       ".* invalid .* ColumnSummary");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'RowSummary', 1)", ...
       ".* invalid .* RowSummary");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'GridVisible', .1)", ...
       ".* invalid .* GridVisible");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'HandleVisibility', .1)", ...
       ".* invalid .* HandleVisibility");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'OuterPosition', .1)", ...
       ".* invalid .* OuterPosition");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'Position', .1)", ...
       ".* invalid .* Position");
 set (0, "DefaultFigureVisible", visibility_setting);
***** test
 set (0, "DefaultFigureVisible", "off");
 fail ("confusionchart ([1 2], [0 1], 'Units', .1)", ".* invalid .* Units");
 set (0, "DefaultFigureVisible", visibility_setting);
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/ecdf.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/ecdf.m
***** demo
 y = exprnd (10, 50, 1);    ## random failure times are exponential(10)
 d = exprnd (20, 50, 1);    ## drop-out times are exponential(20)
 t = min (y, d);            ## we observe the minimum of these times
 censored = (y > d);        ## we also observe whether the subject failed

 ## Calculate and plot the empirical cdf and confidence bounds
 [f, x, flo, fup] = ecdf (t, "censoring", censored);
 stairs (x, f);
 hold on;
 stairs (x, flo, "r:"); stairs (x, fup, "r:");

 ## Superimpose a plot of the known true cdf
 xx = 0:.1:max (t); yy = 1 - exp (-xx / 10); plot (xx, yy, "g-");
 hold off;
***** demo
 R = wblrnd (100, 2, 100, 1);
 ecdf (R, "Function", "survivor", "Alpha", 0.01, "Bounds", "on");
 hold on
 x = 1:1:250;
 wblsurv = 1 - cdf ("weibull", x, 100, 2);
 plot (x, wblsurv, "g-", "LineWidth", 2)
 legend ("Empirical survivor function", "Lower confidence bound", ...
         "Upper confidence bound", "Weibull survivor function", ...
         "Location", "northeast");
 hold off
***** error ecdf ();
***** error ecdf (randi (15,2));
***** error ecdf ([3,2,4,3+2i,5]);
***** error kstest ([2,3,4,5,6],"tail");
***** error kstest ([2,3,4,5,6],"tail", "whatever");
***** error kstest ([2,3,4,5,6],"function", "");
***** error kstest ([2,3,4,5,6],"badoption", 0.51);
***** error kstest ([2,3,4,5,6],"tail", 0);
***** error kstest ([2,3,4,5,6],"alpha", 0);
***** error kstest ([2,3,4,5,6],"alpha", NaN);
***** error kstest ([NaN,NaN,NaN,NaN,NaN],"tail", "unequal");
***** error kstest ([2,3,4,5,6],"alpha", 0.05, "CDF", [2,3,4;1,3,4;1,2,1]);
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
   [F, x, Flo, Fup] = ecdf (x);
   F_out = [0; 0.0714; 0.1429; 0.3571; 0.5; 0.6429; 0.7143; 0.7857; 0.9286; 1];
   assert (F, F_out, ones (10,1) * 1e-4);
   x_out = [0 0 2 3 4 5 6 7 8 9]';
   assert (x, x_out);
   Flo_out = [NaN, 0, 0, 0.1061, 0.2381, 0.3919, 0.4776, 0.5708, 0.7937, NaN]';
   assert (Flo, Flo_out, ones (10,1) * 1e-4);
   Fup_out = [NaN, 0.2063, 0.3262, 0.6081, 0.7619, 0.8939, 0.9509, 1, 1, NaN]';
   assert (Fup, Fup_out, ones (10,1) * 1e-4);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [2, 3, 4, 3, 5, 4, 6, 5, 8, 3, 7, 8, 9, 0];
   ecdf (x);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/linkage.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/linkage.m
***** shared x, t
 x = reshape (mod (magic (6),5), [], 3);
 t = 1e-6;
***** assert (cond (linkage (pdist (x))),                   34.119045, t);
***** assert (cond (linkage (pdist (x), "complete")),       21.793345, t);
***** assert (cond (linkage (pdist (x), "average")),        27.045012, t);
***** assert (cond (linkage (pdist (x), "weighted")),       27.412889, t);
 lastwarn(); # Clear last warning before the test
***** warning <cluster distances> linkage (pdist (x), "centroid");
***** test
 warning off Octave:clustering
 assert (cond (linkage (pdist (x), "centroid")),      27.457477, t);
 warning on Octave:clustering
***** warning <cluster distances> linkage (pdist (x), "median");
***** test
 warning off Octave:clustering
 assert (cond (linkage (pdist (x), "median")),        27.683325, t);
 warning on Octave:clustering
***** assert (cond (linkage (pdist (x), "ward")),           17.195198, t);
***** assert (cond (linkage (x, "ward", "euclidean")),      17.195198, t);
***** assert (cond (linkage (x, "ward", {"euclidean"})),    17.195198, t);
***** assert (cond (linkage (x, "ward", {"minkowski", 2})), 17.195198, t);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/nanmin.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/nanmin.m
***** demo
 ## Find the column minimum values and their indices
 ## for matrix data with missing values.

 x = magic (3);
 x([1, 6:9]) = NaN
 [y, ind] = nanmin (x)
***** demo
 ## Find the minimum of all the values in an array, ignoring missing values.
 ## Create a 2-by-5-by-3 array x with some missing values.

 x = reshape (1:30, [2, 5, 3]);
 x([10:12, 25]) = NaN

 ## Find the minimum of the elements of x.

 y = nanmin (x, [], 'all')
***** assert (nanmin ([2, 4, NaN, 7]), 2)
***** assert (nanmin ([2, 4, NaN, -Inf]), -Inf)
***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]), [1, 5, 3])
***** assert (nanmin ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN]'), [1, 5, 7])
***** assert (nanmin (single ([1, NaN, 3; NaN, 5, 6; 7, 8, NaN])), single ([1, 5, 3]))
***** shared x, y
 x(:,:,1) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19];
 x(:,:,2) = [1.77, -0.005, NaN, -2.95; NaN, 0.34, NaN, 0.19] + 5;
 y = x;
 y(2,3,1) = 0.51;
***** assert (nanmin (x, [], [1, 2])(:), [-2.95; 2.05])
***** assert (nanmin (x, [], [1, 3])(:), [1.77; -0.005; NaN; -2.95])
***** assert (nanmin (x, [], [2, 3])(:), [-2.95; 0.19])
***** assert (nanmin (x, [], [1, 2, 3]), -2.95)
***** assert (nanmin (x, [], 'all'), -2.95)
***** assert (nanmin (y, [], [1, 3])(:), [1.77; -0.005; 0.51; -2.95])
***** assert (nanmin (x(1,:,1), x(2,:,1)), [1.77, -0.005, NaN, -2.95])
***** assert (nanmin (x(1,:,2), x(2,:,2)), [6.77, 4.995, NaN, 2.05])
***** assert (nanmin (y(1,:,1), y(2,:,1)), [1.77, -0.005, 0.51, -2.95])
***** assert (nanmin (y(1,:,2), y(2,:,2)), [6.77, 4.995, NaN, 2.05])
***** test
 xx = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (nanmin (xx, [], [3, 2])), [10, 1, 1, 3]);
 assert (size (nanmin (xx, [], [1, 2])), [1, 1, 6, 3]);
 assert (size (nanmin (xx, [], [1, 2, 4])), [1, 1, 6]);
 assert (size (nanmin (xx, [], [1, 4, 3])), [1, 40]);
 assert (size (nanmin (xx, [], [1, 2, 3, 4])), [1, 1]);
***** assert (nanmin (ones (2), [], 3), ones (2, 2))
***** assert (nanmin (ones (2, 2, 2), [], 99), ones (2, 2, 2))
***** assert (nanmin (magic (3), [], 3), magic (3))
***** assert (nanmin (magic (3), [], [1, 3]), [3, 1, 2])
***** assert (nanmin (magic (3), [], [1, 99]), [3, 1, 2])
***** assert (nanmin (ones (2), 3), ones (2,2))
***** error <nanmin: VECDIM must contain non-repeating positive integers.> ...
 nanmin (y, [], [1, 1, 2])
***** error <nanmin: a second output is not supported with this syntax.> ...
 [v, idx] = nanmin(x, y, [1 2])
24 tests, 24 passed, 0 known failure, 0 skipped
[inst/inconsistent.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/inconsistent.m
***** error inconsistent ()
***** error inconsistent ([1 2 1], 2, 3)
***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 2))
***** error <d must be a positive integer scalar> inconsistent ([1 2 1], -1)
***** error <d must be a positive integer scalar> inconsistent ([1 2 1], 1.3)
***** error <d must be a positive integer scalar> inconsistent ([1 2 1], [1 1])
***** error <Z must be .* generated by the linkage .*> inconsistent (ones (2, 3))
***** test
 load fisheriris;
 Z = linkage(meas, 'average', 'chebychev');
 assert (cond (inconsistent (Z)), 39.9, 1e-3);
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/kstest2.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/kstest2.m
***** error kstest2 ([1,2,3,4,5,5])
***** error kstest2 (ones(2,4), [1,2,3,4,5,5])
***** error kstest2 ([2,3,5,7,3+3i], [1,2,3,4,5,5])
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail")
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", "whatever")
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"badoption", 0.51)
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"tail", 0)
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", 0)
***** error kstest2 ([2,3,4,5,6],[3;5;7;8;7;6;5],"alpha", NaN)
***** error kstest2 ([NaN,NaN,NaN,NaN,NaN],[3;5;7;8;7;6;5],"tail", "unequal")
***** test
 load examgrades
 [h, p] = kstest2 (grades(:,1), grades(:,2));
 assert (h, false);
 assert (p, 0.1222791870137312, 1e-14);
***** test
 load examgrades
 [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "larger");
 assert (h, false);
 assert (p, 0.1844421391011258, 1e-14);
***** test
 load examgrades
 [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller");
 assert (h, false);
 assert (p, 0.06115357930171663, 1e-14);
***** test
 load examgrades
 [h, p] = kstest2 (grades(:,1), grades(:,2), "tail", "smaller", "alpha", 0.1);
 assert (h, true);
 assert (p, 0.06115357930171663, 1e-14);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/multiway.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/multiway.m
***** test
 numbers = [4, 5, 6, 7, 8];
 num_parts = 2;
 [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
 assert (sort (cellfun (@sum, partition)), sort ([15, 15]));
***** test
 numbers = [1, 2, 3, 4, 5, 6];
 num_parts = 3;
 [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
 assert (sort (cellfun (@sum, partition)), sort ([7, 7, 7]));
***** test
 numbers = [24, 21, 18, 17, 12, 11, 8, 2];
 num_parts = 3;
 [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
 assert (sort (cellfun (@sum, partition)), sort ([38, 38, 37]));
***** test
 numbers = [10, 10, 10];
 num_parts = 3;
 [~, partition] = multiway (numbers, num_parts, "completeKK");
 assert (sort (cellfun (@sum, partition)), [10, 10, 10]);
***** test
 numbers = 1:10;
 num_parts = 2;
 [~, partition] = multiway (numbers, num_parts, "completeKK");
 assert (sort (cellfun (@sum, partition)), [27, 28]);
***** test
 numbers = [4, 5, 6, 7, 8];
 num_parts = 2;
 [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
 assert (sort (cellfun (@sum, partition)), sort ([13, 17]));
***** test
 numbers = [1, 2, 3, 4, 5, 6];
 num_parts = 3;
 [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
 assert (sort (cellfun (@sum, partition)), sort ([7, 7, 7]));
***** test
 numbers = [10, 7, 5, 5, 6, 4, 10, 11, 12, 9, 10, 4, 3, 4, 5];
 num_parts = 4;
 [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
 assert (sort (cellfun (@sum, partition)), sort ([27, 27, 27, 24]));
***** test
 numbers = [24, 21, 18, 17, 12, 11, 8, 2];
 num_parts = 3;
 [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
 assert (sort (cellfun (@sum, partition)), sort ([35, 37, 41]));
***** test
 numbers = [10, 10, 10];
 num_parts = 3;
 [~, partition] = multiway (numbers, num_parts, "greedy");
 assert (sort (cellfun (@sum, partition)), [10, 10, 10]);
***** test
 numbers = 1:10;
 num_parts = 2;
 [~, partition] = multiway (numbers, num_parts, "greedy");
 assert (sort (cellfun (@sum, partition)), [27, 28]);
***** test
 grpidx_ckk = multiway ([3 2 4 3 9 3 64], 3);
 grpidx_greedy = multiway ([3 2 4 3 9 3 64], 3, 'greedy');
 assert (isequal (grpidx_ckk, grpidx_greedy), false);
***** test
 numbers = [4; 5; 6; 7; 8];
 num_parts = 2;
 [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
 assert (iscolumn (groupindex), true)
 assert (iscolumn (groupsizes), true);
 assert (sort (cellfun (@sum, partition)), sort ([15, 15]));
 numbers = [4; 5; 6; 7; 8];
 num_parts = 2;
 [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
 assert (iscolumn (groupindex), true)
 assert (iscolumn (groupsizes), true);
 assert (sort (cellfun (@sum, partition)), sort ([13, 17]));
***** test
 numbers = [4, 5, 6, 7, 8];
 num_parts = 2;
 [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "completeKK");
 assert (isrow (groupindex), true)
 assert (isrow (groupsizes), true);
 assert (sort (cellfun (@sum, partition)), sort ([15, 15]));
***** test
 numbers = [4, 5, 6, 7, 8];
 num_parts = 2;
 [groupindex, partition, groupsizes] = multiway (numbers, num_parts, "greedy");
 assert (isrow (groupindex), true)
 assert (isrow (groupsizes), true);
 assert (sort (cellfun (@sum, partition)), sort ([13, 17]));
***** error<multiway: too few input arguments.> multiway ()
***** error<multiway: too few input arguments.> multiway ([1, 2])
***** error<multiway: METHOD value must be a character vector.> ...
 multiway ([1, 2, 3], 2, 1)
***** error<multiway: NUMBERS must be a non-empty vector.> multiway ([], 2)
***** error<multiway: NUMBERS must be a non-empty vector.> multiway (ones (2, 2), 2)
***** error<multiway: NUMBERS must be numeric and cannot contain NaN values.> ...
 multiway ({1, 2, 3}, 2)
***** error<multiway: NUMBERS must be non-negative.> multiway ([1, -2, 3], 2)
***** error<multiway: NUMBERS must be numeric and cannot contain NaN values.> ...
 multiway ([1, 2, NaN], 2)
***** error<multiway: NUM_PARTS must be a scalar.> multiway ([1,2,3], [1,2])
***** error<multiway: NUM_PARTS must be a real numeric value.> ...
 multiway ([1, 2, 3], "2")
***** error<multiway: NUM_PARTS must be a positive integer.> multiway ([1, 2, 3], 0)
***** error<multiway: NUM_PARTS must be a positive integer.> multiway ([1, 2, 3], 1.5)
***** error<multiway: NUM_PARTS must be a positive integer.> multiway ([1, 2, 3], -1)
***** error<multiway: NUM_PARTS cannot be greater than number of elements in NUMBERS.> ...
 multiway ([1, 2], 3)
***** error <multiway: unsupported method 'greedyalgo'.> ...
 multiway ([1,2,3], 2, "greedyalgo")
30 tests, 30 passed, 0 known failure, 0 skipped
[inst/fitrgam.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/fitrgam.m
***** demo
 # Train a RegressionGAM Model for synthetic values

 f1 = @(x) cos (3 *x);
 f2 = @(x) x .^ 3;

 # generate x1 and x2 for f1 and f2
 x1 = 2 * rand (50, 1) - 1;
 x2 = 2 * rand (50, 1) - 1;

 # calculate y
 y = f1(x1) + f2(x2);

 # add noise
 y = y + y .* 0.2 .* rand (50,1);
 X = [x1, x2];

 # create an object
 a = fitrgam (X, y, "tol", 1e-3)
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [1; 2; 3; 4];
 a = fitrgam (x, y);
 assert ({a.X, a.Y}, {x, y})
 assert ({a.BaseModel.Intercept}, {2.5000})
 assert ({a.Knots, a.Order, a.DoF}, {[5, 5, 5], [3, 3, 3], [8, 8, 8]})
 assert ({a.NumObservations, a.NumPredictors}, {4, 3})
 assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
 assert ({a.Formula}, {[]})
***** test
 x = [1, 2, 3, 4; 4, 5, 6, 7; 7, 8, 9, 1; 3, 2, 1, 2];
 y = [1; 2; 3; 4];
 pnames = {"A", "B", "C", "D"};
 formula = "Y ~ A + B + C + D + A:C";
 intMat = logical ([1,0,0,0;0,1,0,0;0,0,1,0;0,0,0,1;1,0,1,0]);
 a = fitrgam (x, y, "predictors", pnames, "formula", formula);
 assert (a.IntMatrix, double (intMat))
 assert ({a.ResponseName, a.PredictorNames}, {"Y", pnames})
 assert (a.Formula, formula)
***** error<fitrgam: too few arguments.> fitrgam ()
***** error<fitrgam: too few arguments.> fitrgam (ones(10,2))
***** error<fitrgam: Name-Value arguments must be in pairs.>
 fitrgam (ones (4,2), ones (4, 1), "K")
***** error<fitrgam: number of rows in X and Y must be equal.>
 fitrgam (ones (4,2), ones (3, 1))
***** error<fitrgam: number of rows in X and Y must be equal.>
 fitrgam (ones (4,2), ones (3, 1), "K", 2)
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/grp2idx.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/grp2idx.m
***** test
 g = grp2idx ([3 2 1 2 3 1]);
 assert (g, [3; 2; 1; 2; 3; 1]);
***** test
 [g, gn] = grp2idx (['b'; 'a'; 'c'; 'a']);
 assert (g, [1; 2; 3; 2]);
 assert (gn, {'b'; 'a'; 'c'});
***** test
 in = [true, false, false, true];
 out = {[2; 1; 1; 2] {'0'; '1'} [false; true]};
 assert (nthargout (1:3, @grp2idx, in), out)
 assert (nthargout (1:3, @grp2idx, in), nthargout (1:3, @grp2idx, in'))
***** test
 assert (nthargout (1:3, @grp2idx, [false, true]),
         {[1; 2] {'0'; '1'} [false; true]});
 assert (nthargout (1:3, @grp2idx, [true, false]),
         {[2; 1] {'0'; '1'} [false; true]});
***** assert (nthargout (1:3, @grp2idx, ['oct'; 'sci'; 'oct'; 'oct'; 'sci']),
        {[1; 2; 1; 1; 2] {'oct'; 'sci'} ['oct'; 'sci']})
***** assert (nthargout (1:3, @grp2idx, {'oct'; 'sci'; 'oct'; 'oct'; 'sci'}),
        {[1; 2; 1; 1; 2] {'oct'; 'sci'} {'oct'; 'sci'}})
***** assert (nthargout (1:3, @grp2idx, [1, -3, -2, -3, -3,  2,  1, -1,  3, -3]),
        {[4; 1; 2; 1; 1; 5; 4; 3; 6; 1], {"-3"; "-2"; "-1"; "1"; "2"; "3"}, ...
         [-3; -2; -1; 1; 2; 3]})
***** test
 s = [1e6, 2e6, 1e6, 3e6];
 [g, gn, gl] = grp2idx (s);
 assert (g, [1; 2; 1; 3]);
 assert (gn, {'1000000'; '2000000'; '3000000'});
 assert (gl, [1000000; 2000000; 3000000]);
***** test
 s = [0.1, 0.2, 0.3, 0.1, 0.2];
 [g, gn, gl] = grp2idx (s);
 assert (g, [1; 2; 3; 1; 2]);
 assert (gn, {'0.1'; '0.2'; '0.3'});
 assert (gl, [0.1; 0.2; 0.3]);
***** test
 s = [-5 -10 0 5 10 -5];
 [g, gn, gl] = grp2idx (s);
 assert (g, [2; 1; 3; 4; 5; 2]);
 assert (gn, {'-10'; '-5'; '0'; '5'; '10'});
 assert (gl, [-10; -5; 0; 5; 10]);
***** assert (nthargout (1:3, @grp2idx, [2, 2, 3, NaN, 2, 3]), ...
        {[1; 1; 2; NaN; 1; 2] {'2'; '3'} [2; 3]})
***** assert (nthargout (1:3, @grp2idx, {'et', 'sa', 'sa', '', 'et'}), ...
        {[1; 2; 2; NaN; 1] {'et'; 'sa'} {'et'; 'sa'}})
***** assert (nthargout (1:3, @grp2idx, [2, 2, 3, NaN, 2, 4]), ...
        {[1; 1; 2; NaN; 1; 3] {'2'; '3'; '4'} [2; 3; 4]})
***** test
 s = [NaN, NaN, NaN];
 [g, gn, gl] = grp2idx (s);
 assert (g, [NaN; NaN; NaN]);
 assert (gn, cell (0,1));
 assert (gl, zeros (0,1));
***** test
 s = single ([NaN, NaN, NaN]);
 [g, gn, gl] = grp2idx (s);
 assert (g, [NaN; NaN; NaN]);
 assert (gn, cell (0,1));
 assert (gl, single (zeros(0,1)));
***** test
 s = {''; ''; ''; ''};
 [g, gn, gl] = grp2idx (s);
 assert (g, [NaN; NaN; NaN; NaN]);
 assert (gn, cell(0,1));
 assert (gl, cell(0,1));
***** test
 s = {'a'; ''; 'b'; ''; 'c'};
 [g, gn, gl] = grp2idx (s);
 assert (g, [1; NaN; 2; NaN; 3]);
 assert (gn, {'a'; 'b'; 'c'});
 assert (gl, {'a'; 'b'; 'c'});
***** test
 s = categorical ({''; ''; ''; ''});
 [g, gn, gl] = grp2idx (s);
 assert (g, [NaN; NaN; NaN; NaN]);
 assert (gn, cell (0,1));
 assert (isequaln (gl, categorical (cell (0,1))));
***** test
 s = string ({missing, missing, missing});
 [g, gn, gl] = grp2idx (s);
 assert (g, [NaN; NaN; NaN]);
 assert (gn, cell (0,1));
 assert (isequal (gl, string (cell (0,1))));
***** test
 s = [duration(NaN, 0, 0), duration(NaN, 0, 0), duration(NaN, 0, 0)];
 [g, gn, gl] = grp2idx (s);
 assert (g, [NaN; NaN; NaN]);
 assert (gn, cell (0,1));
 assert (isequal (gl, duration (NaN (0,3))));
***** test assert (nthargout (1:3, @grp2idx, ['sci'; 'oct'; 'sci'; 'oct'; 'oct']),
        {[1; 2; 1; 2; 2] {'sci'; 'oct'} ['sci'; 'oct']});
***** test assert (nthargout (1:3, @grp2idx, {'sci'; 'oct'; 'sci'; 'oct'; 'oct'}),
        {[1; 2; 1; 2; 2] {'sci'; 'oct'} {'sci'; 'oct'}});
***** test assert (nthargout (1:3, @grp2idx, {'sa' 'et' 'et' '' 'sa'}),
        {[1; 2; 2; NaN; 1] {'sa'; 'et'} {'sa'; 'et'}})
***** test
 [g, gn, gl] = grp2idx (categorical ({'low', 'med', 'high', 'low'}));
 assert (g, [2; 3; 1; 2]);
 assert (gn, {'high'; 'low'; 'med'});
 assert (isequal (gl, categorical ({'high'; 'low'; 'med'})));
***** test
 [g, gn, gl] = grp2idx (categorical ([10, 20, 10, 30, 20]));
 assert (g, [1; 2; 1; 3; 2]);
 assert (gn, {'10'; '20'; '30'});
 assert (isequal (gl, categorical ([10; 20; 30])));
***** test
 cats = categorical ({'high', '<undefined>', 'low', '<undefined>'});
 [g, gn, gl] = grp2idx (cats);
 assert (g, [2; 1; 3; 1]);
 assert (gn, {'<undefined>'; 'high'; 'low'});
 assert (isequal (gl, categorical ({'<undefined>'; 'high'; 'low'})));
***** test
 s = categorical ({''; ''; ''; ''}, {'1', '2', '3'}, {'1', '2', '3'});
 [g, gn, gl] = grp2idx (s);
 assert (g, nan (4, 1));
 assert (gn, {'1'; '2'; '3'});
 assert (iscategorical (gl), true);
 assert (cellstr (gl), gn);
***** test
 s = categorical ({''; '1'; ''; '2'}, {'1', '2', '3'}, {'1', '2', '3'});
 [g, gn, gl] = grp2idx (s);
 assert (g, [NaN; 1; NaN; 2]);
 assert (gn, {'1'; '2'; '3'});
 assert (iscategorical (gl), true);
 assert (cellstr (gl), gn);
***** test
 s = categorical ({''; '2'; ''; '1'}, {'1', '2', '3'}, {'1', '2', '3'});
 [g, gn, gl] = grp2idx (s);
 assert (g, [NaN; 2; NaN; 1]);
 assert (gn, {'1'; '2'; '3'});
 assert (iscategorical (gl), true);
 assert (cellstr (gl), gn);
***** test
 g = gn = gl = [];
 [g, gn, gl] = grp2idx (seconds ([1.234, 1.234, 2.5, 3.000]));
 assert (g, [1; 1; 2; 3]);
 assert (gn, {'1.234 sec'; '2.5 sec'; '3 sec'});
 assert (isequal (gl, seconds ([1.234; 2.5; 3.000])));
***** test
 [g, gn, gl] = grp2idx ([hours(1); hours(2); hours(1); hours(3)]);
 assert (g, [1; 2; 1; 3]);
 assert (gn, {'1 hr'; '2 hr'; '3 hr'});
 assert (isequal (gl, [hours(1); hours(2); hours(3)]));
***** test
 in = [duration(1, 30, 0); duration(0, 45, 30); duration(1, 30, 0); duration(2, 15, 15)];
 [g, gn, gl] = grp2idx(in);
 assert (g, [2; 1; 2; 3]);
 assert (gn, {'00:45:30'; '01:30:00'; '02:15:15'});
 assert (isequal (gl, [duration(0, 45, 30); duration(1, 30, 0); duration(2, 15, 15)]));
***** test
 in = [hours(1); NaN; minutes(30); hours(1); NaN; seconds(90)];
 [g, gn, gl] = grp2idx(in);
 assert (g, [3; NaN; 2; 3; NaN; 1]);
 assert (gn, {'0.025 hr'; '0.5 hr'; '1 hr'});
 assert (isequal(gl, [seconds(90); minutes(30);  hours(1)]));
***** test
 [g, gn, gl] = grp2idx (string ({'123', 'erw', missing, '', '234'}));
 assert (g, [1; 2; NaN; NaN; 3]);
 assert (gn, {'123'; 'erw'; '234'});
 assert (isequal (gl, string ({'123'; 'erw'; '234'})));
***** test
 [g, gn, gl] = grp2idx (string ({'medium', 'low', 'high', 'medium', 'medium'}));
 assert (g, [1; 2; 3; 1; 1]);
 assert (gn, {'medium'; 'low'; 'high'});
 assert (isequal (gl, string ({'medium'; 'low'; 'high'})));
***** test
 [g, gn, gl] = grp2idx (string ({'', 'high', 'low', ''}));
 assert (g, [NaN; 1; 2; NaN]);
 assert (gn, {'high'; 'low'});
 assert (isequal (gl, string ({'high'; 'low'})));
***** test
 [g, gn, gl] = grp2idx (string ({'a', 'a', 'b', 'c'}));
 assert (g, [1; 1; 2; 3]);
 assert (gn, {'a'; 'b'; 'c'});
 assert (isstring (gl), true);
 assert (cellstr (gl), gn);
***** error <grp2idx: S must be either a vector or a matrix.> grp2idx (ones (3, 3, 3))
***** error <grp2idx: 'categorical' grouping variable must be a vector.> ...
 grp2idx (categorical ([1, 2; 1, 3]))
***** error <grp2idx: 'datetime' grouping variable is not supported yet.> ...
 grp2idx (datetime ('now'))
__datetime__: TZDB error: Could not get current timezone
Falling back to UTC.
***** error <grp2idx: 'duration' grouping variable must be a vector.> ...
 grp2idx (days ([1, 2; 1, 3]))
***** error <grp2idx: 'string' grouping variable must be a vector.> ...
 grp2idx (string ({'a', 'a'; 'b', 'c'}))
***** error <grp2idx: unsupported type for input S.> grp2idx ({1})
43 tests, 43 passed, 0 known failure, 0 skipped
[inst/stepwisefit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/stepwisefit.m
***** test
 X = [7 26 6 60;
      1 29 15 52;
      11 56 8 20;
      11 31 8 47;
      7 52 6 33;
      11 55 9 22;
      3 71 17 6;
      1 31 22 44;
      2 54 18 22;
      21 47 4 26;
      1 40 23 34;
      11 66 9 12;
      10 68 8 12];
 y = [78.5; 74.3; 104.3; 87.6; 95.9; 109.2;
      102.7; 72.5; 93.1; 115.9; 83.8; 113.3; 109.4];
 [b,se,pval,finalmodel,stats] = stepwisefit (X,y);
 assert (finalmodel, [true false false true]);
 assert (b, [1.4400; 0.4161; -0.4100; -0.6140], 1e-4);
 assert (se, [0.1384; 0.1856; 0.1992; 0.0486], 1e-4);
 assert (pval, [0; 0.0517; 0.0697; 0], 1e-4);
 assert (stats.rmse, 2.7343, 1e-4);
 assert (stats.SStotal, 2715.7631, 1e-3);
 assert (stats.SSresid, 74.7621, 1e-4);
 assert (stats.df0, 2);
 assert (stats.dfe, 10);
 assert (stats.intercept, 103.0974, 1e-4);
***** test
 X = [
   12.0 4 120 95 2600;
   11.5 6 200 110 3000;
   10.5 8 300 150 3600;
   13.0 4 140 100 2800;
   12.5 6 180 120 3200;
   11.0 8 250 140 3500;
   14.0 4 130 98 2700;
   13.5 6 210 115 3100;
   12.2 8 320 160 3800;
   11.8 4 150 105 2900
 ];
 y = [28; 22; 18; 27; 23; 19; 29; 21; 17; 26];

 [b,se,pval,finalmodel,stats] = stepwisefit (X,y);

 assert (islogical (finalmodel));
 assert (numel (finalmodel) == 5);
 assert (sum (finalmodel) >= 1);
 assert (isnumeric (b));
 assert (isnumeric (se));
 assert (isnumeric (pval));
 assert (stats.rmse > 0);
 assert (isfinite (stats.intercept));
***** test
 X = randn (30, 4);
 y = randn (30, 1);
 [~,~,~,~,stats] = stepwisefit (X, y);

 required_fields = {
   "source", "df0", "dfe", "SStotal", "SSresid", "fstat", "pval", ...
   "rmse", "xr", "yr", "B", "SE", "TSTAT", "PVAL", "covb", ...
   "intercept", "wasnan"
 };

 for k = 1:numel (required_fields)
   assert (isfield (stats, required_fields{k}));
 endfor
***** test
 X = randn (40, 5);
 y = randn (40, 1);
 [b,se,pval,finalmodel,stats] = stepwisefit (X, y);

 p = columns (X);
 n = rows (X(~stats.wasnan, :));

 assert (size (stats.yr), [n, 1]);
 assert (rows (stats.B) == p);
 assert (rows (stats.SE) == p);
 assert (rows (stats.TSTAT) == p);
 assert (rows (stats.PVAL) == p);
 assert (size (stats.covb), [p+1, p+1]);
***** test
 X = randn (25, 3);
 y = randn (25, 1);
 [~,~,~,~,stats] = stepwisefit (X, y);

 SSresid_calc = sum (stats.yr .^ 2);
 assert (SSresid_calc, stats.SSresid, 1e-10);

 rmse_calc = sqrt (stats.SSresid / stats.dfe);
 assert (rmse_calc, stats.rmse, 1e-10);
***** test
 X = randn (50, 6);
 y = randn (50, 1);
 [~,~,~,~,stats] = stepwisefit (X, y);

 if (stats.df0 > 0)
   F_calc = ((stats.SStotal - stats.SSresid) / stats.df0) ...
            / (stats.SSresid / stats.dfe);

   assert (F_calc, stats.fstat, 1e-10);
   assert (stats.pval >= 0 && stats.pval <= 1);
 else
   assert (isnan (stats.fstat));
   assert (isnan (stats.pval));
 endif
***** test
 X = randn (35, 4);
 y = randn (35, 1);
 [~,~,~,finalmodel,stats] = stepwisefit (X, y);
 p = columns (X);
 k = sum (finalmodel);
 assert (size (stats.xr, 2) == p - k);
 assert (all (isfinite (stats.xr(:))));
***** test
 X = randn (35, 4);
 y = randn (35, 1);
 [~,~,~,finalmodel,stats] = stepwisefit (X, y);

 Xc = X(~stats.wasnan, :);
 Xfinal = [ones(rows (Xc),1), Xc(:, finalmodel)];

 for j = 1:columns (stats.xr)
   ortho = Xfinal' * stats.xr(:,j);
   assert (max (abs (ortho(:))) < 1e-6);
 endfor
***** test
 X = randn (40, 5);
 y = randn (40, 1);
 [~,~,~,finalmodel,stats,nextstep,history] = stepwisefit (X, y);

 assert (nextstep == 0);
 assert (isstruct (history));
 assert (isfield (history, "in"));
 assert (isfield (history, "df0"));
 assert (isfield (history, "rmse"));
 assert (isfield (history, "B"));
 assert (isequal (history.in, finalmodel));
 assert (history.df0 == stats.df0);
 assert (history.rmse == stats.rmse);
 assert (rows (history.B) == columns (X));
***** test
 X = randn (20,4);
 y = randn (20,1);
 stepwisefit (X,y,'Keep',[true false true false]);
***** test
 X = randn (30, 4);
 y = randn (30, 1);
 keep = [true false false false];
 [~,~,~,finalmodel] = stepwisefit (X, y, "Keep", keep);
 assert (finalmodel(1) == true);
***** test
 X = randn (40, 6);
 y = randn (40, 1);
 [~,~,~,finalmodel] = stepwisefit (X, y, "MaxIter", 1);
 assert (islogical (finalmodel));
***** test
 X = randn (50, 5);
 y = randn (50, 1);
 [b1] = stepwisefit (X, y);
 [b2] = stepwisefit (X, y, "Scale", "on");
 assert (rows (b1) == rows (b2));
***** test
 X = randn (20,4);
 y = randn (20,1);
 fail ("stepwisefit (X,y,'Keep',[true false])");
***** error <stepwisefit: at least two input arguments required> ...
       stepwisefit ()
***** error <stepwisefit: X must be a matrix and y a vector> ...
       stepwisefit (ones (2,2,2), [1;2])
***** error <stepwisefit: X must be a matrix and y a vector> ...
       stepwisefit (ones (3,2), ones (2,1))
***** error <stepwisefit: unrecognized input arguments> ...
       stepwisefit (randn (10,2), randn (10,1), "UnknownOpt", 5)
***** error <stepwisefit: Display must be 'on' or 'off'> ...
       stepwisefit (randn (10,2), randn (10,1), "Display", "maybe")
***** error <stepwisefit: Scale must be 'on' or 'off'> ...
       stepwisefit (randn (10,2), randn (10,1), "Scale", 123)
***** error <stepwisefit: PEnter must be a scalar strictly between 0 and 1> ...
       stepwisefit (randn (10,2), randn (10,1), "PEnter", -0.1)
***** error <stepwisefit: PRemove must be a scalar strictly between 0 and 1> ...
       stepwisefit (randn (10,2), randn (10,1), "PRemove", 1.5)
***** error <stepwisefit: PRemove must be greater than or equal to PEnter> ...
       stepwisefit (randn (10,2), randn (10,1), ...
                     "PEnter", 0.05, "PRemove", 0.01)
***** error <stepwisefit: MaxIter must be a positive integer> ...
       stepwisefit (randn (10,2), randn (10,1), "MaxIter", -2)
***** error <stepwisefit: MaxIter must be a positive integer> ...
       stepwisefit (randn (10,2), randn (10,1), "MaxIter", 2.5)
***** error <stepwisefit: Keep must be a logical vector> ...
       stepwisefit (randn (10,2), randn (10,1), "Keep", [1 0])
***** error <stepwisefit: InModel must be a logical vector> ...
       stepwisefit (randn (10,2), randn (10,1), "InModel", [1 0])
***** error <stepwisefit: Keep length must match number of predictors> ...
       stepwisefit (randn (10,4), randn (10,1), "Keep", [true false])
***** error <stepwisefit: InModel length must match number of predictors> ...
       stepwisefit (randn (10,4), randn (10,1), "InModel", true)
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/runstest.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/runstest.m
***** test
 ## NIST beam deflection data
 ## http://www.itl.nist.gov/div898/handbook/eda/section4/eda425.htm
 data = [-213, -564, -35, -15, 141, 115, -420, -360, 203, -338, -431, ...
          194, -220, -513, 154, -125, -559, 92, -21, -579, -52, 99, -543, ...
         -175, 162, -457, -346, 204, -300, -474, 164, -107, -572, -8, 83, ...
         -541, -224, 180, -420, -374, 201, -236, -531, 83, 27, -564, -112, ...
          131, -507, -254, 199, -311, -495, 143, -46, -579, -90, 136, ...
         -472, -338, 202, -287, -477, 169, -124, -568, 17, 48, -568, -135, ...
          162, -430, -422, 172, -74, -577, -13, 92, -534, -243, 194, -355, ...
         -465, 156, -81, -578, -64, 139, -449, -384, 193, -198, -538, 110, ...
          -44, -577, -6, 66, -552, -164, 161, -460, -344, 205, -281, -504, ...
          134, -28, -576, -118, 156, -437, -381, 200, -220, -540, 83, 11, ...
         -568, -160, 172, -414, -408, 188, -125, -572, -32, 139, -492, ...
         -321, 205, -262, -504, 142, -83, -574, 0, 48, -571, -106, 137, ...
         -501, -266, 190, -391, -406, 194, -186, -553, 83, -13, -577, -49, ...
          103, -515, -280, 201, 300, -506, 131, -45, -578, -80, 138, -462, ...
         -361, 201, -211, -554, 32, 74, -533, -235, 187, -372, -442, 182, ...
         -147, -566, 25, 68, -535, -244, 194, -351, -463, 174, -125, -570, ...
           15, 72, -550, -190, 172, -424, -385, 198, -218, -536, 96];
 [h, p, stats] = runstest (data, median (data));
 expected_h = 1;
 expected_p = 0.008562;
 expected_z = 2.6229;
 assert (h, expected_h);
 assert (p, expected_p, 1E-6);
 assert (stats.z, expected_z, 1E-4);
***** shared x
 x = [45, -60, 1.225, 55.4, -9 27];
***** test
 [h, p, stats] = runstest (x);
 assert (h, 0);
 assert (p, 0.6, 1e-14);
 assert (stats.nruns, 5);
 assert (stats.n1, 3);
 assert (stats.n0, 3);
 assert (stats.z, 0.456435464587638, 1e-14);
***** test
 [h, p, stats] = runstest (x, [], "method", "approximate");
 assert (h, 0);
 assert (p, 0.6481, 1e-4);
 assert (stats.z, 0.456435464587638, 1e-14);
***** test
 [h, p, stats] = runstest (x, [], "tail", "left");
 assert (h, 0);
 assert (p, 0.9, 1e-14);
 assert (stats.z, 1.369306393762915, 1e-14);
***** error<runstest: X must be a vector a scalar values.> runstest (ones (2,20))
***** error<runstest: X must be a vector a scalar values.> runstest (["asdasda"])
***** error<runstest: V must be either a scalar number or> ...
 runstest ([2 3 4 3 2 3 4], "updown")
***** error<runstest: invalid value for alpha.> ...
 runstest ([2 3 4 3 2 3 4], [], "alpha", 0)
***** error<runstest: invalid value for alpha.> ...
 runstest ([2 3 4 3 2 3 4], [], "alpha", [0.02 0.2])
***** error<runstest: invalid value for alpha.> ...
 runstest ([2 3 4 3 2 3 4], [], "alpha", 1.2)
***** error<runstest: invalid value for alpha.> ...
 runstest ([2 3 4 3 2 3 4], [], "alpha", -0.05)
***** error<runstest: invalid value for method.> ...
 runstest ([2 3 4 3 2 3 4], [], "method", "some")
***** error<runstest: invalid value for tail.> ...
 runstest ([2 3 4 3 2 3 4], [], "tail", "some")
***** error<runstest: invalid optional argument.> ...
 runstest ([2 3 4 3 2 3 4], [], "option", "some")
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/logit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/logit.m
***** test
 p = [0.01:0.01:0.99];
 assert (logit (p), log (p ./ (1-p)), 25*eps);
***** assert (logit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, +Inf, NaN])
***** error logit ()
***** error logit (1, 2)
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/cl_multinom.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/cl_multinom.m
***** demo
 CL = cl_multinom ([27; 43; 19; 11], 10000, 0.05)
***** error<Invalid call to cl_multinom.  Correct usage> cl_multinom ();
***** error cl_multinom (1, 2, 3, 4, 5);
***** error<cl_multinom: argument method must be a string.> ...
 cl_multinom (1, 2, 3, 4);
***** error<cl_multinom: unknown calculation type.> ...
 cl_multinom (1, 2, 3, "some string");
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/ztest.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/ztest.m
***** error<ztest: too few input arguments.> ztest ();
***** error<ztest: invalid value for standard deviation.> ...
 ztest ([1, 2, 3, 4], 2, -0.5);
***** error<ztest: invalid VALUE for alpha.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 0);
***** error<ztest: invalid VALUE for alpha.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 1.2);
***** error<ztest: invalid VALUE for alpha.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", "val");
***** error<ztest: invalid VALUE for tail.>  ...
 ztest ([1, 2, 3, 4], 1, 2, "tail", "val");
***** error<ztest: invalid VALUE for tail.>  ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "val");
***** error<ztest: invalid VALUE for operating dimension.> ...
 ztest ([1, 2, 3, 4], 1, 2, "dim", 3);
***** error<ztest: invalid VALUE for operating dimension.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "dim", 3);
***** error<ztest: invalid NAME for optional arguments.> ...
 ztest ([1, 2, 3, 4], 1, 2, "alpha", 0.01, "tail", "both", "badoption", 3);
***** test
 load carsmall
 [h, pval, ci] = ztest (MPG, mean (MPG, "omitnan"), std (MPG, "omitnan"));
 assert (h, 0);
 assert (pval, 1, 1e-14);
 assert (ci, [22.094; 25.343], 1e-3);
***** test
 load carsmall
 [h, pval, ci] = ztest (MPG, 26, 8);
 assert (h, 1);
 assert (pval, 0.00568359158544743, 1e-14);
 assert (ci, [22.101; 25.335], 1e-3);
***** test
 load carsmall
 [h, pval, ci] = ztest (MPG, 26, 4);
 assert (h, 1);
 assert (pval, 3.184168011941316e-08, 1e-14);
 assert (ci, [22.909; 24.527], 1e-3);
***** test
 x = normrnd (10, 2, 100, 1);
 [h, pval, ci] = ztest (x, 10, 2, "tail", "right");
 assert (isnan (pval), false);
 assert (pval >= 0 && pval <= 1, true);
***** test
 x = normrnd (10, 2, 100, 1);
 [h, pval, ci] = ztest (x, 10, 2, "tail", "left");
 assert (isnan (pval), false);
 assert (pval >= 0 && pval <= 1, true);
***** test
 load fisheriris;
 x = meas(:,1);
 m = 5.8;
 sigma = 0.8;
 [h, pval, ci] = ztest (x, m, sigma, "tail", "right");
 assert (h, 0)
 assert (pval, 0.2535, 1e-4)
 assert (ci, [5.7359; Inf], 1e-5)
***** test
 load fisheriris;
 x = meas(:,1);
 m = 5.8;
 sigma = 0.8;
 [h, pval, ci] = ztest (x, m, sigma, "tail", "left");
 assert (h, 0)
 assert (pval, 0.7465, 1e-4)
 assert (ci, [-Inf; 5.9508], 1e-4)    
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/dcov.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dcov.m
***** demo
 base=@(x) (x- min(x))./(max(x)-min(x));
 N = 5e2;
 x = randn (N,1); x = base (x);
 z = randn (N,1); z = base (z);
 # Linear relations
 cy = [1 0.55 0.3 0 -0.3 -0.55 -1];
 ly = x .* cy;
 ly(:,[1:3 5:end]) = base (ly(:,[1:3 5:end]));
 # Correlated Gaussian
 cz = 1 - abs (cy);
 gy = base ( ly + cz.*z);
 # Shapes
 sx      = repmat (x,1,7);
 sy      = zeros (size (ly));
 v       = 2 * rand (size(x,1),2) - 1;
 sx(:,1) = v(:,1); sy(:,1) = cos(2*pi*sx(:,1)) + 0.5*v(:,2).*exp(-sx(:,1).^2/0.5);
 R       =@(d) [cosd(d) sind(d); -sind(d) cosd(d)];
 tmp     = R(35) * v.';
 sx(:,2) = tmp(1,:); sy(:,2) = tmp(2,:);
 tmp     = R(45) * v.';
 sx(:,3) = tmp(1,:); sy(:,3) = tmp(2,:);
 sx(:,4) = v(:,1); sy(:,4) = sx(:,4).^2 + 0.5*v(:,2);
 sx(:,5) = v(:,1); sy(:,5) = 3*sign(v(:,2)).*(sx(:,5)).^2  + v(:,2);
 sx(:,6) = cos (2*pi*v(:,1)) + 0.5*(x-0.5);
 sy(:,6) = sin (2*pi*v(:,1)) + 0.5*(z-0.5);
 sx(:,7) = x + sign(v(:,1)); sy(:,7) = z + sign(v(:,2));
 sy      = base (sy);
 sx      = base (sx);
 # scaled shape
 sc  = 1/3;
 ssy = (sy-0.5) * sc + 0.5;
 n = size (ly,2);
 ym = 1.2;
 xm = 0.5;
 fmt={'horizontalalignment','center'};
 ff = "% .2f";
 figure (1)
 for i=1:n
   subplot(4,n,i);
   plot (x, gy(:,i), '.b');
   axis tight
   axis off
   text (xm,ym,sprintf (ff, dcov (x,gy(:,i))),fmt{:})

   subplot(4,n,i+n);
   plot (x, ly(:,i), '.b');
   axis tight
   axis off
   text (xm,ym,sprintf (ff, dcov (x,ly(:,i))),fmt{:})

   subplot(4,n,i+2*n);
   plot (sx(:,i), sy(:,i), '.b');
   axis tight
   axis off
   text (xm,ym,sprintf (ff, dcov (sx(:,i),sy(:,i))),fmt{:})
   v = axis ();

   subplot(4,n,i+3*n);
   plot (sx(:,i), ssy(:,i), '.b');
   axis (v)
   axis off
   text (xm,ym,sprintf (ff, dcov (sx(:,i),ssy(:,i))),fmt{:})
 endfor
***** error dcov (randn (30, 5), randn (25,5))
1 test, 1 passed, 0 known failure, 0 skipped
[inst/normalise_distribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/normalise_distribution.m
***** test
 v = normalise_distribution ([1 2 3], [], 1);
 assert (v, [0 0 0])
***** test
 v = normalise_distribution ([1 2 3], [], 2);
 assert (v, norminv ([1 3 5] / 6), 3 * eps)
***** test
 v = normalise_distribution ([1 2 3]', [], 2);
 assert (v, [0 0 0]')
***** test
 v = normalise_distribution ([1 2 3]', [], 1);
 assert (v, norminv ([1 3 5]' / 6), 3 * eps)
***** test
 v = normalise_distribution ([1 1 2 2 3 3], [], 2);
 assert (v, norminv ([3 3 7 7 11 11] / 12), 3 * eps)
***** test
 v = normalise_distribution ([1 1 2 2 3 3]', [], 1);
 assert (v, norminv ([3 3 7 7 11 11]' / 12), 3 * eps)
***** test
 A = randn ( 10 );
 N = normalise_distribution (A, @normcdf);
 assert (A, N, 10000 * eps)
***** test
 A = exprnd (1, 100);
 N = normalise_distribution (A, @(x)(expcdf (x, 1)));
 assert (mean (vec (N)), 0, 0.1)
 assert (std (vec (N)), 1, 0.1)
***** test
 A = rand (1000,1);
 N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1))});
 assert (mean (vec (N)), 0, 0.2)
 assert (std (vec (N)), 1, 0.1)
***** test
 A = [rand(1000,1), randn(1000, 1)];
 N = normalise_distribution (A, {@(x)(unifcdf (x, 0, 1)), @normcdf});
 assert (mean (N), [0, 0], 0.2)
 assert (std (N), [1, 1], 0.1)
***** test
 A = [rand(1000,1), randn(1000, 1), exprnd(1, 1000, 1)]';
 N = normalise_distribution  (A, {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x, 1))}, 2);
 assert (mean (N, 2), [0, 0, 0]', 0.2);
 assert (std (N, [], 2), [1, 1, 1]', 0.1);
***** xtest
 A = exprnd (1, 1000, 9); A (300:500, 4:6) = 17;
 N = normalise_distribution (A);
 assert (mean (N), [0 0 0 0.38 0.38 0.38 0 0 0], 0.1);
 assert (var (N), [1 1 1 2.59 2.59 2.59 1 1 1], 0.1);
***** test
***** error normalise_distribution (zeros (3, 4), ...
 {@(x)(unifcdf (x, 0, 1)); @normcdf; @(x)(expcdf (x,1))});
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/pdist2.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/pdist2.m
***** shared x, y, xx
 x = [1, 1, 1; 2, 2, 2; 3, 3, 3];
 y = [0, 0, 0; 1, 2, 3; 0, 2, 4; 4, 7, 1];
 xx = [1 2 3; 4 5 6; 7 8 9; 3 2 1];
***** test
 d = sqrt([3, 5, 11, 45; 12, 2, 8, 30; 27, 5, 11, 21]);
 assert (pdist2 (x, y), d);
***** test
 d = [5.1962, 2.2361, 3.3166, 6.7082; ...
      3.4641, 2.2361, 3.3166, 5.4772];
 i = [3, 1, 1, 1; 2, 3, 3, 2];
 [D, I] = pdist2 (x, y, "euclidean", "largest", 2);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 d = [1.7321, 1.4142, 2.8284, 4.5826; ...
      3.4641, 2.2361, 3.3166, 5.4772];
 i = [1, 2, 2, 3;2, 1, 1, 2];
 [D, I] = pdist2 (x, y, "euclidean", "smallest", 2);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 yy = [1 2 3;5 6 7;9 5 1];
 d = [0, 6.1644, 5.3852; 1.4142, 6.9282, 8.7750; ...
      3.7417, 7.0711, 9.9499; 6.1644, 10.4881, 10.3441];
 i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
 [D, I] = pdist2 (y, yy, "euclidean", "smallest", 4);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 yy = [1 2 3;5 6 7;9 5 1];
 d = [0, 38, 29; 2, 48, 77; 14, 50, 99; 38, 110, 107];
 i = [2, 4, 4; 3, 2, 2; 1, 3, 3; 4, 1, 1];
 [D, I] = pdist2 (y, yy, "squaredeuclidean", "smallest", 4);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 yy = [1 2 3;5 6 7;9 5 1];
 d = [0, 3.3256, 2.7249; 0.7610, 3.3453, 4.4799; ...
      1.8514, 3.3869, 5.0703; 2.5525, 5.0709, 5.1297];
 i = [2, 2, 4; 3, 4, 2; 1, 3, 1; 4, 1, 3];
 [D, I] = pdist2 (y, yy, "seuclidean", "smallest", 4);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 d = [2.1213, 4.2426, 6.3640; 1.2247, 2.4495, 4.4159; ...
      3.2404, 4.8990, 6.8191; 2.7386, 4.2426, 6.1237];
 assert (pdist2 (y, x, "mahalanobis"), d, 1e-4);
***** test
 xx = [1, 3, 4; 3, 5, 4; 8, 7, 6];
 d = [1.3053, 1.8257, 15.0499; 1.3053, 3.3665, 16.5680];
 i = [2, 2, 2; 3, 4, 4];
 [D, I] = pdist2 (y, xx, "mahalanobis", "smallest", 2);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 d = [2.5240, 4.1633, 17.3638; 2.0905, 3.9158, 17.0147];
 i = [1, 1, 3; 4, 3, 1];
 [D, I] = pdist2 (y, xx, "mahalanobis", "largest", 2);
 assert ({D, I}, {d, i}, 1e-4);
***** test
 d = [3, 3, 5, 9; 6, 2, 4, 8; 9, 3, 5, 7];
 assert (pdist2 (x, y, "cityblock"), d);
***** test
 d = [1, 2, 3, 6; 2, 1, 2, 5; 3, 2, 3, 4];
 assert (pdist2 (x, y, "chebychev"), d);
***** test
 d = repmat ([NaN, 0.0742, 0.2254, 0.1472], [3, 1]);
 assert (pdist2 (x, y, "cosine"), d, 1e-4);
***** test
 yy = [1 2 3;5 6 7;9 5 1];
 d = [0, 0, 0.5; 0, 0, 2; 1.5, 1.5, 2; NaN, NaN, NaN];
 i = [2, 2, 4; 3, 3, 2; 4, 4, 3; 1, 1, 1];
 [D, I] = pdist2 (y, yy, "correlation", "smallest", 4);
 assert ({D, I}, {d, i}, eps);
 [D, I] = pdist2 (y, yy, "spearman", "smallest", 4);
 assert ({D, I}, {d, i}, eps);
***** test
 d = [1, 2/3, 1, 1; 1, 2/3, 1, 1; 1, 2/3, 2/3, 2/3];
 i = [1, 1, 1, 2; 2, 2, 3, 3; 3, 3, 2, 1];
 [D, I] = pdist2 (x, y, "hamming", "largest", 4);
 assert ({D, I}, {d, i}, eps);
 [D, I] = pdist2 (x, y, "jaccard", "largest", 4);
 assert ({D, I}, {d, i}, eps);
***** test
 xx = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
 yy = [1, 2, 2, 3; 2, 3, 3, 4];
 [D, I] = pdist2 (x, y, "euclidean", "Smallest", 4);
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [d, i] = pdist2 (x, y, eucldist, "Smallest", 4);
 assert ({D, I}, {d, i});
***** warning<pdist2: matrix is close to singular> ...
 pdist2 (xx, xx, "mahalanobis");
***** error<pdist2: too few input arguments.> pdist2 (1)
***** error<pdist2: X and Y must have equal number of columns.> ...
 pdist2 (ones (4, 5), ones (4))
***** error<pdist2: X and Y must be 2 dimensional matrices.> ...
 pdist2 (ones (4, 2, 3), ones (3, 2))
***** error<pdist2: missing value in optional name/value paired arguments.> ...
 pdist2 (ones (3), ones (3), "euclidean", "Largest")
***** error<pdist2: missing value in optional name/value paired arguments.> ...
 pdist2 (ones (3), ones (3), "minkowski", 3, "Largest")
***** error<pdist2: invalid NAME in optional pairs of arguments.> ...
 pdist2 (ones (3), ones (3), "minkowski", 3, "large", 4)
***** error<pdist2: K must be a positive integer value.> ...
 pdist2 (ones (3), ones (3), "minkowski", 3, "largest", 4.5)
***** error<pdist2: you can only specify either 'Smallest' or 'Largest'.> ...
 pdist2 (ones (3), ones (3), "minkowski", 3, "Largest", 4, "smallest", 5)
***** error<pdist2: 'Smallest' or 'Largest' must be specified to compute second output.> ...
 [d, i] = pdist2(ones (3), ones (3), "minkowski", 3)
***** error<pdist2: DistParameter for standardized euclidean must be a vector of> ...
 pdist2 (ones (3), ones (3), "seuclidean", 3)
***** error<pdist2: DistParameter for standardized euclidean must be a nonnegative> ...
 pdist2 (ones (3), ones (3), "seuclidean", [1, -1, 3])
***** error<pdist2: DistParameter for mahalanobis distance must be a covariance> ...
 pdist2 (ones (3), eye (3), "mahalanobis", eye(2))
***** error<pdist2: covariance matrix for mahalanobis distance must be symmetric> ...
 pdist2 (ones (3), eye (3), "mahalanobis", ones(3))
***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
 pdist2 (ones (3), eye (3), "minkowski", 0)
***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
 pdist2 (ones (3), eye (3), "minkowski", -5)
***** error<pdist2: DistParameter for minkowski distance must be a positive scalar.> ...
 pdist2 (ones (3), eye (3), "minkowski", [1, 2])
***** error<pdist2: invalid function handle for distance metric.> ...
 pdist2 (ones (3), ones (3), @(v,m) sqrt(repmat(v,rows(m),1)-m,2))
***** error<pdist2: custom distance function produces wrong output size.> ...
 pdist2 (ones (3), ones (3), @(v,m) sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
34 tests, 34 passed, 0 known failure, 0 skipped
[inst/hmmestimate.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/hmmestimate.m
***** test
 sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, ...
             3, 3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
 states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, ...
             1, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
 [transprobest, outprobest] = hmmestimate (sequence, states);
 expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
 expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
 assert (transprobest, expectedtransprob, 0.001);
 assert (outprobest, expectedoutprob, 0.001);
***** test
 sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
             "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
 states = {"One", "One", "Two", "Two", "Two", "One", "One", "One", "One", ...
           "One", "One", "One", "One", "One", "One", "Two", "Two", "Two", ...
           "Two", "One", "One", "One", "One", "One", "One"};
 symbols = {"A", "B", "C"};
 statenames = {"One", "Two"};
 [transprobest, outprobest] = hmmestimate (sequence, states, "symbols", ...
                                           symbols, "statenames", statenames);
 expectedtransprob = [0.88889, 0.11111; 0.28571, 0.71429];
 expectedoutprob = [0.16667, 0.33333, 0.50000; 1.00000, 0.00000, 0.00000];
 assert (transprobest, expectedtransprob, 0.001);
 assert (outprobest, expectedoutprob, 0.001);
***** test
 sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
             3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
 states =   [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
             1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
 pseudotransitions = [8, 2; 4, 6];
 pseudoemissions = [2, 4, 4; 7, 2, 1];
 [transprobest, outprobest] = hmmestimate (sequence, states, ...
  "pseudotransitions", pseudotransitions, "pseudoemissions", pseudoemissions);
 expectedtransprob = [0.85714, 0.14286; 0.35294, 0.64706];
 expectedoutprob = [0.178571, 0.357143, 0.464286; ...
                    0.823529, 0.117647, 0.058824];
 assert (transprobest, expectedtransprob, 0.001);
 assert (outprobest, expectedoutprob, 0.001);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/randsample.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/randsample.m
***** test
 n = 20;
 k = 5;
 x = randsample(n, k);
 assert (size(x), [1 k]);
 x = randsample(n, k, true);
 assert (size(x), [1 k]);
 x = randsample(n, k, false);
 assert (size(x), [1 k]);
 x = randsample(n, k, true, ones(n, 1));
 assert (size(x), [1 k]);
 x = randsample(1:n, k);
 assert (size(x), [1 k]);
 x = randsample(1:n, k, true);
 assert (size(x), [1 k]);
 x = randsample(1:n, k, false);
 assert (size(x), [1 k]);
 x = randsample(1:n, k, true, ones(n, 1));
 assert (size(x), [1 k]);
 x = randsample((1:n)', k);
 assert (size(x), [k 1]);
 x = randsample((1:n)', k, true);
 assert (size(x), [k 1]);
 x = randsample((1:n)', k, false);
 assert (size(x), [k 1]);
 x = randsample((1:n)', k, true, ones(n, 1));
 assert (size(x), [k 1]);
 n = 10;
 k = 100;
 x = randsample(n, k, true, 1:n);
 assert (size(x), [1 k]);
 x = randsample((1:n)', k, true);
 assert (size(x), [k 1]);
 x = randsample(k, k, false, 1:k);
 assert (size(x), [1 k]);
***** test
 n = 20;
 k = 5;
 p = 1:n;
 x = randsample(p, k);
 assert (isnumeric(x));
 assert (size(x), [1 k]);
 x = randsample(p, k, true);
 assert (isnumeric(x));
 assert (size(x), [1 k]);
 x = randsample(p, k, false);
 assert (isnumeric(x));
 assert (size(x), [1 k]);
 k = 30;
 x = randsample(p, k, true);
 assert (isnumeric(x));
 assert (size(x), [1 k]);
***** test
 p = categorical({'a', 'b', 'c', 'd', 'a'});
 k = 3;
 x = randsample(p, k, true);
 assert (iscategorical(x));
 assert (size(x), [1 k]);
 x = randsample(p, k, false);
 assert (iscategorical(x));
 assert (size(x), [1 k]);
 k = 30;
 x = randsample(p, k, true, ones(length(p),1));
 assert (iscategorical(x));
 assert (size(x), [1 k]);
***** test
 p = {'a', 'b', 'c', 'd', 'a'};
 k = 2;
 x = randsample(p, k, true);
 assert (iscell(x));
 assert (size(x), [1 k]);
 x = randsample(p, k, false);
 assert (iscell(x));
 assert (size(x), [1 k]);
 k = 30;
 x = randsample(p, k, true, ones(length(p),1));
 assert (iscell(x));
 assert (size(x), [1 k]);
***** test
 p = string({'a', 'b', 'c', 'd', 'a'});
 k = 2;
 x = randsample(p, k, true);
 assert (isstring(x));
 assert (size(x), [1 k]);
 x = randsample(p, k, false);
 assert (isstring(x));
 assert (size(x), [1 k]);
 k = 30;
 x = randsample(p, k, true, ones(length(p),1));
 assert (isstring(x));
 assert (size(x), [1 k]);
***** error <randsample: The input v must be a vector or positive integer.> ...
 randsample ([1 2 3; 1 2 3], 5)
***** error <randsample: The input k must be a non-negative integer.> ...
 randsample (10, -1)
***** error <Sampling without replacement needs k <= n.> ...
 randsample (10, 100)
***** error <randsample: the size w .* must match the first argument .*> ...
 randsample (10, 5, false, ones(5,1))
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/qrandn.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/qrandn.m
***** demo
 z = qrandn (-5, 5e6);
 [c x] = hist (z,linspace(-1.5,1.5,200),1);
 figure(1)
 plot(x,c,"r."); axis tight; axis([-1.5,1.5]);

 z = qrandn (-0.14286, 5e6);
 [c x] = hist (z,linspace(-2,2,200),1);
 figure(2)
 plot(x,c,"r."); axis tight; axis([-2,2]);

 z = qrandn (2.75, 5e6);
 [c x] = hist (z,linspace(-1e3,1e3,1e3),1);
 figure(3)
 semilogy(x,c,"r."); axis tight; axis([-100,100]);

 # ---------
 # Figures from the reference paper.
***** error<qrandn: the parameter q must be a scalar.> qrandn ([1 2], 1)
***** error<qrandn: the parameter q must be lower than 3.> qrandn (4, 1)
***** error<qrandn: the parameter q must be lower than 3.> qrandn (3, 1)
***** error qrandn (2.5, 1, 2, 3)
***** error qrandn (2.5)
***** test
 q = 1.5;
 s = [2, 3];
 z = qrandn (q, s);
 assert (isnumeric (z) && isequal (size (z), s));
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/dist_obj/NakagamiDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/NakagamiDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Nakagami distribution with
 ## parameters mu = 1 and omega = 1.  Fit a Nakagami distribution to this data and plot
 ## a PDF of the fitted distribution superimposed on a histogram of the data.

 pd_fixed = makedist ("Nakagami", "mu", 1, "omega", 1)
 rand ("seed", 2);
 data = random (pd_fixed, 5000, 1);
 pd_fitted = fitdist (data, "Nakagami")
 plot (pd_fitted)
 msg = "Fitted Nakagami distribution with mu = %0.2f and omega = %0.2f";
 title (sprintf (msg, pd_fitted.mu, pd_fitted.omega))
***** shared pd, t
 pd = NakagamiDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.9817, 0.9999, 1, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9933, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8946, 0.9817, 0.9999, 1], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9933, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.4724, 0.7147, 0.9572, 1.2686, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.0550, 2.1239, 2.2173, 2.3684, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7147, 0.9572, 1.2686, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.1239, 2.2173, 2.3684, 4, NaN], 1e-4);
***** assert (iqr (pd), 0.6411, 1e-4);
***** assert (iqr (t), 0.2502, 1e-4);
***** assert (mean (pd), 0.8862, 1e-4);
***** assert (mean (t), 2.2263, 1e-4);
***** assert (median (pd), 0.8326, 1e-4);
***** assert (median (t), 2.1664, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.7358, 0.0733, 0.0007, 0, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 4, 0.0404, 0, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.7358, 0.0733, 0.0007, 0, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 4, 0.0404, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.4633, 1e-4);
***** assert (std (t), 0.2083, 1e-4);
***** assert (var (pd), 0.2146, 1e-4);
***** assert (var (t), 0.0434, 1e-4);
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution(Inf, 1)
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution(i, 1)
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution("a", 1)
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution([1, 2], 1)
***** error <NakagamiDistribution: MU must be a real scalar of at least 0.5.> ...
 NakagamiDistribution(NaN, 1)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, 0)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, -1)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, Inf)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, i)
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, "a")
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, [1, 2])
***** error <NakagamiDistribution: OMEGA must be a positive real scalar.> ...
 NakagamiDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NakagamiDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NakagamiDistribution, 2, 3)
***** shared x
 x = nakarnd (1, 0.5, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (NakagamiDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NakagamiDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NakagamiDistribution.fit (x), "parameter", {"mu", "omega", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "omega", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (NakagamiDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NakagamiDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NakagamiDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (NakagamiDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NakagamiDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (NakagamiDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NakagamiDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NakagamiDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NakagamiDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NakagamiDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NakagamiDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NakagamiDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (NakagamiDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (NakagamiDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (NakagamiDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NakagamiDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NakagamiDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NakagamiDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (NakagamiDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (NakagamiDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NakagamiDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (NakagamiDistribution)
***** error <truncate: missing input argument.> ...
 truncate (NakagamiDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (NakagamiDistribution, 4, 2)
***** shared pd
 pd = NakagamiDistribution(1, 0.5);
 pd(2) = NakagamiDistribution(1, 0.6);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/ExtremeValueDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/ExtremeValueDistribution.m
***** shared pd, t
 pd = ExtremeValueDistribution (0, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0.6321, 0.9340, 0.9994, 1, 1, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 1, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9887, 0.9994, 1, 1], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 1, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.4999, -0.6717, -0.0874, 0.4759, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.0298, 2.0668, 2.1169, 2.1971, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.6717, -0.0874, 0.4759, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.0668, 2.1169, 2.1971, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.5725, 1e-4);
***** assert (iqr (t), 0.1338, 1e-4);
***** assert (mean (pd), -0.5772, 1e-4);
***** assert (mean (t), 2.1206, 1e-4);
***** assert (median (pd), -0.3665, 1e-4);
***** assert (median (t), 2.0897, 1e-4);
***** assert (pdf (pd, [0:5]), [0.3679, 0.1794, 0.0046, 0, 0, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 7.3891, 0.0001, 0, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2546, 0.1794, 0.0046, 0, 0, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 7.3891, 0.0001, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.2825, 1e-4);
***** assert (std (t), 0.1091, 1e-4);
***** assert (var (pd), 1.6449, 1e-4);
***** assert (var (t), 0.0119, 1e-4);
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution(Inf, 1)
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution(i, 1)
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution("a", 1)
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution([1, 2], 1)
***** error <ExtremeValueDistribution: MU must be a real scalar.> ...
 ExtremeValueDistribution(NaN, 1)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, 0)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, -1)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, Inf)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, i)
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, "a")
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, [1, 2])
***** error <ExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 ExtremeValueDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (ExtremeValueDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (ExtremeValueDistribution, 2, 3)
***** shared x
 rand ("seed", 1);
 x = evrnd (1, 1, [1000, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), ...
          "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (ExtremeValueDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (ExtremeValueDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (ExtremeValueDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (ExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (ExtremeValueDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExtremeValueDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExtremeValueDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExtremeValueDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (ExtremeValueDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (ExtremeValueDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (ExtremeValueDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (ExtremeValueDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (ExtremeValueDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExtremeValueDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExtremeValueDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExtremeValueDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (ExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (ExtremeValueDistribution)
***** error <truncate: missing input argument.> ...
 truncate (ExtremeValueDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (ExtremeValueDistribution, 4, 2)
***** shared pd
 pd = ExtremeValueDistribution(1, 1);
 pd(2) = ExtremeValueDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/LoglogisticDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/LoglogisticDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Log-logistic
 ## distribution with parameters mu = 0 and sigma = 1.  Fit a Log-logistic
 ## distribution to this data and plot a PDF of the fitted distribution
 ## superimposed on a histogram of the data.

 pd_fixed = makedist ("Loglogistic", "mu", 0, "sigma", 1)
 rand ("seed", 2);
 data = random (pd_fixed, 5000, 1);
 pd_fitted = fitdist (data, "Loglogistic")
 plot (pd_fitted)
 msg = "Fitted Log-logistic distribution with mu = %0.2f and sigma = %0.2f";
 title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma))
***** shared pd, t
 pd = LoglogisticDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.6667, 1e-4);
***** assert (iqr (t), 0.9524, 1e-4);
***** assert (mean (pd), Inf);
***** assert (mean (t), 2.8312, 1e-4);
***** assert (median (pd), 1, 1e-4);
***** assert (median (t), 2.75, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), Inf);
***** assert (std (t), 0.5674, 1e-4);
***** assert (var (pd), Inf);
***** assert (var (t), 0.3220, 1e-4);
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution(Inf, 1)
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution(i, 1)
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution("a", 1)
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution([1, 2], 1)
***** error <LoglogisticDistribution: MU must be a nonnegative real scalar.> ...
 LoglogisticDistribution(NaN, 1)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, 0)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, -1)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, Inf)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, i)
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, "a")
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, [1, 2])
***** error <LoglogisticDistribution: SIGMA must be a positive real scalar.> ...
 LoglogisticDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LoglogisticDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LoglogisticDistribution, 2, 3)
***** shared x
 x = loglrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "parameter", {"mu", "sigma", "pa"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (LoglogisticDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LoglogisticDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LoglogisticDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (LoglogisticDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LoglogisticDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (LoglogisticDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LoglogisticDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoglogisticDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoglogisticDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoglogisticDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LoglogisticDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LoglogisticDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (LoglogisticDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (LoglogisticDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (LoglogisticDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LoglogisticDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LoglogisticDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LoglogisticDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (LoglogisticDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (LoglogisticDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LoglogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (LoglogisticDistribution)
***** error <truncate: missing input argument.> ...
 truncate (LoglogisticDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (LoglogisticDistribution, 4, 2)
***** shared pd
 pd = LoglogisticDistribution(1, 1);
 pd(2) = LoglogisticDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/UniformDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/UniformDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Uniform distribution with
 ## parameters Lower = 0 and Upper = 10. Create a Uniform distribution with these
 ## parameters and plot its PDF superimposed on a histogram of the data.

 pd = makedist ("Uniform", "Lower", 0, "Upper", 10);
 rand ("seed", 21);
 data = random (pd, 5000, 1);
 
 x = linspace (pd.Lower - 1, pd.Upper + 1, 500);
 y = pdf (pd, x);
 plot (x, y, 'r-', 'LineWidth', 2);
 hold on;
 
 [counts, centers] = hist (data, 50);
 bin_width = centers(2) - centers(1);
 normalized_counts = counts / (sum (counts) * bin_width);
 bar (centers, normalized_counts, 1);
 
 msg = "Uniform distribution with Lower = %0.2f and Upper = %0.2f";
 title (sprintf (msg, pd.Lower, pd.Upper));
 legend ("PDF", "Histogram", "location", "northeast");
 
 hold off;
***** shared pd, t
 pd = UniformDistribution (0, 5);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.3, 0.4, 0.6, 0.8, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 1, 2, 3, 4, 5], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.4, 2.8, 3.2, 3.6, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 4, 5, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.8, 3.2, 3.6, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.5, 1e-14);
***** assert (iqr (t), 1, 1e-14);
***** assert (mean (pd), 2.5, 1e-14);
***** assert (mean (t), 3, 1e-14);
***** assert (median (pd), 2.5, 1e-14);
***** assert (median (t), 3, 1e-14);
***** assert (pdf (pd, [0:5]), [0.2, 0.2, 0.2, 0.2, 0.2, 0.2], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.5, 0.5, 0.5, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.4434, 1e-4);
***** assert (std (t), 0.5774, 1e-4);
***** assert (var (pd), 2.0833, 1e-4);
***** assert (var (t), 0.3333, 1e-4);
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution (i, 1)
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution (Inf, 1)
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution ([1, 2], 1)
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution ("a", 1)
***** error <UniformDistribution: LOWER must be a real scalar.> ...
 UniformDistribution (NaN, 1)
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, i)
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, Inf)
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, [1, 2])
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, "a")
***** error <UniformDistribution: UPPER must be a real scalar.> ...
 UniformDistribution (1, NaN)
***** error <UniformDistribution: LOWER must be less than UPPER.> ...
 UniformDistribution (2, 1)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (UniformDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (UniformDistribution, 2, 3)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (UniformDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (UniformDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (UniformDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (UniformDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (UniformDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (UniformDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (UniformDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (UniformDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (UniformDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (UniformDistribution, "invalidNAME", "pdf")
***** error <plot: 'probability' PlotType is not supported for 'UniformDistribution'.> ...
 plot (UniformDistribution, "PlotType", "probability")
***** error <truncate: missing input argument.> ...
 truncate (UniformDistribution)
***** error <truncate: missing input argument.> ...
 truncate (UniformDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (UniformDistribution, 4, 2)
***** shared pd
 pd = UniformDistribution (0, 1);
 pd(2) = UniformDistribution (0, 2);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
63 tests, 63 passed, 0 known failure, 0 skipped
[inst/dist_obj/InverseGaussianDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/InverseGaussianDistribution.m
***** shared pd, t
 pd = InverseGaussianDistribution (1, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6681, 0.8855, 0.9532, 0.9791, 0.9901], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7234, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8108, 0.8855, 0.9532, 0.9791], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7234, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.3320, 0.5411, 0.8483, 1.4479, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1889, 2.4264, 2.7417, 3.1993, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5411, 0.8483, 1.4479, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4264, 2.7417, 3.1993, 4, NaN], 1e-4);
***** assert (iqr (pd), 0.8643, 1e-4);
***** assert (iqr (t), 0.8222, 1e-4);
***** assert (mean (pd), 1);
***** assert (mean (t), 2.6953, 1e-4);
***** assert (median (pd), 0.6758, 1e-4);
***** assert (median (t), 2.5716, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1098, 0.0394, 0.0162, 0.0072], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.1736, 0.4211, 0.1730, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1098, 0.0394, 0.0162, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1736, 0.4211, 0.1730, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.5332, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.2843, 1e-4);
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution(0, 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution(Inf, 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution(i, 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution("a", 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution([1, 2], 1)
***** error <InverseGaussianDistribution: MU must be a positive real scalar.> ...
 InverseGaussianDistribution(NaN, 1)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, 0)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, -1)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, Inf)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, i)
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, "a")
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, [1, 2])
***** error <InverseGaussianDistribution: LAMBDA must be a positive real scalar.> ...
 InverseGaussianDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (InverseGaussianDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (InverseGaussianDistribution, 2, 3)
***** shared x
 x = invgrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "parameter", "mu", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), ...
          "parameter", {"mu", "lambda", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "lambda", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (InverseGaussianDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (InverseGaussianDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (InverseGaussianDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (InverseGaussianDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (InverseGaussianDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (InverseGaussianDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (InverseGaussianDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (InverseGaussianDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (InverseGaussianDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (InverseGaussianDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (InverseGaussianDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (InverseGaussianDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (InverseGaussianDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (InverseGaussianDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (InverseGaussianDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (InverseGaussianDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (InverseGaussianDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (InverseGaussianDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (InverseGaussianDistribution)
***** error <truncate: missing input argument.> ...
 truncate (InverseGaussianDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (InverseGaussianDistribution, 4, 2)
***** shared pd
 pd = InverseGaussianDistribution(1, 1);
 pd(2) = InverseGaussianDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/dist_obj/HalfNormalDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/HalfNormalDistribution.m
***** shared pd, t
 pd = HalfNormalDistribution (0, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6827, 0.9545, 0.9973, 0.9999, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9420, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8664, 0.9545, 0.9973, 0.9999], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.9420, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2533, 0.5244, 0.8416, 1.2816, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.0923, 2.2068, 2.3607, 2.6064, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5244, 0.8416, 1.2816, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2068, 2.3607, 2.6064, 4, NaN], 1e-4);
***** assert (iqr (pd), 0.8317, 1e-4);
***** assert (iqr (t), 0.4111, 1e-4);
***** assert (mean (pd), 0.7979, 1e-4);
***** assert (mean (t), 2.3706, 1e-4);
***** assert (median (pd), 0.6745, 1e-4);
***** assert (median (t), 2.2771, 1e-4);
***** assert (pdf (pd, [0:5]), [0.7979, 0.4839, 0.1080, 0.0089, 0.0003, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 2.3765, 0.1951, 0.0059, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.4839, 0.1080, 0.0089, 0.0003, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 2.3765, 0.1951, 0.0059, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.6028, 1e-4);
***** assert (std (t), 0.3310, 1e-4);
***** assert (var (pd), 0.3634, 1e-4);
***** assert (var (t), 0.1096, 1e-4);
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution(Inf, 1)
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution(i, 1)
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution("a", 1)
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution([1, 2], 1)
***** error <HalfNormalDistribution: MU must be a real scalar.> ...
 HalfNormalDistribution(NaN, 1)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, 0)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, -1)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, Inf)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, i)
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, "a")
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, [1, 2])
***** error <HalfNormalDistribution: SIGMA must be a positive real scalar.> ...
 HalfNormalDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (HalfNormalDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (HalfNormalDistribution, 2, 3)
***** shared x
 x = hnrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "parameter", "sigma", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1),"NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (HalfNormalDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", "sigma", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (HalfNormalDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (HalfNormalDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (HalfNormalDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (HalfNormalDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (HalfNormalDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (HalfNormalDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (HalfNormalDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (HalfNormalDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (HalfNormalDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (HalfNormalDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (HalfNormalDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (HalfNormalDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (HalfNormalDistribution.fit (x, 1), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (HalfNormalDistribution.fit (x, 1), {1})
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 1)
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (HalfNormalDistribution.fit (x, 1), 2, {[1 2 3 4]}, ...
          "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (HalfNormalDistribution)
***** error <truncate: missing input argument.> ...
 truncate (HalfNormalDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (HalfNormalDistribution, 4, 2)
***** shared pd
 pd = HalfNormalDistribution(1, 1);
 pd(2) = HalfNormalDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/dist_obj/GeneralizedExtremeValueDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/GeneralizedExtremeValueDistribution.m
***** shared pd, t
 pd = GeneralizedExtremeValueDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0.3679, 0.6922, 0.8734, 0.9514, 0.9819, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7195, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8, 0.8734, 0.9514, 0.9819], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7195, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.4759, 0.0874, 0.6717, 1.4999, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1999, 2.4433, 2.7568, 3.2028, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.0874, 0.6717, 1.4999, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4433, 2.7568, 3.2028, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.5725, 1e-4);
***** assert (iqr (t), 0.8164, 1e-4);
***** assert (mean (pd), 0.5772, 1e-4);
***** assert (mean (t), 2.7043, 1e-4);
***** assert (median (pd), 0.3665, 1e-4);
***** assert (median (t), 2.5887, 1e-4);
***** assert (pdf (pd, [0:5]), [0.3679, 0.2546, 0.1182, 0.0474, 0.0180, 0.0067], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.0902, 0.4369, 0.1659, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1794, 0.2546, 0.1182, 0.0474, 0.0180, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0902, 0.4369, 0.1659, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.2825, 1e-4);
***** assert (std (t), 0.5289, 1e-4);
***** assert (var (pd), 1.6449, 1e-4);
***** assert (var (t), 0.2798, 1e-4);
***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(Inf, 1, 1)
***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(i, 1, 1)
***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
 GeneralizedExtremeValueDistribution("a", 1, 1)
***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
 GeneralizedExtremeValueDistribution([1, 2], 1, 1)
***** error <GeneralizedExtremeValueDistribution: K must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(NaN, 1, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 0, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, -1, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, Inf, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, i, 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, "a", 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, [1, 2], 1)
***** error <GeneralizedExtremeValueDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedExtremeValueDistribution(1, NaN, 1)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, Inf)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, i)
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, "a")
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, [1, 2])
***** error <GeneralizedExtremeValueDistribution: MU must be a real scalar.> ...
 GeneralizedExtremeValueDistribution(1, 1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GeneralizedExtremeValueDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GeneralizedExtremeValueDistribution, 2, 3)
***** shared x
 x = gevrnd (1, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), ...
          "parameter", "sigma", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), ...
          "parameter", {"k", "sigma", "mu", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"k", "sigma", "mu", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedExtremeValueDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "sigma", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (GeneralizedExtremeValueDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GeneralizedExtremeValueDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (GeneralizedExtremeValueDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (GeneralizedExtremeValueDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (GeneralizedExtremeValueDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 4)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, ...
          "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GeneralizedExtremeValueDistribution.fit (x), 1, {[1 2 3 4]}, ...
          "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (GeneralizedExtremeValueDistribution)
***** error <truncate: missing input argument.> ...
 truncate (GeneralizedExtremeValueDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (GeneralizedExtremeValueDistribution, 4, 2)
***** shared pd
 pd = GeneralizedExtremeValueDistribution(1, 1, 1);
 pd(2) = GeneralizedExtremeValueDistribution(1, 3, 1);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
100 tests, 100 passed, 0 known failure, 0 skipped
[inst/dist_obj/NegativeBinomialDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/NegativeBinomialDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Negative Binomial
 ## distribution with parameters R = 5 and P = 0.5.  Fit a Negative Binomial
 ## distribution to this data and plot a PDF of the fitted distribution
 ## superimposed on a histogram of the data.

 pd_fixed = makedist ("NegativeBinomial", "R", 5, "P", 0.5)
 rand ("seed", 2);
 data = random (pd_fixed, 5000, 1);
 pd_fitted = fitdist (data, "NegativeBinomial")
 plot (pd_fitted)
 msg = "Fitted Negative Binomial distribution with R = %0.2f and P = %0.2f";
 title (sprintf (msg, pd_fitted.R, pd_fitted.P))
***** shared pd, t, t_inf
 pd = NegativeBinomialDistribution (5, 0.5);
 t = truncate (pd, 2, 4);
 t_inf = truncate (pd, 2, Inf);
***** assert (cdf (pd, [0:5]), [0.0312, 0.1094, 0.2266, 0.3633, 0.5, 0.6230], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0.3, 0.65, 1, 1], 1e-4);
***** assert (cdf (t_inf, [0:5]), [0, 0, 0.1316, 0.2851, 0.4386, 0.5768], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1094, 0.2266, 0.3633, 0.5000], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.3, 0.65, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 2, 4, 5, 7, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2, 3, 3, 4, 4], 1e-4);
***** assert (icdf (t_inf, [0:0.2:1]), [2, 3, 4, 6, 8, Inf], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 4, 5, 7, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 3, 3, 4, 4, NaN], 1e-4);
***** assert (iqr (pd), 4);
***** assert (iqr (t), 2);
***** assert (mean (pd), 5);
***** assert (mean (t), 3.0500, 1e-4);
***** assert (mean (t_inf), 5.5263, 1e-4);
***** assert (median (pd), 4);
***** assert (median (t), 3);
***** assert (pdf (pd, [0:5]), [0.0312, 0.0781, 0.1172, 0.1367, 0.1367, 0.1230], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.3, 0.35, 0.35, 0], 1e-4);
***** assert (pdf (t_inf, [0:5]), [0, 0, 0.1316, 0.1535, 0.1535, 0.1382], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.0781, 0.1172, 0.1367, 0.1367, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.3, 0.35, 0.35, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 3.1623, 1e-4);
***** assert (std (t), 0.8047, 1e-4);
***** assert (std (t_inf), 2.9445, 1e-4);
***** assert (var (pd), 10);
***** assert (var (t), 0.6475, 1e-4);
***** assert (var (t_inf), 8.6704, 1e-4);
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution(Inf, 1)
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution(i, 1)
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution("a", 1)
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution([1, 2], 1)
***** error <NegativeBinomialDistribution: R must be a positive scalar.> ...
 NegativeBinomialDistribution(NaN, 1)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, 0)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, -1)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, Inf)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, i)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, "a")
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, [1, 2])
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, NaN)
***** error <NegativeBinomialDistribution: P must be a real scalar bounded in the range> ...
 NegativeBinomialDistribution(1, 1.2)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NegativeBinomialDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NegativeBinomialDistribution, 2, 3)
***** shared x
 x = nbinrnd (1, 0.5, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "parameter", "R", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), ...
          "parameter", {"R", "P", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"R", "P", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (NegativeBinomialDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NegativeBinomialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "R", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (NegativeBinomialDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NegativeBinomialDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (NegativeBinomialDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NegativeBinomialDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NegativeBinomialDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NegativeBinomialDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NegativeBinomialDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NegativeBinomialDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NegativeBinomialDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (NegativeBinomialDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (NegativeBinomialDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (NegativeBinomialDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NegativeBinomialDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NegativeBinomialDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NegativeBinomialDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NegativeBinomialDistribution.fit (x), 1, {[1 2 3]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (NegativeBinomialDistribution)
***** error <truncate: missing input argument.> ...
 truncate (NegativeBinomialDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (NegativeBinomialDistribution, 4, 2)
***** shared pd
 pd = NegativeBinomialDistribution(1, 0.5);
 pd(2) = NegativeBinomialDistribution(1, 0.6);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
102 tests, 102 passed, 0 known failure, 0 skipped
[inst/dist_obj/ExponentialDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/ExponentialDistribution.m
***** shared pd, t
 pd = ExponentialDistribution (1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.0986, 1e-4);
***** assert (iqr (t), 0.8020, 1e-4);
***** assert (mean (pd), 1);
***** assert (mean (t), 2.6870, 1e-4);
***** assert (median (pd), 0.6931, 1e-4);
***** assert (median (t), 2.5662, 1e-4);
***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.5253, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.2759, 1e-4);
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(0)
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(-1)
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(Inf)
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(i)
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution("a")
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution([1, 2])
***** error <ExponentialDistribution: MU must be a positive real scalar.> ...
 ExponentialDistribution(NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (ExponentialDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (ExponentialDistribution, 2, 3)
***** shared x
 x = exprnd (1, [100, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (ExponentialDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (ExponentialDistribution.fit (x), "parameter", "mu", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (ExponentialDistribution.fit (x), "parameter", {"mu", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (ExponentialDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "parameter", "parm")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExponentialDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (ExponentialDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (ExponentialDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (ExponentialDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (ExponentialDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (ExponentialDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExponentialDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExponentialDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (ExponentialDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (ExponentialDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (ExponentialDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (ExponentialDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (ExponentialDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (ExponentialDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExponentialDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExponentialDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (ExponentialDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (ExponentialDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (ExponentialDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (ExponentialDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (ExponentialDistribution)
***** error <truncate: missing input argument.> ...
 truncate (ExponentialDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (ExponentialDistribution, 4, 2)
***** shared pd
 pd = ExponentialDistribution(1);
 pd(2) = ExponentialDistribution(3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
90 tests, 90 passed, 0 known failure, 0 skipped
[inst/dist_obj/TriangularDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/TriangularDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Triangular distribution
 ## with parameters A = 0, B = 1, C = 2. Fit a Triangular distribution to
 ## this data and plot a PDF of the fitted distribution superimposed on a
 ## histogram of the data.

 pd_fixed = makedist ("Triangular", "A", 0, "B", 1, "C", 2);
 rand ("seed", 2);
 data = random (pd_fixed, 5000, 1);
 A = min (data);
 C = mean (data);
 B = max (data);

 [counts, centers] = hist (data, 50);
 bin_width = centers(2) - centers(1);
 normalized_counts = counts / (sum (counts) * bin_width);
 bar (centers, normalized_counts, 1);
 hold on;

 x = linspace (A, B, 100);
 y = (2 * (x - A) / (C - A) .* (x <= C)) + (2 * (B - x) / (B - C) .* (x > C));

 plot (x, y, 'r-', 'LineWidth', 2);

 msg = sprintf ("Fitted Triangular distribution with A = %0.2f, C = %0.2f, B = %0.2f", A, C, B);
 title (msg);

 hold off;
***** shared pd, t
 pd = TriangularDistribution (0, 3, 5);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.0667, 0.2667, 0.6000, 0.9000, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.5263, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1500, 0.2667, 0.6, 0.9, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.5263, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 1.7321, 2.4495, 3, 3.5858, 5], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.4290, 2.7928, 3.1203, 3.4945, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4495, 3, 3.5858, 5, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.7928, 3.1203, 3.4945, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4824, 1e-4);
***** assert (iqr (t), 0.8678, 1e-4);
***** assert (mean (pd), 2.6667, 1e-4);
***** assert (mean (t), 2.9649, 1e-4);
***** assert (median (pd), 2.7386, 1e-4);
***** assert (median (t), 2.9580, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.1333, 0.2667, 0.4, 0.2, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.4211, 0.6316, 0.3158, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.0274, 1e-4);
***** assert (std (t), 0.5369, 1e-4);
***** assert (var (pd), 1.0556, 1e-4);
***** assert (var (t), 0.2882, 1e-4);
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution (i, 1, 2)
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution (Inf, 1, 2)
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution ([1, 2], 1, 2)
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution ("a", 1, 2)
***** error <TriangularDistribution: lower limit A must be a real scalar.> ...
 TriangularDistribution (NaN, 1, 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, i, 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, Inf, 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, [1, 2], 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, "a", 2)
***** error <TriangularDistribution: mode B must be a real scalar.> ...
 TriangularDistribution (1, NaN, 2)
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, i)
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, Inf)
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, [1, 2])
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, "a")
***** error <TriangularDistribution: upper limit C must be a real scalar.> ...
 TriangularDistribution (1, 2, NaN)
***** error <TriangularDistribution: lower limit A must be less than upper limit C.> ...
 TriangularDistribution (1, 1, 1)
***** error <TriangularDistribution: mode B must be within lower limit A and upper limit C.> ...
 TriangularDistribution (1, 0.5, 2)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (TriangularDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (TriangularDistribution, 2, 3)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (TriangularDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (TriangularDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (TriangularDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (TriangularDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (TriangularDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (TriangularDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (TriangularDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (TriangularDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (TriangularDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (TriangularDistribution, "invalidNAME", "pdf")
***** error <'probability' PlotType is not supported for 'TriangularDistribution'.> ...
 plot (TriangularDistribution, "PlotType", "probability")
***** error <truncate: missing input argument.> ...
 truncate (TriangularDistribution)
***** error <truncate: missing input argument.> ...
 truncate (TriangularDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (TriangularDistribution, 4, 2)
***** shared pd
 pd = TriangularDistribution (0, 1, 2);
 pd(2) = TriangularDistribution (0, 1, 2);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
69 tests, 69 passed, 0 known failure, 0 skipped
[inst/dist_obj/BirnbaumSaundersDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/BirnbaumSaundersDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Birnbaum-Saunders
 ## distribution with parameters β = 1 and γ = 0.5.  Fit a Birnbaum-Saunders
 ## distribution to this data and plot a PDF of the fitted distribution
 ## superimposed on a histogram of the data.

 pd_fixed = makedist ("BirnbaumSaunders", "beta", 1, "gamma", 0.5)
 randg ("seed", 21);
 data = random (pd_fixed, 5000, 1);
 pd_fitted = fitdist (data, "BirnbaumSaunders")
 plot (pd_fitted)
 msg = "Fitted Birnbaum-Saunders distribution with beta = %0.2f and gamma = %0.2f";
 title (sprintf (msg, pd_fitted.beta, pd_fitted.gamma))
***** shared pd, t
 pd = BirnbaumSaundersDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7602, 0.8759, 0.9332, 0.9632], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6687, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6585, 0.7602, 0.8759, 0.9332, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.6687, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.4411, 0.7767, 1.2875, 2.2673, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2293, 2.5073, 2.8567, 3.3210, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7767, 1.2875, 2.2673, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5073, 2.8567, 3.3210, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4236, 1e-4);
***** assert (iqr (t), 0.8968, 1e-4);
***** assert (mean (pd), 1.5, eps);
***** assert (mean (t), 2.7723, 1e-4);
***** assert (median (pd), 1, 1e-4);
***** assert (median (t), 2.6711, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1648, 0.0788, 0.0405, 0.0216], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.9528, 0.4559, 0.2340, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2497, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.5, eps);
***** assert (std (t), 0.5528, 1e-4);
***** assert (var (pd), 2.25, eps);
***** assert (var (t), 0.3056, 1e-4);
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(0, 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(Inf, 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(i, 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution("beta", 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution([1, 2], 1)
***** error <BirnbaumSaundersDistribution: BETA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(NaN, 1)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, 0)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, -1)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, Inf)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, i)
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, "beta")
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, [1, 2])
***** error <BirnbaumSaundersDistribution: GAMMA must be a positive real scalar.> ...
 BirnbaumSaundersDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BirnbaumSaundersDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BirnbaumSaundersDistribution, 2, 3)
***** shared x
 rand ("seed", 5);
 x = bisarnd (1, 1, [100, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "parameter", ...
          "beta", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), ...
          "parameter", {"beta", "gamma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"beta", "gamma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BirnbaumSaundersDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "beta", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (BirnbaumSaundersDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BirnbaumSaundersDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (BirnbaumSaundersDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BirnbaumSaundersDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BirnbaumSaundersDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BirnbaumSaundersDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BirnbaumSaundersDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BirnbaumSaundersDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BirnbaumSaundersDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (BirnbaumSaundersDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (BirnbaumSaundersDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (BirnbaumSaundersDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BirnbaumSaundersDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (BirnbaumSaundersDistribution)
***** error <truncate: missing input argument.> ...
 truncate (BirnbaumSaundersDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (BirnbaumSaundersDistribution, 4, 2)
***** shared pd
 pd = BirnbaumSaundersDistribution(1, 1);
 pd(2) = BirnbaumSaundersDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/dist_obj/BurrDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/BurrDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Burr type XII
 ## distribution with parameters alpha = 1, c = 2, and k = 1.  Fit a Burr type
 ## XII distribution to this data and plot a PDF of the fitted distribution
 ## superimposed on a histogram of the data

 pd = makedist ("Burr", "alpha", 1, "c", 2, "k", 1)
 rand ("seed", 21);
 data = random (pd, 5000, 1);
 pd = fitdist (data, "Burr")
 plot (pd)
 msg = strcat ("Fitted Burr type XII distribution with", ...
               " alpha = %0.2f, c =  %0.2f, and k = %0.2f");
 title (sprintf (msg, pd.alpha, pd.c, pd.k))
***** shared pd, t
 pd = BurrDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.5, 0.6667, 0.75, 0.8, 0.8333], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.625, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6, 0.6667, 0.75, 0.8], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.625, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.25, 0.6667, 1.5, 4, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2609, 2.5714, 2.9474, 3.4118, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.6667, 1.5, 4, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5714, 2.9474, 3.4118, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.6667, 1e-4);
***** assert (iqr (t), 0.9524, 1e-4);
***** assert (mean (pd), Inf);
***** assert (mean (t), 2.8312, 1e-4);
***** assert (median (pd), 1, 1e-4);
***** assert (median (t), 2.75, 1e-4);
***** assert (pdf (pd, [0:5]), [1, 0.25, 0.1111, 0.0625, 0.04, 0.0278], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.8333, 0.4687, 0.3, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.25, 0.1111, 0.0625, 0.04, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.8333, 0.4687, 0.3, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), Inf);
***** assert (std (t), 0.5674, 1e-4);
***** assert (var (pd), Inf);
***** assert (var (t), 0.3220, 1e-4);
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(0, 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(-1, 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(Inf, 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(i, 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution("a", 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution([1, 2], 1, 1)
***** error <BurrDistribution: ALPHA must be a positive real scalar.> ...
 BurrDistribution(NaN, 1, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, 0, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, -1, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, Inf, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, i, 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, "a", 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, [1, 2], 1)
***** error <BurrDistribution: C must be a positive real scalar.> ...
 BurrDistribution(1, NaN, 1)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, 0)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, -1)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, Inf)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, i)
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, "a")
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, [1, 2])
***** error <BurrDistribution: K must be a positive real scalar.> ...
 BurrDistribution(1, 1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BurrDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BurrDistribution, 2, 3)
***** shared x
 rand ("seed", 4);
 x = burrrnd (1, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (BurrDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BurrDistribution.fit (x), "parameter", "c", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BurrDistribution.fit (x), "parameter", {"alpha", "c", "k", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"alpha", "c", "k", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (BurrDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BurrDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BurrDistribution.fit (x), "alpha", 0.01, "parameter", "c", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (BurrDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BurrDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (BurrDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BurrDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BurrDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BurrDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BurrDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BurrDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BurrDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (BurrDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (BurrDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (BurrDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BurrDistribution.fit (x), 4)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BurrDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BurrDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (BurrDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (BurrDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (BurrDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BurrDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BurrDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (BurrDistribution)
***** error <truncate: missing input argument.> ...
 truncate (BurrDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (BurrDistribution, 4, 2)
***** shared pd
 pd = BurrDistribution(1, 1, 1);
 pd(2) = BurrDistribution(1, 3, 1);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
104 tests, 104 passed, 0 known failure, 0 skipped
[inst/dist_obj/MultinomialDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/MultinomialDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Multinomial distribution
 ## with parameters Probabilities = [0.1, 0.2, 0.3, 0.2, 0.1, 0.1].  Create
 ## the distribution and plot the PDF superimposed on a histogram of the data.

 probs = [0.1, 0.2, 0.3, 0.2, 0.1, 0.1];
 pd = makedist ("Multinomial", "Probabilities", probs);
 rand ("seed", 2);
 data = random (pd, 5000, 1);
 hist (data, length (probs));
 hold on
 x = 1:length (probs);
 y = pdf (pd, x) * 5000;
 stem (x, y, "r", "LineWidth", 2);
 hold off
 msg = "Multinomial distribution with Probabilities = [%s]";
 probs_str = num2str (probs, "%0.1f ");
 title (sprintf (msg, probs_str))
***** shared pd, t
 pd = MultinomialDistribution ([0.1, 0.2, 0.3, 0.2, 0.1, 0.1]);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [2, 3, 4]), [0.3, 0.6, 0.8], eps);
***** assert (cdf (t, [2, 3, 4]), [0.2857, 0.7143, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.1, 0.3, 0.6, 0.8], eps);
***** assert (cdf (pd, [1.5, 2-eps, 3, 4]), [0.1, 0.1, 0.6, 0.8], eps);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.2857, 0.7143, 1], 1e-4);
***** assert (cdf (t, [1.5, 2-eps, 3, 4]), [0, 0, 0.7143, 1], 1e-4);
***** assert (cdf (pd, [1, 2.5, 4, 6]), [0.1, 0.3, 0.8, 1], eps);
***** assert (icdf (pd, [0, 0.2857, 0.7143, 1]), [1, 2, 4, 6]);
***** assert (icdf (t, [0, 0.2857, 0.7143, 1]), [2, 2, 4, 4]);
***** assert (icdf (t, [0, 0.35, 0.7143, 1]), [2, 3, 4, 4]);
***** assert (icdf (t, [0, 0.35, 0.7143, 1, NaN]), [2, 3, 4, 4, NaN]);
***** assert (icdf (t, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 2, 3, 4, 4, NaN]);
***** assert (icdf (pd, [-0.5, 0, 0.35, 0.7143, 1, NaN]), [NaN, 1, 3, 4, 6, NaN]);
***** assert (iqr (pd), 2);
***** assert (iqr (t), 2);
***** assert (mean (pd), 3.3, 1e-14);
***** assert (mean (t), 3, eps);
***** assert (median (pd), 3);
***** assert (median (t), 3);
***** assert (pdf (pd, [-5, 1, 2.5, 4, 6, NaN, 9]), [0, 0.1, 0, 0.2, 0.1, NaN, 0]);
***** assert (pdf (pd, [-5, 1, 2, 3, 4, 6, NaN, 9]), ...
 [0, 0.1, 0.2, 0.3, 0.2, 0.1, NaN, 0]);
***** assert (pdf (t, [-5, 1, 2, 3, 4, 6, NaN, 0]), ...
 [0, 0, 0.2857, 0.4286, 0.2857, 0, NaN, 0], 1e-4);
***** assert (pdf (t, [-5, 1, 2, 4, 6, NaN, 0]), ...
 [0, 0, 0.2857, 0.2857, 0, NaN, 0], 1e-4);
***** assert (unique (random (pd, 1000, 5)), [1, 2, 3, 4, 5, 6]');
***** assert (unique (random (t, 1000, 5)), [2, 3, 4]');
***** assert (std (pd), 1.4177, 1e-4);
***** assert (std (t), 0.7559, 1e-4);
***** assert (var (pd), 2.0100, 1e-4);
***** assert (var (t), 0.5714, 1e-4);
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(0)
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(-1)
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(Inf)
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(i)
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution("a")
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution([1, 2])
***** error <MultinomialDistribution: PROBABILITIES must be a vector of positive real scalars that sum up to 1.> ...
 MultinomialDistribution(NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (MultinomialDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (MultinomialDistribution, 2, 3)
***** error <cdf: X must be real.> ...
 cdf (MultinomialDistribution, i)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (MultinomialDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (MultinomialDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (MultinomialDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (MultinomialDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (MultinomialDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (MultinomialDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (MultinomialDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (MultinomialDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (MultinomialDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (MultinomialDistribution, "invalidNAME", "pdf")
***** error <plot: 'probability' PlotType is not supported for 'MultinomialDistribution'.> ...
 plot (MultinomialDistribution, "PlotType", "probability")
***** error <truncate: is_nan input argument.> ...
 truncate (MultinomialDistribution)
***** error <truncate: is_nan input argument.> ...
 truncate (MultinomialDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (MultinomialDistribution, 4, 2)
***** shared pd
 pd = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
 pd(2) = MultinomialDistribution([0.1, 0.2, 0.3, 0.4]);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
64 tests, 64 passed, 0 known failure, 0 skipped
[inst/dist_obj/RayleighDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/RayleighDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Rayleigh distribution with
 ## parameter sigma = 2.  Fit a Rayleigh distribution to this data and plot
 ## a PDF of the fitted distribution superimposed on a histogram of the data.

 pd_fixed = makedist ("Rayleigh", "sigma", 2)
 rand ("seed", 2);
 data = random (pd_fixed, 5000, 1);
 pd_fitted = fitdist (data, "Rayleigh")
 plot (pd_fitted)
 msg = "Fitted Rayleigh distribution with sigma = %0.2f";
 title (sprintf (msg, pd_fitted.sigma))
***** shared pd, t
 pd = RayleighDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.3935, 0.8647, 0.9889, 0.9997, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.9202, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.6753, 0.8647, 0.9889, 0.9997, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.9202, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.6680, 1.0108, 1.3537, 1.7941, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1083, 2.2402, 2.4135, 2.6831, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.0108, 1.3537, 1.7941, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.2402, 2.4135, 2.6831, 4, NaN], 1e-4);
***** assert (iqr (pd), 0.9066, 1e-4);
***** assert (iqr (t), 0.4609, 1e-4);
***** assert (mean (pd), 1.2533, 1e-4);
***** assert (mean (t), 2.4169, 1e-4);
***** assert (median (pd), 1.1774, 1e-4);
***** assert (median (t), 2.3198, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.6065, 0.2707, 0.0333, 0.0013, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 2.0050, 0.2469, 0.0099, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4870, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.6551, 1e-4);
***** assert (std (t), 0.3591, 1e-4);
***** assert (var (pd), 0.4292, 1e-4);
***** assert (var (t), 0.1290, 1e-4);
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(0)
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(-1)
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(Inf)
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(i)
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution("a")
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution([1, 2])
***** error <RayleighDistribution: SIGMA must be a positive real scalar.> ...
 RayleighDistribution(NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (RayleighDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (RayleighDistribution, 2, 3)
***** shared x
 x = raylrnd (1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (RayleighDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RayleighDistribution.fit (x), "parameter", "sigma", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (RayleighDistribution.fit (x), "parameter", {"sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (RayleighDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RayleighDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RayleighDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "sigma", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (RayleighDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (RayleighDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (RayleighDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (RayleighDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RayleighDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RayleighDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RayleighDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (RayleighDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (RayleighDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (RayleighDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (RayleighDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (RayleighDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RayleighDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RayleighDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RayleighDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (RayleighDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (RayleighDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (RayleighDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (RayleighDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (RayleighDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (RayleighDistribution)
***** error <truncate: missing input argument.> ...
 truncate (RayleighDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (RayleighDistribution, 4, 2)
***** shared pd
 pd = RayleighDistribution(1);
 pd(2) = RayleighDistribution(3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
90 tests, 90 passed, 0 known failure, 0 skipped
[inst/dist_obj/LognormalDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/LognormalDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Lognormal distribution with
 ## parameters mu = 0 and sigma = 1.  Fit a Lognormal distribution to this data and plot
 ## a PDF of the fitted distribution superimposed on a histogram of the data.

 pd_fixed = makedist ("Lognormal", "mu", 0, "sigma", 1)
 randn ("seed", 2);
 data = random (pd_fixed, 5000, 1);
 pd_fitted = fitdist (data, "Lognormal")
 plot (pd_fitted)
 msg = "Fitted Lognormal distribution with mu = %0.2f and sigma = %0.2f";
 title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma))
***** shared pd, t
 pd = LognormalDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.5, 0.7559, 0.8640, 0.9172, 0.9462], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6705, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.6574, 0.7559, 0.8640, 0.9172], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6705, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.4310, 0.7762, 1.2883, 2.3201, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2256, 2.5015, 2.8517, 3.3199, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.7762, 1.2883, 2.3201, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5015, 2.8517, 3.3199, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4536, 1e-4);
***** assert (iqr (t), 0.8989, 1e-4);
***** assert (mean (pd), 1.6487, 1e-4);
***** assert (mean (t), 2.7692, 1e-4);
***** assert (median (pd), 1, 1e-4);
***** assert (median (t), 2.6653, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.3989, 0.1569, 0.0727, 0.0382, 0.0219], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.9727, 0.4509, 0.2366, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3989, 0.1569, 0.0727, 0.0382, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.9727, 0.4509, 0.2366, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 2.1612, 1e-4);
***** assert (std (t), 0.5540, 1e-4);
***** assert (var (pd), 4.6708, 1e-4);
***** assert (var (t), 0.3069, 1e-4);
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution(Inf, 1)
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution(i, 1)
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution("a", 1)
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution([1, 2], 1)
***** error <LognormalDistribution: MU must be a real scalar.> ...
 LognormalDistribution(NaN, 1)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, 0)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, -1)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, Inf)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, i)
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, "a")
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, [1, 2])
***** error <LognormalDistribution: SIGMA must be a positive real scalar.> ...
 LognormalDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LognormalDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LognormalDistribution, 2, 3)
***** shared x
 randn ("seed", 1);
 x = lognrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (LognormalDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LognormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LognormalDistribution.fit (x), "parameter", {"mu", "sigma", "parm"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (LognormalDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LognormalDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LognormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (LognormalDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LognormalDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (LognormalDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LognormalDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LognormalDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LognormalDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LognormalDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LognormalDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LognormalDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (LognormalDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (LognormalDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (LognormalDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LognormalDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LognormalDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LognormalDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (LognormalDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (LognormalDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (LognormalDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LognormalDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LognormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (LognormalDistribution)
***** error <truncate: missing input argument.> ...
 truncate (LognormalDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (LognormalDistribution, 4, 2)
***** shared pd
 pd = LognormalDistribution(1, 1);
 pd(2) = LognormalDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/BetaDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/BetaDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Beta distribution with
 ## parameters a = 2 and b = 5.  Fit a Beta distribution to this data and plot
 ## a PDF of the fitted distribution superimposed on a histogram of the data.

 pd_fixed = makedist ("Beta", "a", 2, "b", 5)
 randg ("seed", 2);
 data = random (pd_fixed, 5000, 1);
 pd_fitted = fitdist (data, "Beta")
 plot (pd_fitted)
 msg = "Fitted Beta distribution with a = %0.2f and b = %0.2f";
 title (sprintf (msg, pd_fitted.a, pd_fitted.b))
***** shared pd, t
 pd = BetaDistribution;
 t = truncate (pd, 0.2, 0.8);
***** assert (cdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
***** assert (cdf (t, [0:0.2:1]), [0, 0, 0.3333, 0.6667, 1, 1], 1e-4);
***** assert (cdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
***** assert (cdf (t, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2, 0.4, 0.6, 0.8, 1], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [0.2, 0.32, 0.44, 0.56, 0.68, 0.8], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.4, 0.6, 0.8, 1, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 0.44, 0.56, 0.68, 0.8, NaN], 1e-4);
***** assert (iqr (pd), 0.5, 1e-4);
***** assert (iqr (t), 0.3, 1e-4);
***** assert (mean (pd), 0.5);
***** assert (mean (t), 0.5, 1e-6);
***** assert (median (pd), 0.5);
***** assert (median (t), 0.5, 1e-6);
***** assert (pdf (pd, [0:0.2:1]), [1, 1, 1, 1, 1, 1], 1e-4);
***** assert (pdf (t, [0:0.2:1]), [0, 1.6667, 1.6667, 1.6667, 1.6667, 0], 1e-4);
***** assert (pdf (pd, [-1, 1, NaN]), [0, 1, NaN], 1e-4);
***** assert (pdf (t, [-1, 1, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 0.2), false);
***** assert (any (random (t, 1000, 1) > 0.8), false);
***** assert (std (pd), 0.2887, 1e-4);
***** assert (std (t), 0.1732, 1e-4);
***** assert (var (pd), 0.0833, 1e-4);
***** assert (var (t), 0.0300, 1e-4);
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution(0, 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution(Inf, 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution(i, 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution("a", 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution([1, 2], 1)
***** error <BetaDistribution: A must be a positive real scalar.> ...
 BetaDistribution(NaN, 1)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, 0)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, -1)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, Inf)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, i)
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, "a")
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, [1, 2])
***** error <BetaDistribution: B must be a positive real scalar.> ...
 BetaDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BetaDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BetaDistribution, 2, 3)
***** shared x
 randg ("seed", 1);
 x = betarnd (1, 1, [100, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (BetaDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BetaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BetaDistribution.fit (x), "parameter", {"a", "b", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"a", "b", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (BetaDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BetaDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BetaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (BetaDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BetaDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (BetaDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BetaDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BetaDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BetaDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BetaDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BetaDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BetaDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (BetaDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (BetaDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (BetaDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BetaDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BetaDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BetaDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (BetaDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (BetaDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (BetaDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BetaDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BetaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (BetaDistribution)
***** error <truncate: missing input argument.> ...
 truncate (BetaDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (BetaDistribution, 4, 2)
***** shared pd
 pd = BetaDistribution(1, 1);
 pd(2) = BetaDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/dist_obj/LoguniformDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/LoguniformDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Log-uniform distribution with
 ## parameters Lower = 1 and Upper = 10.  Plot a PDF of the distribution superimposed
 ## on a histogram of the data.

 pd_fixed = makedist ("Loguniform", "Lower", 1, "Upper", 10);
 rand ("seed", 2);
 data = random (pd_fixed, 5000, 1);
 plot (pd_fixed)
 hold on
 hist (data, 50)
 hold off
 msg = "Log-uniform distribution with Lower = %0.2f and Upper = %0.2f";
 title (sprintf (msg, pd_fixed.Lower, pd_fixed.Upper))
***** shared pd, t
 pd = LoguniformDistribution (1, 4);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0, 0.5, 0.7925, 1, 1], 1e-4);
***** assert (cdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0, 0.5850, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.2925, 0.5, 0.7925, 1], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.5850, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [1, 1.3195, 1.7411, 2.2974, 3.0314, 4], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2974, 2.6390, 3.0314, 3.4822, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.7411, 2.2974, 3.0314, 4, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.6390, 3.0314, 3.4822, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4142, 1e-4);
***** assert (iqr (t), 0.9852, 1e-4);
***** assert (mean (pd), 2.1640, 1e-4);
***** assert (mean (t), 2.8854, 1e-4);
***** assert (median (pd), 2);
***** assert (median (t), 2.8284, 1e-4);
***** assert (pdf (pd, [0, 1, 2, 3, 4, 5]), [0, 0.7213, 0.3607, 0.2404, 0.1803, 0], 1e-4);
***** assert (pdf (t, [0, 1, 2, 3, 4, 5]), [0, 0, 0.7213, 0.4809, 0.3607, 0], 1e-4);
***** assert (pdf (pd, [-1, 1, 2, 3, 4, NaN]), [0, 0.7213, 0.3607, 0.2404, 0.1803, NaN], 1e-4);
***** assert (pdf (t, [-1, 1, 2, 3, 4, NaN]), [0, 0, 0.7213, 0.4809, 0.3607, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (pd, 1000, 1) < 1), false);
***** assert (any (random (pd, 1000, 1) > 4), false);
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.8527, 1e-4);
***** assert (std (t), 0.5751, 1e-4);
***** assert (var (pd), 0.7270, 1e-4);
***** assert (var (t), 0.3307, 1e-4);
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution (i, 1)
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution (Inf, 1)
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution ([1, 2], 1)
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution ("a", 1)
***** error <LoguniformDistribution: LOWER must be a positive real scalar.> ...
 LoguniformDistribution (NaN, 1)
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, i)
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, Inf)
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, [1, 2])
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, "a")
***** error <LoguniformDistribution: UPPER must be a real scalar.> ...
 LoguniformDistribution (1, NaN)
***** error <LoguniformDistribution: LOWER must be less than UPPER.> ...
 LoguniformDistribution (2, 1)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LoguniformDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LoguniformDistribution, 2, 3)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (LoguniformDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LoguniformDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (LoguniformDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LoguniformDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoguniformDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoguniformDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LoguniformDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LoguniformDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LoguniformDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (LoguniformDistribution, "invalidNAME", "pdf")
***** error <plot: 'probability' PlotType is not supported for 'LoguniformDistribution'.> ...
 plot (LoguniformDistribution, "PlotType", "probability")
***** error <truncate: missing input argument.> ...
 truncate (LoguniformDistribution)
***** error <truncate: missing input argument.> ...
 truncate (LoguniformDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (LoguniformDistribution, 4, 2)
***** shared pd
 pd = LoguniformDistribution(1, 4);
 pd(2) = LoguniformDistribution(2, 5);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
65 tests, 65 passed, 0 known failure, 0 skipped
[inst/dist_obj/GeneralizedParetoDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/GeneralizedParetoDistribution.m
***** shared pd, t
 pd = GeneralizedParetoDistribution (1, 1, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0, 0.5, 0.6667, 0.75, 0.8], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.6667, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.3333, 0.5, 0.6667, 0.75], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.6667, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [1, 1.25, 1.6667, 2.5, 5, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2222, 2.5, 2.8571, 3.3333, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.6667, 2.5, 5, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.5, 2.8571, 3.3333, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.6667, 1e-4);
***** assert (iqr (t), 0.9143, 1e-4);
***** assert (mean (pd), Inf);
***** assert (mean (t), 2.7726, 1e-4);
***** assert (median (pd), 2);
***** assert (median (t), 2.6667, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 1, 0.25, 0.1111, 0.0625, 0.04], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1, 0.4444, 0.25, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 1, 0.25, 0.1111, 0.0625, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1, 0.4444, 0.25, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), Inf);
***** assert (std (t), 0.5592, 1e-4);
***** assert (var (pd), Inf);
***** assert (var (t), 0.3128, 1e-4);
***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
 GeneralizedParetoDistribution(Inf, 1, 1)
***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
 GeneralizedParetoDistribution(i, 1, 1)
***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
 GeneralizedParetoDistribution("a", 1, 1)
***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
 GeneralizedParetoDistribution([1, 2], 1, 1)
***** error <GeneralizedParetoDistribution: K must be a real scalar.> ...
 GeneralizedParetoDistribution(NaN, 1, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, 0, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, -1, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, Inf, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, i, 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, "a", 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, [1, 2], 1)
***** error <GeneralizedParetoDistribution: SIGMA must be a positive real scalar.> ...
 GeneralizedParetoDistribution(1, NaN, 1)
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, Inf)
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, i)
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, "a")
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, [1, 2])
***** error <GeneralizedParetoDistribution: THETA must be a real scalar.> ...
 GeneralizedParetoDistribution(1, 1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GeneralizedParetoDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GeneralizedParetoDistribution, 2, 3)
***** shared x
 x = gprnd (1, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), ...
          "parameter", "sigma", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), ...
          "parameter", {"k", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", {"k", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GeneralizedParetoDistribution.fit (x, 1), "alpha", 0.01, ...
          "parameter", "sigma", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (GeneralizedParetoDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GeneralizedParetoDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (GeneralizedParetoDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GeneralizedParetoDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedParetoDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedParetoDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GeneralizedParetoDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GeneralizedParetoDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GeneralizedParetoDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (GeneralizedParetoDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (GeneralizedParetoDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (GeneralizedParetoDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, ...
          "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GeneralizedParetoDistribution.fit (x, 1), 1, {[1 2 3 4]}, ...
          "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (GeneralizedParetoDistribution)
***** error <truncate: missing input argument.> ...
 truncate (GeneralizedParetoDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (GeneralizedParetoDistribution, 4, 2)
***** shared pd
 pd = GeneralizedParetoDistribution(1, 1, 1);
 pd(2) = GeneralizedParetoDistribution(1, 3, 1);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
100 tests, 100 passed, 0 known failure, 0 skipped
[inst/dist_obj/RicianDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/RicianDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Rician distribution with
 ## parameters s = 2 and sigma = 1. Fit a Rician distribution to this data and
 ## plot a PDF of the fitted distribution superimposed on a histogram of the data.

 pd_fixed = makedist ("Rician", "s", 2, "sigma", 1)
 rand ("seed", 2);
 data = random (pd_fixed, 5000, 1);
 pd_fitted = fitdist (data, "Rician")
 plot (pd_fitted)
 msg = "Fitted Rician distribution with s = %0.2f and sigma = %0.2f";
 title (sprintf (msg, pd_fitted.s, pd_fitted.sigma))
***** shared pd, t
 pd = RicianDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.2671, 0.7310, 0.9563, 0.9971, 0.9999], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.8466, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.5120, 0.7310, 0.9563, 0.9971, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.8466, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.8501, 1.2736, 1.6863, 2.2011, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1517, 2.3296, 2.5545, 2.8868, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1.2736, 1.6863, 2.2011, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3296, 2.5545, 2.8868, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.0890, 1e-4);
***** assert (iqr (t), 0.5928, 1e-4);
***** assert (mean (pd), 1.5486, 1e-4);
***** assert (mean (t), 2.5380, 1e-4);
***** assert (median (pd), 1.4755, 1e-4);
***** assert (median (t), 2.4341, 1e-4);
***** assert (pdf (pd, [0:5]), [0, 0.4658, 0.3742, 0.0987, 0.0092, 0.0003], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.4063, 0.3707, 0.0346, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.4864, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 0.7758, 1e-4);
***** assert (std (t), 0.4294, 1e-4);
***** assert (var (pd), 0.6019, 1e-4);
***** assert (var (t), 0.1844, 1e-4);
***** error <RicianDistribution: S must be a non-negative real scalar.> ...
 RicianDistribution(-eps, 1)
***** error <RicianDistribution: S must be a non-negative real scalar.> ...
 RicianDistribution(-1, 1)
***** error <RicianDistribution: S must be a non-negative real scalar.> ...
 RicianDistribution(Inf, 1)
***** error <RicianDistribution: S must be a non-negative real scalar.> ...
 RicianDistribution(i, 1)
***** error <RicianDistribution: S must be a non-negative real scalar.> ...
 RicianDistribution("a", 1)
***** error <RicianDistribution: S must be a non-negative real scalar.> ...
 RicianDistribution([1, 2], 1)
***** error <RicianDistribution: S must be a non-negative real scalar.> ...
 RicianDistribution(NaN, 1)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, 0)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, -1)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, Inf)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, i)
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, "a")
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, [1, 2])
***** error <RicianDistribution: SIGMA must be a positive real scalar.> ...
 RicianDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (RicianDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (RicianDistribution, 2, 3)
***** shared x
 x = gevrnd (1, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (RicianDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (RicianDistribution.fit (x), "parameter", "s", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (RicianDistribution.fit (x), "parameter", {"s", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"s", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (RicianDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RicianDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (RicianDistribution.fit (x), "alpha", 0.01, "parameter", "s", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (RicianDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (RicianDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (RicianDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (RicianDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RicianDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RicianDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (RicianDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (RicianDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (RicianDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (RicianDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (RicianDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (RicianDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RicianDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RicianDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (RicianDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (RicianDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (RicianDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (RicianDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (RicianDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (RicianDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (RicianDistribution)
***** error <truncate: missing input argument.> ...
 truncate (RicianDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (RicianDistribution, 4, 2)
***** shared pd
 pd = RicianDistribution(1, 1);
 pd(2) = RicianDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
97 tests, 97 passed, 0 known failure, 0 skipped
[inst/dist_obj/tLocationScaleDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/tLocationScaleDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a t Location-Scale distribution
 ## with parameters mu = 0, sigma = 1, and nu = 5. Fit a t Location-Scale
 ## distribution to this data and plot a PDF of the fitted distribution
 ## superimposed on a histogram of the data.

 pd_fixed = makedist ("tLocationScale", "mu", 0, "sigma", 1, "nu", 5);
 rand ("seed", 2);
 data = random (pd_fixed, 5000, 1);
 pd_fitted = fitdist (data, "tLocationScale");
 plot (pd_fitted);
 msg = "Fitted t Location-Scale distribution with mu = %0.2f, sigma = %0.2f, nu = %0.2f";
 title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma, pd_fitted.nu));
***** shared pd, t
 pd = tLocationScaleDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0.5, 0.8184, 0.9490, 0.9850, 0.9948, 0.9979], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7841, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.9030, 0.9490, 0.9850, 0.9948, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7841, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.9195, -0.2672, 0.2672, 0.9195, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1559, 2.3533, 2.6223, 3.0432, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2672, 0.2672, 0.9195, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.3533, 2.6223, 3.0432, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.4534, 1e-4);
***** assert (iqr (t), 0.7139, 1e-4);
***** assert (mean (pd), 0, eps);
***** assert (mean (t), 2.6099, 1e-4);
***** assert (median (pd), 0, eps);
***** assert (median (t), 2.4758, 1e-4);
***** assert (pdf (pd, [0:5]), [0.3796, 0.2197, 0.0651, 0.0173, 0.0051, 0.0018], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.4209, 0.3775, 0.1119, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0.2197, 0.1245, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.2910, 1e-4);
***** assert (std (t), 0.4989, 1e-4);
***** assert (var (pd), 1.6667, 1e-4);
***** assert (var (t), 0.2489, 1e-4);
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution(i, 1, 1)
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution(Inf, 1, 1)
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution([1, 2], 1, 1)
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution("a", 1, 1)
***** error <tLocationScaleDistribution: MU must be a real scalar.> ...
 tLocationScaleDistribution(NaN, 1, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 0, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, -1, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, Inf, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, i, 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, "a", 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, [1, 2], 1)
***** error <tLocationScaleDistribution: SIGMA must be a positive real scalar.> ...
 tLocationScaleDistribution(0, NaN, 1)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, 0)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, -1)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, Inf)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, i)
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, "a")
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, [1, 2])
***** error <tLocationScaleDistribution: NU must be a positive real scalar.> ...
 tLocationScaleDistribution(0, 1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (tLocationScaleDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (tLocationScaleDistribution, 2, 3)
***** shared x
 x = tlsrnd (0, 1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "parameter", "mu", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), ...
          "parameter", {"mu", "sigma", "nu", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "nu", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (tLocationScaleDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (tLocationScaleDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "mu", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (tLocationScaleDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (tLocationScaleDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (tLocationScaleDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (tLocationScaleDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (tLocationScaleDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (tLocationScaleDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (tLocationScaleDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (tLocationScaleDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (tLocationScaleDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (tLocationScaleDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (tLocationScaleDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (tLocationScaleDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (tLocationScaleDistribution.fit (x), 4)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (tLocationScaleDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (tLocationScaleDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (tLocationScaleDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (tLocationScaleDistribution)
***** error <truncate: missing input argument.> ...
 truncate (tLocationScaleDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (tLocationScaleDistribution, 4, 2)
***** shared pd
 pd = tLocationScaleDistribution (0, 1, 1);
 pd(2) = tLocationScaleDistribution (0, 1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
102 tests, 102 passed, 0 known failure, 0 skipped
[inst/dist_obj/PiecewiseLinearDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/PiecewiseLinearDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Beta distribution with
 ## parameters a = 2 and b = 5 scaled to [0,10].
 ## Compute empirical CDF, subsample, create PiecewiseLinearDistribution,
 ## and plot the PDF superimposed on a histogram of the data.

 randg ("seed", 2);
 data = betarnd (2, 5, 5000, 1) * 10;
 [f, x] = ecdf (data);
 f = f(1:5:end);
 x = x(1:5:end);
 pd = PiecewiseLinearDistribution (x, f);
 [counts, centers] = hist (data, 50);
 bin_width = centers(2) - centers(1);
 bar (centers, counts / (sum (counts) * bin_width), 1);
 hold on
 vals = min (data):0.1:max (data);
 y = pdf (pd, vals);
 plot (vals, y, "-r", "LineWidth", 2)
 hold off
 title ("Piecewise Linear approximation to scaled Beta(2,5) data")
 legend ("Histogram", "Piecewise PDF")
***** shared pd, t
 load patients
 [f, x] = ecdf (Weight);
 f = f(1:5:end);
 x = x(1:5:end);
 pd = PiecewiseLinearDistribution (x, f);
 t = truncate (pd, 130, 180);
***** assert (cdf (pd, [120, 130, 140, 150, 200]), [0.0767, 0.25, 0.4629, 0.5190, 0.9908], 1e-4);
***** assert (cdf (t, [120, 130, 140, 150, 200]), [0, 0, 0.4274, 0.5403, 1], 1e-4);
***** assert (cdf (pd, [100, 250, NaN]), [0, 1, NaN], 1e-4);
***** assert (cdf (t, [115, 290, NaN]), [0, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [111, 127.5, 136.62, 169.67, 182.17, 202], 1e-2);
***** assert (icdf (t, [0:0.2:1]), [130, 134.15, 139.26, 162.5, 173.99, 180], 1e-2);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NA, 136.62, 169.67, 182.17, 202, NA], 1e-2);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NA, 139.26, 162.5, 173.99, 180, NA], 1e-2);
***** assert (iqr (pd), 50.0833, 1e-4);
***** assert (iqr (t), 36.8077, 1e-4);
***** assert (mean (pd), 153.61, 1e-10);
***** assert (mean (t), 152.311, 1e-3);
***** assert (median (pd), 142, 1e-10);
***** assert (median (t), 141.9462, 1e-4);
***** assert (pdf (pd, [120, 130, 140, 150, 200]), [0.0133, 0.0240, 0.0186, 0.0024, 0.0004], 6e-3);
***** assert (pdf (t, [120, 130, 140, 150, 200]), [0, 0.0482, 0.0373, 0.0048, 0], 1e-4);
***** assert (pdf (pd, [100, 250, NaN]), [0, 0, NaN], 1e-4);
***** assert (pdf (t, [100, 250, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 130), false);
***** assert (any (random (t, 1000, 1) > 180), false);
***** assert (std (pd), 26.5196, 1e-4);
***** assert (std (t), 18.2941, 1e-4);
***** assert (var (pd), 703.2879, 1e-4);
***** assert (var (t), 334.6757, 1e-4);
***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
 PiecewiseLinearDistribution ([0, i], [0, 1])
***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
 PiecewiseLinearDistribution ([0, Inf], [0, 1])
***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
 PiecewiseLinearDistribution (["a", "c"], [0, 1])
***** error <PiecewiseLinearDistribution: X must be a real vector.> ...
 PiecewiseLinearDistribution ([NaN, 1], [0, 1])
***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
 PiecewiseLinearDistribution ([0, 1], [0, i])
***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
 PiecewiseLinearDistribution ([0, 1], [0, Inf])
***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
 PiecewiseLinearDistribution ([0, 1], ["a", "c"])
***** error <PiecewiseLinearDistribution: Fx must be a real vector.> ...
 PiecewiseLinearDistribution ([0, 1], [NaN, 1])
***** error <PiecewiseLinearDistribution: X and FX must be vectors of equal size.> ...
 PiecewiseLinearDistribution ([0, 1], [0, 0.5, 1])
***** error <PiecewiseLinearDistribution: X and FX must be at least two-elements long.> ...
 PiecewiseLinearDistribution ([0], [1])
***** error <PiecewiseLinearDistribution: FX must be bounded in the range> ...
 PiecewiseLinearDistribution ([0, 0.5, 1], [0, 1, 1.5])
***** error <cdf: invalid argument for upper tail.> ...
 cdf (PiecewiseLinearDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (PiecewiseLinearDistribution, 2, 3)
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (PiecewiseLinearDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (PiecewiseLinearDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (PiecewiseLinearDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (PiecewiseLinearDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PiecewiseLinearDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PiecewiseLinearDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PiecewiseLinearDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (PiecewiseLinearDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (PiecewiseLinearDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (PiecewiseLinearDistribution, "invalidNAME", "pdf")
***** error <plot: 'probability' PlotType is not supported for 'PiecewiseLinearDistribution'.> ...
 plot (PiecewiseLinearDistribution, "PlotType", "probability")
***** error <truncate: missing input argument.> ...
 truncate (PiecewiseLinearDistribution)
***** error <truncate: missing input argument.> ...
 truncate (PiecewiseLinearDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (PiecewiseLinearDistribution, 4, 2)
***** shared pd
 pd = PiecewiseLinearDistribution ();
 pd(2) = PiecewiseLinearDistribution ();
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
63 tests, 63 passed, 0 known failure, 0 skipped
[inst/dist_obj/WeibullDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/WeibullDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Weibull distribution with
 ## parameters lambda = 1 and k = 2.  Fit a Weibull distribution to this data and plot
 ## a PDF of the fitted distribution superimposed on a histogram of a data.

 pd_fixed = makedist ("Weibull", "lambda", 1, "k", 2)
 rand ("seed", 2);
 data = random (pd_fixed, 5000, 1);
 pd_fitted = fitdist (data, "Weibull")
 plot (pd_fitted)
 msg = "Fitted Weibull distribution with lambda = %0.2f and k = %0.2f";
 title (sprintf (msg, pd_fitted.lambda, pd_fitted.k))
***** shared pd, t
 pd = WeibullDistribution;
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.7769, 0.8647, 0.9502, 0.9817, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0, 0.7311, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.0986, 1e-4);
***** assert (iqr (t), 0.8020, 1e-4);
***** assert (mean (pd), 1, 1e-14);
***** assert (mean (t), 2.6870, 1e-4);
***** assert (median (pd), 0.6931, 1e-4);
***** assert (median (t), 2.5662, 1e-4);
***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0.2231, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1, 1e-14);
***** assert (std (t), 0.5253, 1e-4);
***** assert (var (pd), 1, 1e-14);
***** assert (var (t), 0.2759, 1e-4);
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(0, 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(-1, 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(Inf, 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(i, 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution("a", 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution([1, 2], 1)
***** error <WeibullDistribution: LAMBDA must be a positive real scalar.> ...
 WeibullDistribution(NaN, 1)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, 0)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, -1)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, Inf)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, i)
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, "a")
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, [1, 2])
***** error <WeibullDistribution: K must be a positive real scalar.> ...
 WeibullDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (WeibullDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (WeibullDistribution, 2, 3)
***** shared x
 x = wblrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (WeibullDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (WeibullDistribution.fit (x), "parameter", "k", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (WeibullDistribution.fit (x), "parameter", {"lambda", "k", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"lambda", "k", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (WeibullDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (WeibullDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (WeibullDistribution.fit (x), "alpha", 0.01, "parameter", "k", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (WeibullDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (WeibullDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (WeibullDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (WeibullDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (WeibullDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (WeibullDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (WeibullDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (WeibullDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (WeibullDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (WeibullDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (WeibullDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (WeibullDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (WeibullDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (WeibullDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (WeibullDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (WeibullDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (WeibullDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (WeibullDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (WeibullDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (WeibullDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (WeibullDistribution)
***** error <truncate: missing input argument.> ...
 truncate (WeibullDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (WeibullDistribution, 4, 2)
***** shared pd
 pd = WeibullDistribution(1, 1);
 pd(2) = WeibullDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
97 tests, 97 passed, 0 known failure, 0 skipped
[inst/dist_obj/NormalDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/NormalDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Normal distribution with
 ## parameters mu = 0 and sigma = 1.  Fit a Normal distribution to this data and plot
 ## a PDF of the fitted distribution superimposed on a histogram of the data.

 pd_fixed = makedist ("Normal", "mu", 0, "sigma", 1)
 randn ("seed", 2);
 data = random (pd_fixed, 5000, 1);
 pd_fitted = fitdist (data, "Normal")
 plot (pd_fitted)
 msg = "Fitted Normal distribution with mu = %0.2f and sigma = %0.2f";
 title (sprintf (msg, pd_fitted.mu, pd_fitted.sigma))
***** shared pd, t
 pd = NormalDistribution;
 t = truncate (pd, -2, 2);
***** assert (cdf (pd, [0:5]), [0.5, 0.8413, 0.9772, 0.9987, 1, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0.5, 0.8576, 1, 1, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.9332, 0.9772, 0.9987, 1], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0.9538, 1, 1, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -0.8416, -0.2533, 0.2533, 0.8416, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [-2, -0.7938, -0.2416, 0.2416, 0.7938, 2], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2533, 0.2533, 0.8416, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, -0.2416, 0.2416, 0.7938, 2, NaN], 1e-4);
***** assert (iqr (pd), 1.3490, 1e-4);
***** assert (iqr (t), 1.2782, 1e-4);
***** assert (mean (pd), 0);
***** assert (mean (t), 0, 3e-16);
***** assert (median (pd), 0);
***** assert (median (t), 0, 3e-16);
***** assert (pdf (pd, [0:5]), [0.3989, 0.2420, 0.0540, 0.0044, 0.0001, 0], 1e-4);
***** assert (pdf (t, [0:5]), [0.4180, 0.2535, 0.0566, 0, 0, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0.2420, 0.2420, 0.0540, 0.0044, 0.0001, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0.2535, 0.2535, 0.0566, 0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < -2), false);
***** assert (any (random (t, 1000, 1) > 2), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.8796, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.7737, 1e-4);
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution(Inf, 1)
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution(i, 1)
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution("a", 1)
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution([1, 2], 1)
***** error <NormalDistribution: MU must be a real scalar.> ...
 NormalDistribution(NaN, 1)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, 0)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, -1)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, Inf)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, i)
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, "a")
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, [1, 2])
***** error <NormalDistribution: SIGMA must be a positive real scalar.> ...
 NormalDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NormalDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (NormalDistribution, 2, 3)
***** shared x
 x = normrnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (NormalDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (NormalDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NormalDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (NormalDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NormalDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (NormalDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (NormalDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NormalDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (NormalDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (NormalDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NormalDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NormalDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (NormalDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NormalDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (NormalDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (NormalDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (NormalDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (NormalDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NormalDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NormalDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (NormalDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (NormalDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (NormalDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (NormalDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NormalDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (NormalDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (NormalDistribution)
***** error <truncate: missing input argument.> ...
 truncate (NormalDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (NormalDistribution, 4, 2)
***** shared pd
 pd = NormalDistribution(1, 1);
 pd(2) = NormalDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/LogisticDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/LogisticDistribution.m
***** shared pd, t
 pd = LogisticDistribution (0, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0.5, 0.7311, 0.8808, 0.9526, 0.9820, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7091, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.8176, 0.8808, 0.9526, 0.9820], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7091, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [-Inf, -1.3863, -0.4055, 0.4055, 1.3863, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.2088, 2.4599, 2.7789, 3.2252, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, -0.4055, 0.4055, 1.3863, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4599, 2.7789, 3.2252, 4, NaN], 1e-4);
***** assert (iqr (pd), 2.1972, 1e-4);
***** assert (iqr (t), 0.8286, 1e-4);
***** assert (mean (pd), 0, 1e-4);
***** assert (mean (t), 2.7193, 1e-4);
***** assert (median (pd), 0);
***** assert (median (t), 2.6085, 1e-4);
***** assert (pdf (pd, [0:5]), [0.25, 0.1966, 0.1050, 0.0452, 0.0177, 0.0066], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.0373, 0.4463, 0.1745, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0.1966, 0.1966, 0.1050, 0.0452, 0.0177, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.0373, 0.4463, 0.1745, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.8138, 1e-4);
***** assert (std (t), 0.5320, 1e-4);
***** assert (var (pd), 3.2899, 1e-4);
***** assert (var (t), 0.2830, 1e-4);
***** error <LogisticDistribution: MU must be a finite real scalar.> ...
 LogisticDistribution(Inf, 1)
***** error <LogisticDistribution: MU must be a finite real scalar.> ...
 LogisticDistribution(i, 1)
***** error <LogisticDistribution: MU must be a finite real scalar.> ...
 LogisticDistribution("a", 1)
***** error <LogisticDistribution: MU must be a finite real scalar.> ...
 LogisticDistribution([1, 2], 1)
***** error <LogisticDistribution: MU must be a finite real scalar.> ...
 LogisticDistribution(NaN, 1)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, 0)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, -1)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, Inf)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, i)
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, "a")
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, [1, 2])
***** error <LogisticDistribution: SIGMA must be a positive real scalar.> ...
 LogisticDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LogisticDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (LogisticDistribution, 2, 3)
***** shared x
 x = logirnd (1, 1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (LogisticDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (LogisticDistribution.fit (x), "parameter", "mu", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LogisticDistribution.fit (x), "parameter", {"mu", "sigma", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"mu", "sigma", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (LogisticDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LogisticDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (LogisticDistribution.fit (x), "alpha", 0.01, "parameter", "mu", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (LogisticDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LogisticDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (LogisticDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (LogisticDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LogisticDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LogisticDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (LogisticDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LogisticDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (LogisticDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (LogisticDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (LogisticDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (LogisticDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LogisticDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LogisticDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (LogisticDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (LogisticDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (LogisticDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (LogisticDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LogisticDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (LogisticDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (LogisticDistribution)
***** error <truncate: missing input argument.> ...
 truncate (LogisticDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (LogisticDistribution, 4, 2)
***** shared pd
 pd = LogisticDistribution(1, 1);
 pd(2) = LogisticDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
95 tests, 95 passed, 0 known failure, 0 skipped
[inst/dist_obj/GammaDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/GammaDistribution.m
***** shared pd, t
 pd = GammaDistribution (1, 1);
 t = truncate (pd, 2, 4);
***** assert (cdf (pd, [0:5]), [0, 0.6321, 0.8647, 0.9502, 0.9817, 0.9933], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0, 0.7311, 1, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7769, 0.8647, 0.9502, 0.9817], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0, 0.7311, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0.2231, 0.5108, 0.9163, 1.6094, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2.1899, 2.4244, 2.7315, 3.1768, 4], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 0.5108, 0.9163, 1.6094, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2.4244, 2.7315, 3.1768, 4, NaN], 1e-4);
***** assert (iqr (pd), 1.0986, 1e-4);
***** assert (iqr (t), 0.8020, 1e-4);
***** assert (mean (pd), 1);
***** assert (mean (t), 2.6870, 1e-4);
***** assert (median (pd), 0.6931, 1e-4);
***** assert (median (t), 2.5662, 1e-4);
***** assert (pdf (pd, [0:5]), [1, 0.3679, 0.1353, 0.0498, 0.0183, 0.0067], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 1.1565, 0.4255, 0.1565, 0], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1353, 0.0498, 0.0183, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 1.1565, 0.4255, 0.1565, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.5253, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.2759, 1e-4);
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution(0, 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution(Inf, 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution(i, 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution("a", 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution([1, 2], 1)
***** error <GammaDistribution: A must be a positive real scalar.> ...
 GammaDistribution(NaN, 1)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, 0)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, -1)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, Inf)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, i)
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, "a")
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, [1, 2])
***** error <GammaDistribution: B must be a positive real scalar.> ...
 GammaDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GammaDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (GammaDistribution, 2, 3)
***** shared x
 x = gamrnd (1, 1, [100, 1]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (GammaDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (GammaDistribution.fit (x), "parameter", "a", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GammaDistribution.fit (x), "parameter", {"a", "b", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"a", "b", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (GammaDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GammaDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (GammaDistribution.fit (x), "alpha", 0.01, "parameter", "a", ...
          "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (GammaDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GammaDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (GammaDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (GammaDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GammaDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GammaDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (GammaDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GammaDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (GammaDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (GammaDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (GammaDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (GammaDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GammaDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GammaDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (GammaDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (GammaDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (GammaDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (GammaDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GammaDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (GammaDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (GammaDistribution)
***** error <truncate: missing input argument.> ...
 truncate (GammaDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (GammaDistribution, 4, 2)
***** shared pd
 pd = GammaDistribution(1, 1);
 pd(2) = GammaDistribution(1, 3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
96 tests, 96 passed, 0 known failure, 0 skipped
[inst/dist_obj/BinomialDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/BinomialDistribution.m
***** shared pd, t, t_inf
 pd = BinomialDistribution (5, 0.5);
 t = truncate (pd, 2, 4);
 t_inf = truncate (pd, 2, Inf);
***** assert (cdf (pd, [0:5]), [0.0312, 0.1875, 0.5, 0.8125, 0.9688, 1], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0.4, 0.8, 1, 1], 1e-4);
***** assert (cdf (t_inf, [0:5]), [0, 0, 0.3846, 0.7692, 0.9615, 1], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4, NaN]), [0.1875, 0.5, 0.8125, 0.9688, NaN], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4, NaN]), [0, 0.4, 0.8, 1, NaN], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 2, 2, 3, 3, 5], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 3, 3, 4], 1e-4);
***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 3, 3, 4, 5], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 5, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 3, 3, 4, NaN], 1e-4);
***** assert (iqr (pd), 1);
***** assert (iqr (t), 1);
***** assert (mean (pd), 2.5, 1e-10);
***** assert (mean (t), 2.8, 1e-10);
***** assert (mean (t_inf), 2.8846, 1e-4);
***** assert (median (pd), 2.5);
***** assert (median (t), 3);
***** assert (pdf (pd, [0:5]), [0.0312, 0.1562, 0.3125, 0.3125, 0.1562, 0.0312], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.4, 0.4, 0.2, 0], 1e-4);
***** assert (pdf (t_inf, [0:5]), [0, 0, 0.3846, 0.3846, 0.1923, 0.0385], 1e-4);
***** assert (pdf (pd, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (pdf (t, [-1, 1.5, NaN]), [0, 0, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1.1180, 1e-4);
***** assert (std (t), 0.7483, 1e-4);
***** assert (std (t_inf), 0.8470, 1e-4);
***** assert (var (pd), 1.2500, 1e-4);
***** assert (var (t), 0.5600, 1e-4);
***** assert (var (t_inf), 0.7175, 1e-4);
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution(Inf, 0.5)
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution(i, 0.5)
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution("a", 0.5)
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution([1, 2], 0.5)
***** error <BinomialDistribution: N must be a positive integer scalar.> ...
 BinomialDistribution(NaN, 0.5)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, 1.01)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, -0.01)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, Inf)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, i)
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, "a")
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, [1, 2])
***** error <BinomialDistribution: p must be a real scalar bounded in the range> ...
 BinomialDistribution(1, NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BinomialDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (BinomialDistribution, 2, 3)
***** shared x
 rand ("seed", 2);
 x = binornd (5, 0.5, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "parameter", "p", ...
          "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), ...
          "parameter", {"N", "p", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
          "parameter", {"N", "p", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (BinomialDistribution.fit (x, 6), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BinomialDistribution.fit (x, 6), "parameter", "N")
***** error <paramci: unknown distribution parameter.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
          "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
          "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (BinomialDistribution.fit (x, 6), "alpha", 0.01, ...
          "parameter", "p", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (BinomialDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BinomialDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (BinomialDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (BinomialDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BinomialDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BinomialDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (BinomialDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BinomialDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (BinomialDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (BinomialDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (BinomialDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (BinomialDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BinomialDistribution.fit (x, 6), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BinomialDistribution.fit (x, 6), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (BinomialDistribution.fit (x, 6), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (BinomialDistribution.fit (x, 6), 2, {[1 2 3]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (BinomialDistribution)
***** error <truncate: missing input argument.> ...
 truncate (BinomialDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (BinomialDistribution, 4, 2)
***** shared pd
 pd = BinomialDistribution(1, 0.5);
 pd(2) = BinomialDistribution(1, 0.6);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
102 tests, 102 passed, 0 known failure, 0 skipped
[inst/dist_obj/PoissonDistribution.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_obj/PoissonDistribution.m
***** demo
 ## Generate a data set of 5000 random samples from a Poisson distribution with
 ## parameter lambda = 5.  Fit a Poisson distribution to this data and plot
 ## a PDF of the fitted distribution superimposed on a histogram of the data.

 pd_fixed = makedist ("Poisson", "lambda", 5)
 rand ("seed", 2);
 data = random (pd_fixed, 5000, 1);
 pd_fitted = fitdist (data, "Poisson")
 plot (pd_fitted)
 msg = "Fitted Poisson distribution with lambda = %0.2f";
 title (sprintf (msg, pd_fitted.lambda))
***** shared pd, t, t_inf
 pd = PoissonDistribution;
 t = truncate (pd, 2, 4);
 t_inf = truncate (pd, 2, Inf);
***** assert (cdf (pd, [0:5]), [0.3679, 0.7358, 0.9197, 0.9810, 0.9963, 0.9994], 1e-4);
***** assert (cdf (t, [0:5]), [0, 0, 0.7059, 0.9412, 1, 1], 1e-4);
***** assert (cdf (t_inf, [0:5]), [0, 0, 0.6961, 0.9281, 0.9861, 0.9978], 1e-4);
***** assert (cdf (pd, [1.5, 2, 3, 4]), [0.7358, 0.9197, 0.9810, 0.9963], 1e-4);
***** assert (cdf (t, [1.5, 2, 3, 4]), [0, 0.7059, 0.9412, 1], 1e-4);
***** assert (icdf (pd, [0:0.2:1]), [0, 0, 1, 1, 2, Inf], 1e-4);
***** assert (icdf (t, [0:0.2:1]), [2, 2, 2, 2, 3, 4], 1e-4);
***** assert (icdf (t_inf, [0:0.2:1]), [2, 2, 2, 2, 3, Inf], 1e-4);
***** assert (icdf (pd, [-1, 0.4:0.2:1, NaN]), [NaN, 1, 1, 2, Inf, NaN], 1e-4);
***** assert (icdf (t, [-1, 0.4:0.2:1, NaN]), [NaN, 2, 2, 3, 4, NaN], 1e-4);
***** assert (iqr (pd), 2);
***** assert (iqr (t), 1);
***** assert (mean (pd), 1);
***** assert (mean (t), 2.3529, 1e-4);
***** assert (mean (t_inf), 2.3922, 1e-4);
***** assert (median (pd), 1);
***** assert (median (t), 2);
***** assert (median (t_inf), 2);
***** assert (pdf (pd, [0:5]), [0.3679, 0.3679, 0.1839, 0.0613, 0.0153, 0.0031], 1e-4);
***** assert (pdf (t, [0:5]), [0, 0, 0.7059, 0.2353, 0.0588, 0], 1e-4);
***** assert (pdf (t_inf, [0:5]), [0, 0, 0.6961, 0.2320, 0.0580, 0.0116], 1e-4);
***** assert (pdf (pd, [-1, 1:4, NaN]), [0, 0.3679, 0.1839, 0.0613, 0.0153, NaN], 1e-4);
***** assert (pdf (t, [-1, 1:4, NaN]), [0, 0, 0.7059, 0.2353, 0.0588, NaN], 1e-4);
***** assert (isequal (size (random (pd, 100, 50)), [100, 50]))
***** assert (any (random (t, 1000, 1) < 2), false);
***** assert (any (random (t, 1000, 1) > 4), false);
***** assert (std (pd), 1);
***** assert (std (t), 0.5882, 1e-4);
***** assert (std (t_inf), 0.6738, 1e-4);
***** assert (var (pd), 1);
***** assert (var (t), 0.3460, 1e-4);
***** assert (var (t_inf), 0.4540, 1e-4);
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(0)
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(-1)
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(Inf)
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(i)
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution("a")
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution([1, 2])
***** error <PoissonDistribution: LAMBDA must be a positive real scalar.> ...
 PoissonDistribution(NaN)
***** error <cdf: invalid argument for upper tail.> ...
 cdf (PoissonDistribution, 2, "uper")
***** error <cdf: invalid argument for upper tail.> ...
 cdf (PoissonDistribution, 2, 3)
***** shared x
 x = poissrnd (1, [1, 100]);
***** error <paramci: optional arguments must be in NAME-VALUE pairs.> ...
 paramci (PoissonDistribution.fit (x), "alpha")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 1)
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", [0.5 2])
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", "")
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", {0.05})
***** error <paramci: invalid VALUE for 'Alpha' argument.> ...
 paramci (PoissonDistribution.fit (x), "parameter", "lambda", "alpha", {0.05})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (PoissonDistribution.fit (x), "parameter", {"lambda", "param"})
***** error <paramci: invalid VALUE size for 'Parameter' argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
          "parameter", {"lambda", "param"})
***** error <paramci: unknown distribution parameter.> ...
 paramci (PoissonDistribution.fit (x), "parameter", "param")
***** error <paramci: unknown distribution parameter.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0.01, "parameter", "param")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (PoissonDistribution.fit (x), "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0.01, "NAME", "value")
***** error <paramci: invalid NAME for optional argument.> ...
 paramci (PoissonDistribution.fit (x), "alpha", 0.01, ...
          "parameter", "lambda", "NAME", "value")
***** error <plot: optional arguments must be in NAME-VALUE pairs.> ...
 plot (PoissonDistribution, "Parent")
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (PoissonDistribution, "PlotType", 12)
***** error <plot: invalid VALUE size for 'Parameter' argument.> ...
 plot (PoissonDistribution, "PlotType", {"pdf", "cdf"})
***** error <plot: invalid VALUE for 'PlotType' argument.> ...
 plot (PoissonDistribution, "PlotType", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PoissonDistribution, "Discrete", "pdfcdf")
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PoissonDistribution, "Discrete", [1, 0])
***** error <plot: invalid VALUE for 'Discrete' argument.> ...
 plot (PoissonDistribution, "Discrete", {true})
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (PoissonDistribution, "Parent", 12)
***** error <plot: invalid VALUE for 'Parent' argument.> ...
 plot (PoissonDistribution, "Parent", "hax")
***** error <plot: invalid NAME for optional argument.> ...
 plot (PoissonDistribution, "invalidNAME", "pdf")
***** error <plot: no fitted DATA to plot a probability plot.> ...
 plot (PoissonDistribution, "PlotType", "probability")
***** error <proflik: no fitted data available.> ...
 proflik (PoissonDistribution, 2)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (PoissonDistribution.fit (x), 3)
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (PoissonDistribution.fit (x), [1, 2])
***** error <proflik: PNUM must be a scalar number indexing a non-fixed parameter.> ...
 proflik (PoissonDistribution.fit (x), {1})
***** error <proflik: SETPARAM must be a numeric vector.> ...
 proflik (PoissonDistribution.fit (x), 1, ones (2))
***** error <proflik: missing VALUE for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display")
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", 1)
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", {1})
***** error <proflik: invalid VALUE type for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", {"on"})
***** error <proflik: invalid VALUE size for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", ["on"; "on"])
***** error <proflik: invalid VALUE for 'Display' argument.> ...
 proflik (PoissonDistribution.fit (x), 1, "Display", "onnn")
***** error <proflik: invalid NAME for optional arguments.> ...
 proflik (PoissonDistribution.fit (x), 1, "NAME", "on")
***** error <proflik: invalid optional argument.> ...
 proflik (PoissonDistribution.fit (x), 1, {"NAME"}, "on")
***** error <proflik: invalid optional argument.> ...
 proflik (PoissonDistribution.fit (x), 1, {[1 2 3 4]}, "Display", "on")
***** error <truncate: missing input argument.> ...
 truncate (PoissonDistribution)
***** error <truncate: missing input argument.> ...
 truncate (PoissonDistribution, 2)
***** error <truncate: invalid lower upper limits.> ...
 truncate (PoissonDistribution, 4, 2)
***** shared pd
 pd = PoissonDistribution(1);
 pd(2) = PoissonDistribution(3);
***** error <cdf: requires a scalar probability distribution.> cdf (pd, 1)
***** error <icdf: requires a scalar probability distribution.> icdf (pd, 0.5)
***** error <iqr: requires a scalar probability distribution.> iqr (pd)
***** error <mean: requires a scalar probability distribution.> mean (pd)
***** error <median: requires a scalar probability distribution.> median (pd)
***** error <negloglik: requires a scalar probability distribution.> negloglik (pd)
***** error <paramci: requires a scalar probability distribution.> paramci (pd)
***** error <pdf: requires a scalar probability distribution.> pdf (pd, 1)
***** error <plot: requires a scalar probability distribution.> plot (pd)
***** error <proflik: requires a scalar probability distribution.> proflik (pd, 2)
***** error <random: requires a scalar probability distribution.> random (pd)
***** error <std: requires a scalar probability distribution.> std (pd)
***** error <truncate: requires a scalar probability distribution.> ...
 truncate (pd, 2, 4)
***** error <var: requires a scalar probability distribution.> var (pd)
97 tests, 97 passed, 0 known failure, 0 skipped
[inst/qqplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/qqplot.m
***** test
 hf = figure ("visible", "off");
 unwind_protect
   qqplot ([2 3 3 4 4 5 6 5 6 7 8 9 8 7 8 9 0 8 7 6 5 4 6 13 8 15 9 9]);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error qqplot ()
***** error <qqplot: X must be a numeric vector> qqplot ({1})
***** error <qqplot: X must be a numeric vector> qqplot (ones (2,2))
***** error <qqplot: X must be a numeric vector> qqplot (1, "foobar")
***** error <qqplot: no inverse CDF found> qqplot ([1 2 3], "foobar")
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/hmmviterbi.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/hmmviterbi.m
***** test
 sequence = [1, 2, 1, 1, 1, 2, 2, 1, 2, 3, 3, 3, ...
             3, 2, 3, 1, 1, 1, 1, 3, 3, 2, 3, 1, 3];
 transprob = [0.8, 0.2; 0.4, 0.6];
 outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
 vpath = hmmviterbi (sequence, transprob, outprob);
 expected = [1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, ...
             1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1];
 assert (vpath, expected);
***** test
 sequence = {"A", "B", "A", "A", "A", "B", "B", "A", "B", "C", "C", "C", ...
             "C", "B", "C", "A", "A", "A", "A", "C", "C", "B", "C", "A", "C"};
 transprob = [0.8, 0.2; 0.4, 0.6];
 outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
 symbols = {"A", "B", "C"};
 statenames = {"One", "Two"};
 vpath = hmmviterbi (sequence, transprob, outprob, "symbols", symbols, ...
                                                   "statenames", statenames);
 expected = {"One", "One", "Two", "Two", "Two", "One", "One", "One", ...
             "One", "One", "One", "One", "One", "One", "One", "Two", ...
             "Two", "Two", "Two", "One", "One", "One", "One", "One", "One"};
 assert (vpath, expected);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/geomean.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/geomean.m
***** test
 x = [0:10];
 y = [x;x+5;x+10];
 assert (geomean (x), 0);
 m = [0 9.462942809849169 14.65658770861967];
 assert (geomean (y, 2), m', 4e-14);
 assert (geomean (y, "all"), 0);
 y(2,4) = NaN;
 m(2) = 9.623207231679554;
 assert (geomean (y, 2), [0 NaN m(3)]', 4e-14);
 assert (geomean (y', "omitnan"), m, 4e-14);
 z = y + 20;
 assert (geomean (z, "all"), NaN);
 assert (geomean (z, "all", "includenan"), NaN);
 assert (geomean (z, "all", "omitnan"), 29.59298474535024, 4e-14);
 m = [24.79790781765634 NaN 34.85638839503932];
 assert (geomean (z'), m, 4e-14);
 assert (geomean (z', "includenan"), m, 4e-14);
 m(2) = 30.02181156156319;
 assert (geomean (z', "omitnan"), m, 4e-14);
 assert (geomean (z, 2, "omitnan"), m', 4e-14);
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 assert (size (geomean (x, [3 2])), [10 1 1 3]);
 assert (size (geomean (x, [1 2])), [1 1 6 3]);
 assert (size (geomean (x, [1 2 4])), [1 1 6]);
 assert (size (geomean (x, [1 4 3])), [1 40]);
 assert (size (geomean (x, [1 2 3 4])), [1 1]);
***** test
 x = repmat ([1:20;6:25], [5 2 6 3]);
 m = repmat ([8.304361203739333;14.3078118884256], [5 1 1 3]);
 assert (geomean (x, [3 2]), m, 4e-13);
 x(2,5,6,3) = NaN;
 m(2,3) = NaN;
 assert (geomean (x, [3 2]), m, 4e-13);
 m(2,3) = 14.3292729579901;
 assert (geomean (x, [3 2], "omitnan"), m, 4e-13);
***** error <geomean: X must contain real nonnegative values.> geomean ("char")
***** error <geomean: X must contain real nonnegative values.> geomean ([1 -1 3])
***** error <geomean: DIM must be a positive integer scalar or vector.> ...
 geomean (repmat ([1:20;6:25], [5 2 6 3 5]), -1)
***** error <geomean: DIM must be a positive integer scalar or vector.> ...
 geomean (repmat ([1:20;6:25], [5 2 6 3 5]), 0)
***** error <geomean: VECDIM must contain non-repeating positive integers.> ...
 geomean (repmat ([1:20;6:25], [5 2 6 3 5]), [1 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/makima.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/makima.m
***** test
 % 1. Basic linear-like data
 x = [1; 2; 3; 4];
 y = [2; 4; 6; 8];
 xi = [1.5; 2.5; 3.5];
 yi = makima (x, y, xi);
 assert (yi, [3; 5; 7], 1e-12);
***** test
 % 2. Nonlinear dataset (finite check)
 x = [0; 1; 2; 3; 4];
 y = [0; 1; 0; 1; 0];
 xi = linspace (0,4,20)';
 yi = makima (x, y, xi);
 assert (all (isfinite (yi)));
***** test
 % 3. makima(y, xq) syntax
 y = [10; 20; 30];
 xq = [1; 2; 3];
 yi = makima (y, xq);
 assert (yi, y, 1e-12);
***** test
 % 4. Matrix y input (multiple columns)
 x = [1; 3; 5];
 y = [1 2; 3 4; 2 6];
 xi = 2;
 yi = makima (x, y, xi);
 assert (columns(yi), 2);
 assert (all (isfinite (yi)));
 % Column 1 exact value (1917/832)
 assert (yi(1), 2.3040865384615385, 1e-12);
 % Column 2 exact value (Linear)
 assert (yi(2), 3.000, 1e-12);
***** test
 % 5. Extrapolation enabled
 x = [1; 2; 3];
 y = [5; 10; 15];
 xi = [0; 4];
 yi = makima (x, y, xi, "extrap");
 assert (all (isfinite (yi)));
 assert (yi, [0; 20], 1e-12);
***** test
 % 6. Default NaN extrapolation
 x = [1; 2; 3];
 y = [5; 10; 15];
 xi = [0; 4];
 yi = makima (x, y, xi);
 assert (isnan (yi(1)));
 assert (isnan (yi(2)));
***** test
 % 7. Complex interpolation
 x = [1; 2; 4];
 y = [1+2i; 2+3i; 4+8i];
 xi = 3;
 yi = makima (x, y, xi);
 assert (yi, 3 + 5.09767206477733i, 1e-12);
 assert (iscomplex (yi));
***** test
 % 8. Single Point Input (1x1)
 x = 5;
 y = 12;
 xi = 5;
 yi = makima (x, y, xi);
 assert (yi, 12, 1e-12);
***** test
 % 9. Two-point interpolation (Linear fallback)
 x = [1; 5];
 y = [10; 30];
 xi = 3;
 yi = makima (x, y, xi);
 assert (yi, 20, 1e-12);
***** test
 % 10. Single Precision Input
 x = single ([1; 2; 3]);
 y = single ([10; 20; 30]);
 xi = single (1.5);
 yi = makima (x, y, xi);
 assert (isa (yi, "single"));
 assert (yi, single (15), 1e-6);
***** test
 % 11. Row vector inputs (Orientation check)
 x = [1 2 3];
 y = [4 5 6];
 xi = [1.5 2.5];
 yi = makima (x, y, xi);
 assert (yi, [4.5 5.5], 1e-12);
***** test
 % 12. Step function (Overshoot Check)
 x = [1 2 3 4 5 6];
 y = [0 0 1 1 0 0];
 xi = [2.5 3.5 4.5];
 yi = makima (x, y, xi);
 expected_12 = [0.5000, 1.1250, 0.5000];
 assert (yi, expected_12, 1e-12);
***** test
 % 13. Runge function (Oscillation Check)
 x = linspace (-1, 1, 7)';
 y = 1 ./ (1 + 25 * x.^2);
 xi = [-0.5; 0.1; 0.5];
 yi = makima (x, y, xi);
 expected_13 = [0.148690385982729; 0.857734549516009; 0.148690385982729];
 assert (yi, expected_13, 1e-12);
***** test
 % 14. Constant Slopes / Zero Weights
 x = [1; 2; 3; 4; 5];
 y = [1; 1; 1; 1; 1];
 xi = 3.5;
 yi = makima (x, y, xi);
 expected_14 = [1];
 assert (yi, expected_14, 1e-12);
***** test
 % 15. Empty xq input
 x = [1; 2; 3];
 y = [4; 5; 6];
 xi = [];
 yi = makima (x, y, xi);
 assert (isempty (yi));
 assert (iscolumn (yi)); % Ensure it defaults to column output
***** test
 % 16. Wide range of y-values
 x = [1e-10; 2e-10; 3e-10; 4e-10];
 y = [1e10; 2e10; 3e10; 4e10];
 xi = 2.5e-10;
 yi = makima (x, y, xi);
 assert (yi, 2.5e10, 1e-6); % Using a slightly relaxed tolerance
***** test
 % 17. Single column matrix input (ensures nc=1 logic works)
 x = [1; 2; 3];
 y = [10; 20; 30];
 xi = [1.5 2.5]; % Row input
 yi = makima (x, y, xi);
 assert (yi, [15 25], 1e-12);
 assert (isrow (yi));
***** error<makima: x must be strictly increasing>
 makima ([1 1 2], [3 4 5], 1.5)
***** error<makima: x and y must have the same number of rows>
 makima ([1;2;3], [1;2], 2)
***** error<makima: invalid number of inputs>
 makima (1)
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/knnsearch.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/knnsearch.m
***** demo
 ## find 10 nearest neighbour of a point using different distance metrics
 ## and compare the results by plotting
 load fisheriris
 X = meas(:,3:4);
 Y = species;
 point = [5, 1.45];

 ## calculate 10 nearest-neighbours by minkowski distance
 [id, d] = knnsearch (X, point, "K", 10);

 ## calculate 10 nearest-neighbours by minkowski distance
 [idm, dm] = knnsearch (X, point, "K", 10, "distance", "minkowski", "p", 5);

 ## calculate 10 nearest-neighbours by chebychev distance
 [idc, dc] = knnsearch (X, point, "K", 10, "distance", "chebychev");

 ## plotting the results
 gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
 title ("Fisher's Iris Data - Nearest Neighbors with different types of distance metrics");
 xlabel("Petal length (cm)");
 ylabel("Petal width (cm)");

 line (point(1), point(2), "marker", "X", "color", "k", ...
       "linewidth", 2, "displayname", "query point")
 line (X(id,1), X(id,2), "color", [0.5 0.5 0.5], "marker", "o", ...
       "linestyle", "none", "markersize", 10, "displayname", "euclidean")
 line (X(idm,1), X(idm,2), "color", [0.5 0.5 0.5], "marker", "d", ...
       "linestyle", "none", "markersize", 10, "displayname", "Minkowski")
 line (X(idc,1), X(idc,2), "color", [0.5 0.5 0.5], "marker", "p", ...
       "linestyle", "none", "markersize", 10, "displayname", "chebychev")
 xlim ([4.5 5.5]);
 ylim ([1 2]);
 axis square;
***** demo
 ## knnsearch on iris dataset using kdtree method
 load fisheriris
 X = meas(:,3:4);
 gscatter (X(:,1), X(:,2), species, [.75 .75 0; 0 .75 .75; .75 0 .75], ".", 20);
 title ("Fisher's iris dataset : Nearest Neighbors with kdtree search");

 ## new point to be predicted
 point = [5 1.45];

 line (point(1), point(2), "marker", "X", "color", "k", ...
       "linewidth", 2, "displayname", "query point")

 ## knnsearch using kdtree method
 [idx, d] = knnsearch (X, point, "K", 10, "NSMethod", "kdtree");

 ## plotting predicted neighbours
 line (X(idx,1), X(idx,2), "color", [0.5 0.5 0.5], "marker", "o", ...
       "linestyle", "none", "markersize", 10, ...
       "displayname", "nearest neighbour")
 xlim ([4 6])
 ylim ([1 3])
 axis square
 ## details of predicted labels
 tabulate (species(idx))

 ctr = point - d(end);
 diameter = 2 * d(end);
 ##  Draw a circle around the 10 nearest neighbors.
 h = rectangle ("position", [ctr, diameter, diameter], "curvature", [1 1]);

 ## here only 8 neighbours are plotted instead of 10 since the dataset
 ## contains duplicate values
***** shared X, Y
 X = [1, 2, 3, 4; 2, 3, 4, 5; 3, 4, 5, 6];
 Y = [1, 2, 2, 3; 2, 3, 3, 4];
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "euclidean");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * sqrt (2));
***** test
 eucldist = @(v,m) sqrt(sumsq(repmat(v,rows(m),1)-m,2));
 [idx, D] = knnsearch (X, Y, "Distance", eucldist);
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * sqrt (2));
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "includeties", true);
 assert (iscell (idx), true);
 assert (iscell (D), true)
 assert (idx {1}, [1]);
 assert (idx {2}', [1, 2]);
 assert (D{1}, ones (1, 1) * sqrt (2));
 assert (D{2}', ones (1, 2) * sqrt (2));
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "euclidean", "k", 2);
 assert (idx, [1, 2; 1, 2]);
 assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "seuclidean");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * sqrt (2));
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "seuclidean", "k", 2);
 assert (idx, [1, 2; 1, 2]);
 assert (D, [sqrt(2), 3.162277660168380; sqrt(2), sqrt(2)], 1e-14);
***** test
 xx = [1, 2; 1, 3; 2, 4; 3, 6];
 yy = [2, 4; 2, 6];
 [idx, D] = knnsearch (xx, yy, "Distance", "mahalanobis");
 assert (idx, [3; 2]);
 assert (D, [0; 3.162277660168377], 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "minkowski");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * sqrt (2));
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "minkowski", "p", 3);
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * 1.259921049894873, 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "cityblock");
 assert (idx, [1; 1]);
 assert (D, [2; 2]);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "chebychev");
 assert (idx, [1; 1]);
 assert (D, [1; 1]);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "cosine");
 assert (idx, [2; 3]);
 assert (D, [0.005674536395645; 0.002911214328620], 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "correlation");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "spearman");
 assert (idx, [1; 1]);
 assert (D, ones (2, 1) * 0.051316701949486, 1e-14);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "hamming");
 assert (idx, [1; 1]);
 assert (D, [0.5; 0.5]);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "jaccard");
 assert (idx, [1; 1]);
 assert (D, [0.5; 0.5]);
***** test
 [idx, D] = knnsearch (X, Y, "Distance", "jaccard", "k", 2);
 assert (idx, [1, 2; 1, 2]);
 assert (D, [0.5, 1; 0.5, 0.5]);
***** test
 a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
 b = [1, 1];
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", true);
 assert (iscell (idx), true);
 assert (iscell (D), true)
 assert (cell2mat (idx)', [4, 2, 3, 6, 1, 5, 7, 9]);
 assert (cell2mat (D)', [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000], 1e-4);
***** test
 a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
 b = [1, 1];
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", true);
 assert (iscell (idx), true);
 assert (iscell (D), true)
 assert (cell2mat (idx), [4, 2, 3, 6, 1, 5, 7, 9]);
 assert (cell2mat (D), [0.7071, 1.0000, 1.4142, 2.5447, 4.0000, 4.0000, 4.0000, 4.0000], 1e-4);
***** test
 a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
 b = [1, 1];
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree", "includeties", false);
 assert (iscell (idx), false);
 assert (iscell (D), false)
 assert (idx, [4, 2, 3, 6, 1]);
 assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000], 1e-4);
***** test
 a = [1, 5; 1, 2; 2, 2; 1.5, 1.5; 5, 1; 2 -1.34; 1, -3; 4, -4; -3, 1; 8, 9];
 b = [1, 1];
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "exhaustive", "includeties", false);
 assert (iscell (idx), false);
 assert (iscell (D), false)
 assert (idx, [4, 2, 3, 6, 1]);
 assert (D, [0.7071, 1.0000, 1.4142, 2.5447, 4.0000], 1e-4);
***** test
 load fisheriris
 a = meas;
 b = min(meas);
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
 assert (idx, [42, 9, 14, 39, 13]);
 assert (D, [0.5099, 0.9950, 1.0050, 1.0536, 1.1874], 1e-4);
***** test
 load fisheriris
 a = meas;
 b = mean(meas);
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
 assert (idx, [65, 83, 89, 72, 100]);
 assert (D, [0.3451, 0.3869, 0.4354, 0.4481, 0.4625], 1e-4);
***** test
 load fisheriris
 a = meas;
 b = max(meas);
 [idx, D] = knnsearch (a, b, "K", 5, "NSMethod", "kdtree");
 assert (idx, [118, 132, 110, 106, 136]);
 assert (D, [0.7280, 0.9274, 1.3304, 1.5166, 1.6371], 1e-4);

***** test
 load fisheriris
 a = meas;
 b = max(meas);
 [idx, D] = knnsearch (a, b, "K", 5, "includeties", true);
 assert (iscell (idx), true);
 assert (iscell (D), true);
 assert (cell2mat (idx)', [118, 132, 110, 106, 136]);
 assert (cell2mat (D)', [0.7280, 0.9274, 1.3304, 1.5166, 1.6371], 1e-4);
***** error<knnsearch: too few input arguments.> knnsearch (1)
***** error<knnsearch: number of columns in X and Y must match.> ...
 knnsearch (ones (4, 5), ones (4))
***** error<knnsearch: invalid NAME in optional pairs of arguments.> ...
 knnsearch (ones (4, 2), ones (3, 2), "Distance", "euclidean", "some", "some")
***** error<knnsearch: only a single distance parameter can be defined.> ...
 knnsearch (ones (4, 5), ones (1, 5), "scale", ones (1, 5), "P", 3)
***** error<knnsearch: invalid value of K.> ...
 knnsearch (ones (4, 5), ones (1, 5), "K", 0)
***** error<knnsearch: invalid value of Minkowski Exponent.> ...
 knnsearch (ones (4, 5), ones (1, 5), "P", -2)
***** error<knnsearch: invalid value in Scale or the size of Scale.> ...
 knnsearch (ones (4, 5), ones (1, 5), "scale", ones(4,5), "distance", "euclidean")
***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
 knnsearch (ones (4, 5), ones (1, 5), "cov", ["some" "some"])
***** error<knnsearch: invalid value in Cov, Cov can only be given for mahalanobis distance.> ...
 knnsearch (ones (4, 5), ones (1, 5), "cov", ones(4,5), "distance", "euclidean")
***** error<knnsearch: invalid value of bucketsize.> ...
 knnsearch (ones (4, 5), ones (1, 5), "bucketsize", -1)
***** error<knnsearch: invalid value of bucketsize.> ...
 knnsearch (ones (4, 5), ones (1, 5), "bucketsize", 2.5)
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "cosine")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "mahalanobis")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "correlation")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "seuclidean")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "spearman")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "hamming")
***** error<knnsearch: 'kdtree' cannot be used with the given distance metric.> ...
 knnsearch (ones (4, 5), ones (1, 5), "NSmethod", "kdtree", "distance", "jaccard")
43 tests, 43 passed, 0 known failure, 0 skipped
[inst/grpstats.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/grpstats.m
***** demo
 load carsmall;
 [m, p, g] = grpstats (Weight, Model_Year, {'mean', 'predci', 'gname'})
 n = length (m);
 errorbar ((1:n)',m,p(:,2)-m);
 set (gca, "xtick", 1:n, "xticklabel", g);
 title ("95% prediction intervals for mean weight by year");
***** demo
 load carsmall;
 [m, p, g] = grpstats ([Acceleration,Weight/1000],Cylinders, ...
                       {'mean', 'meanci', 'gname'}, 0.05)
 [c, r] = size (m);
 errorbar ((1:c)'.*ones(c,r),m,p(:,[(1:r)])-m);
 set (gca, "xtick", 1:c, "xticklabel", g);
 title ("95% prediction intervals for mean weight by year");
***** demo
 ## Plot mean and 95% CI for a single grouping variable
 load carsmall;
 grpstats (Weight, Model_Year, 0.05);
 title ("Mean Weight by Model Year");
***** demo
 ## Plot mean and 95% CI for two grouping variables
 load carsmall;
 grpstats (Weight, {Origin, Cylinders}, 0.05);
 title ("Mean Weight by Origin and Number of Cylinders");
***** test
 load carsmall
 means = grpstats (Acceleration, Origin);
 assert (means, [14.4377; 18.0500; 15.8867; 16.3778; 16.6000; 15.5000], 0.001);
***** test
 load carsmall
 [grpMin, grpMax, grp] = grpstats (Acceleration, Origin, {'min', 'max', ...
                                                          'gname'});
 assert (grpMin, [8.0; 15.3; 13.9; 12.2; 15.7; 15.5]);
 assert (grpMax, [22.2; 21.9; 18.2; 24.6; 17.5; 15.5]);
***** test
 load carsmall
 [grpMin, grpMax, grp] = grpstats (Acceleration, Origin, {'min', 'max', ...
                                                          'gname'});
 assert (grp', {'USA', 'France', 'Japan', 'Germany', 'Sweden', 'Italy'});
***** test
 load carsmall
 [m, p, g] = grpstats ([Acceleration, Weight/1000], Cylinders, ...
                       {'mean', 'meanci', 'gname'}, 0.05);
 ## check meanci lower bounds (first slice) with tolerance
 expected_lower = [15.9163; 15.6622; 10.7968];
 expected_upper = [17.4249; 17.2907; 12.4845];
 assert (abs (p(:,1,1)), expected_lower, 1e-3);
 assert (abs (p(:,1,2)), expected_upper, 1e-3);
***** test
 [mC, g] = grpstats ([], []);
 assert (isempty (mC), true);
 assert (isempty (g), true);
***** test
 ## column vector, no group
 x = [1; 2; 3; 4; 5];
 m = grpstats (x);
 expected = 3;
 assert (m, expected);
***** test
 ## row vector, no group
 x = [1 2 3 4 5];
 m = grpstats (x);
 expected = 3;
 assert (m, expected);
***** test
 ## matrix, no group
 x = [1 2; 3 4; 5 6];
 m = grpstats (x);
 expected = [3 4];
 assert (m, expected);
***** test
 ## vector, numeric groups
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 m = grpstats (x, g);
 expected = [15; 35; 55];
 assert (m, expected);
***** test
 ## vector, cellstr groups
 x = [10; 20; 30; 40; 50; 60];
 g = {'A'; 'A'; 'B'; 'B'; 'C'; 'C'};
 m = grpstats (x, g);
 expected = [15; 35; 55];
 assert (m, expected);
***** test
 ## matrix, numeric groups
 x = [1 10; 2 20; 3 30; 4 40; 5 50; 6 60];
 g = [1; 1; 2; 2; 3; 3];
 m = grpstats (x, g);
 expected = [1.5 15; 3.5 35; 5.5 55];
 assert (m, expected);
***** test
 ## NaN handling
 x = [1; NaN; 3; 4; NaN; 6];
 g = [1; 1; 2; 2; 3; 3];
 m = grpstats (x, g);
 expected = [1; 3.5; 6];
 assert (m, expected);
***** test
 ## single group
 x = [1; 2; 3; 4; 5];
 g = ones (5, 1);
 m = grpstats (x, g);
 expected = 3;
 assert (m, expected);
***** test
 ## single statistic
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 m = grpstats (x, g, 'mean');
 expected = [15; 35; 55];
 assert (m, expected);
***** test
 ## single statistic
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 m = grpstats (x, g, 'median');
 expected = [15; 35; 55];
 assert (m, expected);
***** test
 ## single statistic
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 s = grpstats (x, g, 'std');
 expected = [7.07106781186548; 7.07106781186548; 7.07106781186548];
 assert (s, expected, 1e-14);
***** test
 ## single statistic
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 v = grpstats (x, g, 'var');
 expected = [50; 50; 50];
 assert (v, expected);
***** test
 ## single statistic
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 s = grpstats (x, g, 'sem');
 expected = [5; 5; 5];
 assert (s, expected);
***** test
 ## single statistic
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 mn = grpstats (x, g, 'min');
 expected = [10; 30; 50];
 assert (mn, expected);
***** test
 ## single statistic
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 mx = grpstats (x, g, 'max');
 expected = [20; 40; 60];
 assert (mx, expected);
***** test
 ## single statistic
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 r = grpstats (x, g, 'range');
 expected = [10; 10; 10];
 assert (r, expected);
***** test
 ## single statistic
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 n = grpstats (x, g, 'numel');
 expected = [2; 2; 2];
 assert (n, expected);
***** test
 ## single statistic
 x = [10; 20; 30; 40; 50; 60];
 g = {"A"; "A"; "B"; "B"; "C"; "C"};
 names = grpstats (x, g, 'gname');
 expected = {"A"; "B"; "C"};
 assert (names, expected);
***** test
 ## single statistic (default alpha)
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 ci = grpstats (x, g, 'meanci');
 expected = [-48.5310236808735 78.5310236808735; -28.5310236808735 ...
             98.5310236808735; -8.53102368087348 118.531023680873];
 assert (ci, expected, 1e-12);
***** test
 ## single statistic (default alpha)
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 ci = grpstats (x, g, 'predci');
 expected = [-95.0389608721344 125.038960872134; -75.0389608721344 ...
             145.038960872134; -55.0389608721344 165.038960872134];
 assert (ci, expected, 1e-12);
***** test
 ## mean + std
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 [m, s] = grpstats (x, g, {'mean', 'std'});
 expected_m = [15; 35; 55];
 expected_s = [7.07106781186548; 7.07106781186548; 7.07106781186548];
 assert (m, expected_m, 1e-14);
 assert (s, expected_s, 1e-14);
***** test
 ## min + max + range
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 [mn, mx, r] = grpstats (x, g, {'min', 'max', 'range'});
 expected_mn = [10; 30; 50];
 expected_mx = [20; 40; 60];
 expected_r  = [10; 10; 10];
 assert (mn, expected_mn);
 assert (mx, expected_mx);
 assert (r, expected_r);
***** test
 ## mean + median + numel + gname
 x = [10; 20; 30; 40; 50; 60];
 g = {'A'; 'A'; 'B'; 'B'; 'C'; 'C'};
 [m, med, n, names] = grpstats (x, g, {'mean', 'median', 'numel', 'gname'});
 expected_m   = [15; 35; 55];
 expected_med = [15; 35; 55];
 expected_n   = [2; 2; 2];
 expected_names = {'A'; 'B'; 'C'};
 assert (m, expected_m);
 assert (med, expected_med);
 assert (n, expected_n);
 assert (names, expected_names);
***** test
 ## all basic statistics
 x = [10; 20; 30; 40; 50; 60; 70; 80];
 g = [1; 1; 2; 2; 2; 2; 3; 3];
 [m, med, s, v, se, mn, mx, r, n] = grpstats (x, g, {'mean', 'median', ...
                                                     'std', 'var', 'sem', ...
                                                     'min', 'max', 'range', ...
                                                     'numel'});
 expected_m   = [15; 45; 75];
 expected_med = [15; 45; 75];
 expected_s   = [7.07106781186548; 12.9099444873581; 7.07106781186548];
 expected_v   = [50; 166.666666666667; 50];
 expected_se  = [5; 6.45497224367903; 5];
 expected_mn  = [10; 30; 70];
 expected_mx  = [20; 60; 80];
 expected_r   = [10; 30; 10];
 expected_n   = [2; 4; 2];
 assert (m,   expected_m);
 assert (med, expected_med);
 assert (s,   expected_s, 1e-13);
 assert (v,   expected_v, 1e-12);
 assert (se,  expected_se, 1e-14);
 assert (mn,  expected_mn);
 assert (mx,  expected_mx);
 assert (r,   expected_r);
 assert (n,   expected_n);
***** test
 ## meanci-alpha-0.1
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 ci = grpstats (x, g, 'meanci', 0.1);
 expected = [-16.5687575733752 46.5687575733752; 3.4312424266248 ...
             66.5687575733752; 23.4312424266248 86.5687575733752];
 assert (ci, expected, 1e-13);
***** test
 ## predci-alpha-0.1
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 ci = grpstats (x, g, 'predci', 0.1);
 expected = [-39.6786920489106 69.6786920489106; -19.6786920489106 ...
             89.6786920489106; 0.321307951089366 109.678692048911];
 assert (ci, expected, 1e-12);
***** test
 ## meanci-alpha-0.01
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 ci = grpstats (x, g, 'meanci', 0.01);
 expected = [-303.283705814358 333.283705814358; -283.283705814358 ...
             353.283705814358; -263.283705814358 373.283705814358];
 assert (ci, expected, 3e-8);
***** test
 ## predci-alpha-0.01
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 ci = grpstats (x, g, 'predci', 0.01);
 expected = [-536.283549691775 566.283549691775; -516.283549691775 ...
             586.283549691775; -496.283549691775 606.283549691775];
 assert (ci, expected, 3e-8);
***** test
 ## meanci-alpha-0.2
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 ci = grpstats (x, g, 'meanci', 0.2);
 expected = [-0.388417685876263 30.3884176858763; 19.6115823141237 ...
             50.3884176858763; 39.6115823141237 70.3884176858763];
 assert (ci, expected, 1e-13);
***** test
 ## predci-alpha-0.2
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 ci = grpstats (x, g, 'predci', 0.2);
 expected = [-11.6535212800292 41.6535212800292; 8.34647871997083 ...
             61.6535212800292; 28.3464787199708 81.6535212800292];
 assert (ci, expected, 1e-13);
***** test
 ## meanci, name-value alpha=0.2
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 ci = grpstats (x, g, 'meanci', 'alpha', 0.2);
 expected = [-0.388417685876263 30.3884176858763; 19.6115823141237 ...
             50.3884176858763; 39.6115823141237 70.3884176858763];
 assert (ci, expected, 1e-13);
***** test
 ## meanci + predci, alpha=0.01
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 2; 2; 3; 3];
 [ci_m, ci_p] = grpstats (x, g, {'meanci', 'predci'}, 0.01);
 expected_m = [-303.283705814358 333.283705814358; -283.283705814358 ...
               353.283705814358; -263.283705814358 373.283705814358];
 expected_p = [-536.283549691775 566.283549691775; -516.283549691775 ...
               586.283549691775; -496.283549691775 606.283549691775];
 assert (ci_m, expected_m, 3e-8);
 assert (ci_p, expected_p, 3e-8);
***** test
 ## matrix, mean+std+numel
 x = [1 10; 2 20; 3 30; 4 40; 5 50; 6 60];
 g = [1; 1; 2; 2; 3; 3];
 [m, s, n] = grpstats (x, g, {'mean', 'std', 'numel'});
 expected_m = [1.5 15; 3.5 35; 5.5 55];
 expected_s = [0.707106781186548 7.07106781186548; 0.707106781186548 ...
               7.07106781186548; 0.707106781186548 7.07106781186548];
 expected_n = [2 2; 2 2; 2 2];
 assert (m, expected_m);
 assert (s, expected_s, 1e-14);
 assert (n, expected_n);
***** test
 ## matrix with NaN, mean+numel
 x = [1 10; NaN 20; 3 NaN; 4 40; 5 50; 6 60];
 g = [1; 1; 2; 2; 3; 3];
 [m, n] = grpstats (x, g, {'mean', 'numel'});
 expected_m = [1   15; 3.5 40; 5.5 55];
 expected_n = [1 2; 2 1; 2 2];
 assert (m, expected_m);
 assert (n, expected_n);
***** test
 ## 3-column matrix, mean+min+max
 x = [1 100 1000; 2 200 2000; 3 300 3000; 4 400 4000];
 g = [1; 1; 2; 2];
 [m, mn, mx] = grpstats (x, g, {'mean', 'min', 'max'});
 expected_m  = [1.5 150 1500; 3.5 350 3500];
 expected_mn = [1 100 1000; 3 300 3000];
 expected_mx = [2 200 2000; 4 400 4000];
 assert (m,  expected_m);
 assert (mn, expected_mn);
 assert (mx, expected_mx);
***** test
 ## one element per group
 x = [1; 2; 3];
 g = [1; 2; 3];
 [m, s, n] = grpstats (x, g, {'mean', 'std', 'numel'});
 expected_m = [1; 2; 3];
 expected_s = [0; 0; 0];
 expected_n = [1; 1; 1];
 assert (m, expected_m);
 assert (s, expected_s);
 assert (n, expected_n);
***** test
 ## group with all NaN
 x = [1; 2; NaN; NaN; 5; 6];
 g = [1; 1; 2; 2; 3; 3];
 [m, s, n] = grpstats (x, g, {'mean', 'std', 'numel'});
 expected_m = [1.5; NaN; 5.5];
 expected_s = [0.707106781186548; NaN; 0.707106781186548];
 expected_n = [2; 0; 2];
 assert (m, expected_m);
 assert (s, expected_s, 1e-14);
 assert (n, expected_n);
***** test
 ## unequal group sizes
 x = [1; 2; 3; 4; 5; 6; 7; 8; 9; 10];
 g = [1; 1; 1; 1; 2; 2; 2; 3; 3; 3];
 [m, v, n] = grpstats (x, g, {'mean', 'var', "numel"});
 expected_m = [2.5; 6; 9];
 expected_v = [1.66666666666667; 1; 1];
 expected_n = [4; 3; 3];
 assert (m, expected_m);
 assert (v, expected_v, 1e-14);
 assert (n, expected_n);
***** test
 ## non-consecutive numeric groups
 x = [10; 20; 30; 40; 50; 60];
 g = [1; 1; 5; 5; 10; 10];
 [m, names] = grpstats (x, g, {'mean', 'gname'});
 expected_m = [15; 35; 55];
 expected_names = {'1'; '5'; '10'};
 assert (m, expected_m);
 assert (names, expected_names);
***** test
 ## unsorted string groups
 x = [30; 10; 40; 20; 60; 50];
 g = {'C'; 'A'; 'C'; 'A'; 'B'; 'B'};
 [m, names] = grpstats (x, g, {"mean", "gname"});
 expected_m = [35; 15; 55];
 expected_names = {'C'; 'A'; 'B'};
 assert (m, expected_m);
 assert (names, expected_names);
***** test
 ## 20 groups, one element each
 x = (1:20)';
 g = (1:20)';
 [m, n] = grpstats (x, g, {'mean', 'numel'});
 expected_m = (1:20)';
 expected_n = ones (20, 1);
 assert (m, expected_m);
 assert (n, expected_n);
***** test
 ## large sample meanci
 x = (1:50)';
 g = [ones(25, 1); 2 * ones(25, 1)];
 ci = grpstats (x, g, 'meanci');
 expected = [9.96202357522388 16.0379764247761; 34.9620235752239 ...
             41.0379764247761];
 assert (ci, expected, 1e-13);
***** test
 ## large sample predci
 x = (1:50)';
 g = [ones(25, 1); 2 * ones(25, 1)];
 ci = grpstats (x, g, 'predci');
 expected = [-2.49070107176829 28.4907010717683; 22.5092989282317 ...
             53.4907010717683];
 assert (ci, expected, 1e-13);
***** test
 Y = [5; 6; 7; 4; 9; 8];
 X = [1; 2; 3; 4; 5; 6];
 Group = categorical ({'A'; 'A'; 'B'; 'B'; 'C'; 'C'});
 tbl = table (Y, X, Group);
 stats_tbl = grpstats (tbl, 'Group', {'mean', 'numel'});
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y', 'numel_Y', ...
                                              'mean_X', 'numel_X'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'; 'C'});
 assert (stats_tbl.GroupCount, [2; 2; 2]);
 assert (stats_tbl.mean_Y, [5.5; 5.5; 8.5]);
 assert (stats_tbl.numel_Y, [2; 2; 2]);
 assert (stats_tbl.mean_X, [1.5; 3.5; 5.5]);
 assert (stats_tbl.numel_X, [2; 2; 2]);
***** test
 Y = [5; 6; 7; 4; 9; 8];
 Group = categorical ({'A'; 'A'; 'B'; 'B'; 'C'; 'C'});
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'; 'C'});
 assert (stats_tbl.GroupCount, [2; 2; 2]);
 assert (stats_tbl.mean_Y, [5.5; 5.5; 8.5]);
***** test
 Y = [10; 20; 30; 40];
 X = [100; 200; 300; 400];
 Z = [1000; 2000; 3000; 4000];
 Group = categorical ({'A'; 'A'; 'B'; 'B'});
 tbl = table (Y, X, Z, Group);
 stats_tbl = grpstats (tbl, 'Group', {'mean', 'numel'});
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
         'mean_Y', 'numel_Y', 'mean_X', 'numel_X', 'mean_Z', 'numel_Z'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'});
 assert (stats_tbl.GroupCount, [2; 2]);
 assert (stats_tbl.mean_Y, [15; 35]);
 assert (stats_tbl.numel_Y, [2; 2]);
 assert (stats_tbl.mean_X, [150; 350]);
 assert (stats_tbl.numel_X, [2; 2]);
 assert (stats_tbl.mean_Z, [1500; 3500]);
 assert (stats_tbl.numel_Z, [2; 2]);
***** test
 Y = [1; 2; 3; 4; 5; 6; 7; 8];
 Group = categorical ({'A'; 'A'; 'A'; 'A'; 'B'; 'B'; 'B'; 'B'});
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'});
 assert (stats_tbl.GroupCount, [4; 4]);
 assert (stats_tbl.mean_Y, [2.5; 6.5]);
***** test
 Y = [1; 2; 3; 4; 5; 6; 7];
 Group = categorical ({'A'; 'A'; 'A'; 'A'; 'A'; 'B'; 'B'});
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', {'mean', 'numel'});
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y', 'numel_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'});
 assert (stats_tbl.GroupCount, [5; 2]);
 assert (stats_tbl.mean_Y, [3; 6.5]);
 assert (stats_tbl.numel_Y, [5; 2]);
***** test
 Y = [10; 20; 30];
 Group = categorical ({'A'; 'B'; 'C'});
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'; 'C'});
 assert (stats_tbl.GroupCount, [1; 1; 1]);
 assert (stats_tbl.mean_Y, [10; 20; 30]);
***** test
 Y = [5; 5; 5; 5];
 Group = categorical ({'A'; 'A'; 'B'; 'B'});
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'});
 assert (stats_tbl.GroupCount, [2; 2]);
 assert (stats_tbl.mean_Y, [5; 5]);
***** test
 Y = [1; NaN; 3; 4; NaN; 6];
 X = [10; 20; NaN; 40; 50; NaN];
 Group = categorical ({'A'; 'A'; 'B'; 'B'; 'C'; 'C'});
 tbl = table (Y, X, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y', 'mean_X'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'; 'C'});
 assert (stats_tbl.GroupCount, [2; 2; 2]);
 assert (stats_tbl.mean_Y, [1; 3.5; 6]);
 assert (stats_tbl.mean_X, [15; 40; 50]);
***** test
 Y = [1; NaN; 3; 4; 5; 6];
 Group = categorical ({'A'; 'A'; 'B'; 'B'; 'C'; 'C'});
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', {'mean', 'numel'});
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y', 'numel_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'; 'C'});
 assert (stats_tbl.GroupCount, [2; 2; 2]);
 assert (stats_tbl.mean_Y, [1; 3.5; 5.5]);
 assert (stats_tbl.numel_Y, [1; 2; 2]);
***** test
 Y = [100; 200; 300; 400; 500; 600];
 Group = categorical ({'Group1'; 'Group1'; 'Group2'; 'Group2'; ...
                       'Group3'; 'Group3'});
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y'});
 assert (stats_tbl.Properties.RowNames, {'Group1'; 'Group2'; 'Group3'});
 assert (stats_tbl.GroupCount, [2; 2; 2]);
 assert (stats_tbl.mean_Y, [150; 350; 550]);
***** test
 Var1 = [1; 2; 3; 4];
 Var2 = [10; 20; 30; 40];
 Var3 = [100; 200; 300; 400];
 Var4 = [1000; 2000; 3000; 4000];
 Group = categorical ({'A'; 'A'; 'B'; 'B'});
 tbl = table (Var1, Var2, Var3, Var4, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
         'mean_Var1', 'mean_Var2', 'mean_Var3', 'mean_Var4'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'});
 assert (stats_tbl.GroupCount, [2; 2]);
 assert (stats_tbl.mean_Var1, [1.5; 3.5]);
 assert (stats_tbl.mean_Var2, [15; 35]);
 assert (stats_tbl.mean_Var3, [150; 350]);
 assert (stats_tbl.mean_Var4, [1500; 3500]);
***** test
 Y = [1.5; 2.5; 3.5; 4.5; 5.5; 6.5];
 Group = categorical ({'A'; 'A'; 'B'; 'B'; 'C'; 'C'});
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'; 'C'});
 assert (stats_tbl.GroupCount, [2; 2; 2]);
 assert (stats_tbl.mean_Y, [2; 4; 6]);
***** test
 Y = [-10; -20; 30; 40; 50; 60];
 Group = categorical ({'A'; 'A'; 'B'; 'B'; 'C'; 'C'});
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'; 'C'});
 assert (stats_tbl.GroupCount, [2; 2; 2]);
 assert (stats_tbl.mean_Y, [-15; 35; 55]);
***** test
 Y = [0; 0; 0; 0; 0; 0];
 Group = categorical ({'A'; 'A'; 'B'; 'B'; 'C'; 'C'});
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'; 'C'});
 assert (stats_tbl.GroupCount, [2; 2; 2]);
 assert (stats_tbl.mean_Y, [0; 0; 0]);
***** test
 Y = [1e6; 2e6; 3e6; 4e6; 5e6; 6e6];
 Group = categorical ({'A'; 'A'; 'B'; 'B'; 'C'; 'C'});
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'; 'C'});
 assert (stats_tbl.GroupCount, [2; 2; 2]);
 assert (stats_tbl.mean_Y, [1.5e6; 3.5e6; 5.5e6]);
***** test
 Y = (1:10)';
 Group = categorical (repmat ({'A'; 'B'}, 5, 1));
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', {'mean', 'numel'});
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, ...
   {'Group', 'GroupCount', 'mean_Y', 'numel_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'});
 assert (stats_tbl.GroupCount, [5; 5]);
 assert (stats_tbl.mean_Y, [5; 6]);
 assert (stats_tbl.numel_Y, [5; 5]);
***** test
 Y = (1:20)';
 Group = categorical (repmat ({'A'; 'B'; 'C'; 'D'}, 5, 1));
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'; 'C'; 'D'});
 assert (stats_tbl.GroupCount, [5; 5; 5; 5]);
 assert (stats_tbl.mean_Y, [9; 10; 11; 12]);
***** test
 Y = [1; 2; 3; 4; 5];
 Group = categorical ({'A'; 'B'; 'C'; 'D'; 'E'});
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'; 'C'; 'D'; 'E'});
 assert (stats_tbl.GroupCount, [1; 1; 1; 1; 1]);
 assert (stats_tbl.mean_Y, [1; 2; 3; 4; 5]);
***** test
 Score1 = [85; 90; 78; 92; 88; 76];
 Score2 = [82; 88; 75; 90; 85; 73];
 Group = categorical ({'High'; 'High'; 'Med'; 'Med'; 'Low'; 'Low'});
 tbl = table (Score1, Score2, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Score1', 'mean_Score2'});
 assert (stats_tbl.Properties.RowNames, {'High'; 'Low'; 'Med'});
 assert (stats_tbl.GroupCount, [2; 2; 2]);
 assert (stats_tbl.mean_Score1, [87.5; 82; 85]);
 assert (stats_tbl.mean_Score2, [85; 79; 82.5]);
***** test
 Height = [170; 175; 165; 180; 160; 185];
 Weight = [70; 75; 65; 80; 60; 85];
 Category = categorical ({'M'; 'M'; 'F'; 'F'; 'M'; 'M'});
 tbl = table (Height, Weight, Category);
 stats_tbl = grpstats (tbl, 'Category', {'mean', 'numel'});
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Category', 'GroupCount', ...
         'mean_Height', 'numel_Height', 'mean_Weight', 'numel_Weight'});
 assert (stats_tbl.Properties.RowNames, {'F'; 'M'});
 assert (stats_tbl.GroupCount, [2; 4]);
 assert (stats_tbl.mean_Height, [172.5; 172.5]);
 assert (stats_tbl.numel_Height, [2; 4]);
 assert (stats_tbl.mean_Weight, [72.5; 72.5]);
 assert (stats_tbl.numel_Weight, [2; 4]);
***** test
 Value = [10.5; 11.2; 9.8; 10.1; 11.5; 10.8];
 Type = categorical ({'A'; 'A'; 'A'; 'B'; 'B'; 'B'});
 tbl = table (Value, Type);
 stats_tbl = grpstats (tbl, 'Type', 'numel');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Type', 'GroupCount', ...
                                              'numel_Value'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'});
 assert (stats_tbl.GroupCount, [3; 3]);
 assert (stats_tbl.numel_Value, [3; 3]);
***** test
 Data = [1; 2; 3; 4; 5; 6; 7; 8; 9; 10];
 Label = categorical ({'A'; 'A'; 'A'; 'A'; 'A'; 'B'; 'B'; 'B'; 'B'; 'B'});
 tbl = table (Data, Label);
 stats_tbl = grpstats (tbl, 'Label', {'mean', 'numel'});
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Label', 'GroupCount', ...
                                              'mean_Data', 'numel_Data'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'});
 assert (stats_tbl.GroupCount, [5; 5]);
 assert (stats_tbl.mean_Data, [3; 8]);
 assert (stats_tbl.numel_Data, [5; 5]);
***** test
 X1 = [1; 2; 3; 4];
 X2 = [5; 6; 7; 8];
 G = categorical ({'A'; 'A'; 'B'; 'B'});
 tbl = table (X1, X2, G);
 stats_tbl = grpstats (tbl, 'G', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'G', 'GroupCount', ...
                                              'mean_X1', 'mean_X2'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'});
 assert (stats_tbl.GroupCount, [2; 2]);
 assert (stats_tbl.mean_X1, [1.5; 3.5]);
 assert (stats_tbl.mean_X2, [5.5; 7.5]);
***** test
 Measurement = [100; 150; 200; 250; 300; 350];
 GroupVar = categorical ({'Control'; 'Control'; 'Treatment'; 'Treatment'; ...
                          'Placebo'; 'Placebo'});
 tbl = table (Measurement, GroupVar);
 stats_tbl = grpstats (tbl, 'GroupVar', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'GroupVar', 'GroupCount', ...
                                              'mean_Measurement'});
 assert (stats_tbl.Properties.RowNames, {'Control'; 'Placebo'; 'Treatment'});
 assert (stats_tbl.GroupCount, [2; 2; 2]);
 assert (stats_tbl.mean_Measurement, [125; 325; 225]);
***** test
 Y = [NaN; NaN; 3; 4; 5; 6];
 Group = categorical ({'A'; 'A'; 'B'; 'B'; 'C'; 'C'});
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', 'mean');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'; 'C'});
 assert (stats_tbl.GroupCount, [2; 2; 2]);
 assert (isequaln (stats_tbl.mean_Y, [NaN; 3.5; 5.5]));
***** test
 Y = [1; 2; NaN; NaN; NaN; NaN];
 Group = categorical ({'A'; 'A'; 'B'; 'B'; 'C'; 'C'});
 tbl = table (Y, Group);
 stats_tbl = grpstats (tbl, 'Group', {'mean', 'numel'});
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Group', 'GroupCount', ...
                                              'mean_Y', 'numel_Y'});
 assert (stats_tbl.Properties.RowNames, {'A'; 'B'; 'C'});
 assert (stats_tbl.GroupCount, [2; 2; 2]);
 assert (isequaln (stats_tbl.mean_Y, [1.5; NaN; NaN]));
 assert (stats_tbl.numel_Y, [2; 0; 0]);
***** test
 Val = [5.5; 6.5; 7.5; 8.5];
 Cat = categorical ({'X'; 'X'; 'Y'; 'Y'});
 tbl = table (Val, Cat);
 stats_tbl = grpstats (tbl, 'Cat', 'numel');
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Cat', 'GroupCount', ...
                                              'numel_Val'});
 assert (stats_tbl.Properties.RowNames, {'X'; 'Y'});
 assert (stats_tbl.GroupCount, [2; 2]);
 assert (stats_tbl.numel_Val, [2; 2]);
***** test
 A = [1; 2; 3; 4; 5; 6];
 B = [10; 20; 30; 40; 50; 60];
 C = [100; 200; 300; 400; 500; 600];
 Grp = categorical ({'G1'; 'G1'; 'G2'; 'G2'; 'G3'; 'G3'});
 tbl = table (A, B, C, Grp);
 stats_tbl = grpstats (tbl, 'Grp', {'mean', 'numel'});
 assert (istable (stats_tbl));
 assert (stats_tbl.Properties.VariableNames, {'Grp', 'GroupCount', ...
         'mean_A', 'numel_A', 'mean_B', 'numel_B', 'mean_C', 'numel_C'});
 assert (stats_tbl.Properties.RowNames, {'G1'; 'G2'; 'G3'});
 assert (stats_tbl.GroupCount, [2; 2; 2]);
 assert (stats_tbl.mean_A, [1.5; 3.5; 5.5]);
 assert (stats_tbl.numel_A, [2; 2; 2]);
 assert (stats_tbl.mean_B, [15; 35; 55]);
 assert (stats_tbl.numel_B, [2; 2; 2]);
 assert (stats_tbl.mean_C, [150; 350; 550]);
 assert (stats_tbl.numel_C, [2; 2; 2]);
***** test
 x = [1; NaN; 3; 4];
 g = [1; 1; 2; 2];
 muci = grpstats (x, g, 'meanci');
 assert (muci, [NaN, NaN; -2.8531, 9.8531], 1e-4);
***** test
 x = [1; NaN; 3; 4; 5; 6];
 g = [1; 1; 1; 2; 2; 2];
 predci = grpstats (x, g, 'predci');
 assert (predci, [-20.0078, 24.0078; 0.0317, 9.9683], 1e-4);
***** error <grpstats: X must be a matrix or a table.> grpstats (ones (2, 2, 2))
***** error <grpstats: only one output argument in allowed when X is a table.> ...
       [a, b] = grpstats (table (1))
***** error <grpstats: invalid WHICHSTATS specification in cell array.> ...
       grpstats (ones (6, 2), [1; 1; 1; 2; 2; 2], {'mean', 1.5})
***** error <grpstats: invalid WHICHSTATS data type.> ...
       grpstats (ones (6, 2), [1; 1; 1; 2; 2; 2], 1.5)
***** error <grpstats: unrecognized function names in WHICHSTATS.> ...
       grpstats (ones (6, 2), [1; 1; 1; 2; 2; 2], 'some_function')
***** error <grpstats: unrecognized input arguments.> ...
       grpstats (ones (6, 2), [1; 1; 1; 2; 2; 2], 'mean', 35)
***** error <grpstats: unrecognized input arguments.> ...
       grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "predci", "somename", -0.1);
***** error <grpstats: invalid data types for 'VarNames'.> ...
       grpstats (ones (6, 2), [1; 1; 1; 2; 2; 2], 'mean', 'VarNames', 3)
***** error <grpstats: X must be numeric to plot mean and CI for each group.> ...
       grpstats ({ones(6, 2)}, [], 0.05)
***** error <grpstats: 'alpha' must be a real scalar in the range \(0,1\).> ...
       grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, "predci", "alpha", -0.1);
***** error <grpstats: cannot resolve GROUPVARS in input table.> ...
       grpstats (table ([1:5]'), {'Var_5'})
***** error <grpstats: invalid 'DataVars' reference to table X.> ...
       grpstats (table ([1:5]'), {'Var1'}, [], 'DataVars', 'Var5')
***** error <grpstats: 'VarNames' do not match expected variables.> ...
       grpstats (table ([1:5]', [1:5]'), {'Var1'}, [], 'VarNames', {'A', 'B'})
***** error <grpstats: samples in X and GROUPS mismatch.> ...
       grpstats ([1:5]', {'A'; 'B'; 'A'; 'B'})
***** error <grpstats: inconsistent number of output arguments.> ...
       m = grpstats ([1:4]', {'A'; 'B'; 'A'; 'B'}, {'mean', 'std'})
93 tests, 93 passed, 0 known failure, 0 skipped
[inst/fitcdiscr.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/fitcdiscr.m
***** demo
 ## Train a linear discriminant classifier for Gamma = 0.5
 ## and plot the decision boundaries.

 load fisheriris
 idx = ! strcmp (species, "setosa");
 X = meas(idx,3:4);
 Y = cast (strcmpi (species(idx), "virginica"), "double");
 obj = fitcdiscr (X, Y, "Gamma", 0.5)
 x1 = [min(X(:,1)):0.03:max(X(:,1))];
 x2 = [min(X(:,2)):0.02:max(X(:,2))];
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];
 pred = predict (obj, XGrid);
 gidx = logical (pred);

 figure
 scatter (XGrid(gidx,1), XGrid(gidx,2), "markerfacecolor", "magenta");
 hold on
 scatter (XGrid(!gidx,1), XGrid(!gidx,2), "markerfacecolor", "red");
 plot (X(Y == 0, 1), X(Y == 0, 2), "ko", X(Y == 1, 1), X(Y == 1, 2), "kx");
 xlabel ("Petal length (cm)");
 ylabel ("Petal width (cm)");
 title ("Linear Discriminant Analysis Decision Boundary");
 legend ({"Versicolor Region", "Virginica Region", ...
         "Sampled Versicolor", "Sampled Virginica"}, ...
         "location", "northwest")
 axis tight
 hold off
***** test
 load fisheriris
 Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
 [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.9999, 0.0001], 1e-4)
 assert (cost, [1, 0.0001, 0.9999], 1e-4)
 [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.6368, 0.3632], 1e-4)
 assert (cost, [1, 0.3632, 0.6368], 1e-4)
 assert (class (Mdl), "ClassificationDiscriminant");
 assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {meas, species, 150})
 assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
 assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
 assert (Mdl.ClassNames, unique (species))
 sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
          0.046361, 0.115388, 0.027622, 0.016355; ...
          0.083757, 0.027622, 0.185188, 0.021333; ...
          0.019201, 0.016355, 0.021333, 0.041882];
 assert (Mdl.Sigma, sigma, 1e-6)
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 assert (Mdl.Mu, mu, 1e-14)
 assert (Mdl.LogDetSigma, -8.6884, 1e-4)
***** error<fitcdiscr: too few arguments.> fitcdiscr ()
***** error<fitcdiscr: too few arguments.> fitcdiscr (ones (4,1))
***** error<fitcdiscr: name-value arguments must be in pairs.>
 fitcdiscr (ones (4,2), ones (4, 1), "K")
***** error<fitcdiscr: number of rows in X and Y must be equal.>
 fitcdiscr (ones (4,2), ones (3, 1))
***** error<fitcdiscr: number of rows in X and Y must be equal.>
 fitcdiscr (ones (4,2), ones (3, 1), "K", 2)
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/anovan.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/anovan.m
***** demo

 # Two-sample unpaired test on independent samples (equivalent to Student's
 # t-test). Note that the absolute value of t-statistic can be obtained by
 # taking the square root of the reported F statistic. In this example,
 # t = sqrt (1.44) = 1.20.

 score = [54 23 45 54 45 43 34 65 77 46 65]';
 gender = {"male" "male" "male" "male" "male" "female" "female" "female" ...
           "female" "female" "female"}';

 [P, ATAB, STATS] = anovan (score, gender, "display", "on", "varnames", "gender");
***** demo

 # Two-sample paired test on dependent or matched samples equivalent to a
 # paired t-test. As for the first example, the t-statistic can be obtained by
 # taking the square root of the reported F statistic. Note that the interaction
 # between treatment x subject was dropped from the full model by assigning
 # subject as a random factor (').

 score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
 treatment = {"before" "after"; "before" "after"; "before" "after";
              "before" "after"; "before" "after"}';
 subject = {"GS" "GS"; "JM" "JM"; "HM" "HM"; "JW" "JW"; "PS" "PS"}';

 [P, ATAB, STATS] = anovan (score(:), {treatment(:), subject(:)}, ...
                            "model", "full", "random", 2, "sstype", 2, ...
                            "varnames", {"treatment", "subject"}, ...
                            "display", "on");
***** demo

 # One-way ANOVA on the data from a study on the strength of structural beams,
 # in Hogg and Ledolter (1987) Engineering Statistics. New York: MacMillan

 strength = [82 86 79 83 84 85 86 87 74 82 ...
            78 75 76 77 79 79 77 78 82 79]';
 alloy = {"st","st","st","st","st","st","st","st", ...
          "al1","al1","al1","al1","al1","al1", ...
          "al2","al2","al2","al2","al2","al2"}';

 [P, ATAB, STATS] = anovan (strength, alloy, "display", "on", ...
                            "varnames", "alloy");
***** demo

 # One-way repeated measures ANOVA on the data from a study on the number of
 # words recalled by 10 subjects for three time conditions, in Loftus & Masson
 # (1994) Psychon Bull Rev. 1(4):476-490, Table 2. Note that the interaction
 # between seconds x subject was dropped from the full model by assigning
 # subject as a random factor (').

 words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
          15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
 seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
            1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];
 subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
             6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];

 [P, ATAB, STATS] = anovan (words(:), {seconds(:), subject(:)}, ...
                            "model", "full", "random", 2, "sstype", 2, ...
                            "display", "on", "varnames", {"seconds", "subject"});
***** demo

 # Balanced two-way ANOVA with interaction on the data from a study of popcorn
 # brands and popper types, in Hogg and Ledolter (1987) Engineering Statistics.
 # New York: MacMillan

 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 brands = {"Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"; ...
           "Gourmet", "National", "Generic"};
 popper = {"oil", "oil", "oil"; "oil", "oil", "oil"; "oil", "oil", "oil"; ...
           "air", "air", "air"; "air", "air", "air"; "air", "air", "air"};

 [P, ATAB, STATS] = anovan (popcorn(:), {brands(:), popper(:)}, ...
                            "display", "on", "model", "full", ...
                            "varnames", {"brands", "popper"});
***** demo

 # Unbalanced two-way ANOVA (2x2) on the data from a study on the effects of
 # gender and having a college degree on salaries of company employees,
 # in Maxwell, Delaney and Kelly (2018): Chapter 7, Table 15

 salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
           25 29 27 19 18 21 20 21 22 19]';
 gender = {"f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f" "f"...
           "m" "m" "m" "m" "m" "m" "m" "m" "m" "m"}';
 degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';

 [P, ATAB, STATS] = anovan (salary, {gender, degree}, "model", "full", ...
                            "sstype", 3, "display", "on", "varnames", ...
                            {"gender", "degree"});
***** demo

 # Unbalanced two-way ANOVA (3x2) on the data from a study of the effect of
 # adding sugar and/or milk on the tendency of coffee to make people babble,
 # in from Navarro (2019): 16.10

 sugar = {"real" "fake" "fake" "real" "real" "real" "none" "none" "none" ...
          "fake" "fake" "fake" "real" "real" "real" "none" "none" "fake"}';
 milk = {"yes" "no" "no" "yes" "yes" "no" "yes" "yes" "yes" ...
         "no" "no" "yes" "no" "no" "no" "no" "no" "yes"}';
 babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
           5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';

 [P, ATAB, STATS] = anovan (babble, {sugar, milk}, "model", "full",  ...
                            "sstype", 3, "display", "on", ...
                            "varnames", {"sugar", "milk"});
***** demo

 # Unbalanced three-way ANOVA (3x2x2) on the data from a study of the effects
 # of three different drugs, biofeedback and diet on patient blood pressure,
 # adapted* from Maxwell, Delaney and Kelly (2018): Chapter 8, Table 12
 # * Missing values introduced to make the sample sizes unequal to test the
 #   calculation of different types of sums-of-squares

 drug = {"X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" ...
         "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X" "X";
         "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" ...
         "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y" "Y";
         "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" ...
         "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z" "Z"};
 feedback = [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
 diet = [0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
 BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
       173 194 197 190 176 198 164 190 169 164 176 175;
       186 194 201 215 219 209 164 166 159 182 187 174 ...
       189 194 217 206 199 195 171 173 196 199 180 NaN;
       180 187 199 170 204 194 162 184 183 156 180 173 ...
       202 228 190 206 224 204 205 199 170 160 NaN NaN];

 [P, ATAB, STATS] = anovan (BP(:), {drug(:), feedback(:), diet(:)}, ...
                                    "model", "full", "sstype", 3, ...
                                    "display", "on", ...
                                    "varnames", {"drug", "feedback", "diet"});
***** demo

 # Balanced three-way ANOVA (2x2x2) with one of the factors being a blocking
 # factor. The data is from a randomized block design study on the effects
 # of antioxidant treatment on glutathione-S-transferase (GST) levels in
 # different mouse strains, from Festing (2014), ILAR Journal, 55(3):427-476.
 # Note that all interactions involving block were dropped from the full model
 # by assigning block as a random factor (').

 measurement = [444 614 423 625 408  856 447 719 ...
                764 831 586 782 609 1002 606 766]';
 strain= {"NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola", ...
          "NIH","NIH","BALB/C","BALB/C","A/J","A/J","129/Ola","129/Ola"}';
 treatment={"C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T" "C" "T"}';
 block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';

 [P, ATAB, STATS] = anovan (measurement/10, {strain, treatment, block}, ...
                            "sstype", 2, "model", "full", "random", 3, ...
                            "display", "on", ...
                            "varnames", {"strain", "treatment", "block"});
***** demo

 # One-way ANCOVA on data from a study of the additive effects of species
 # and temperature on chirpy pulses of crickets, from Stitch, The Worst Stats
 # Text eveR

 pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
          98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
          60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
 temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
         29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
         21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
 species = {"ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" "ex" ...
            "ex" "ex" "ex" "niv" "niv" "niv" "niv" "niv" "niv" "niv" ...
            "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv" "niv"};

 [P, ATAB, STATS] = anovan (pulse, {species, temp}, "model", "linear", ...
                           "continuous", 2, "sstype", "h", "display", "on", ...
                           "varnames", {"species", "temp"});
***** demo

 # Factorial ANCOVA on data from a study of the effects of treatment and
 # exercise on stress reduction score after adjusting for age. Data from R
 # datarium package).

 score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
          97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
          81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
          84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
          100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
          91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
 treatment = {"yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" "yes" ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  ...
              "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"  "no"}';
 exercise = {"lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
             "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
             "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  ...
             "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  "lo"  ...
             "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" "mid" ...
             "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"  "hi"}';
 age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
        58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
        75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';

 [P, ATAB, STATS] = anovan (score, {treatment, exercise, age}, ...
                            "model", [1 0 0; 0 1 0; 0 0 1; 1 1 0], ...
                            "continuous", 3, "sstype", "h", "display", "on", ...
                            "varnames", {"treatment", "exercise", "age"});
***** demo

 # Unbalanced one-way ANOVA with custom, orthogonal contrasts. The statistics
 # relating to the contrasts are shown in the table of model parameters, and
 # can be retrieved from the STATS.coeffs output.

 dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 ...
      4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
 C = [ 0.4001601  0.3333333  0.5  0.0
       0.4001601  0.3333333 -0.5  0.0
       0.4001601 -0.6666667  0.0  0.0
      -0.6002401  0.0000000  0.0  0.5
      -0.6002401  0.0000000  0.0 -0.5];

 [P,ATAB, STATS] = anovan (dv, g, "contrasts", C, "varnames", "score", ...
                          "alpha", 0.05, "display", "on");
***** demo

 # One-way ANOVA with the linear model fit by weighted least squares to
 # account for heteroskedasticity. In this example, the variance appears
 # proportional to the outcome, so weights have been estimated by initially
 # fitting the model without weights and regressing the absolute residuals on
 # the fitted values. Although this data could have been analysed by Welch's
 # ANOVA test, the approach here can generalize to ANOVA models with more than
 # one factor.

 g = [1, 1, 1, 1, 1, 1, 1, 1, ...
      2, 2, 2, 2, 2, 2, 2, 2, ...
      3, 3, 3, 3, 3, 3, 3, 3]';
 y = [13, 16, 16,  7, 11,  5,  1,  9, ...
      10, 25, 66, 43, 47, 56,  6, 39, ...
      11, 39, 26, 35, 25, 14, 24, 17]';

 [P,ATAB,STATS] = anovan(y, g, "display", "off");
 fitted = STATS.X * STATS.coeffs(:,1); # fitted values
 b = polyfit (fitted, abs (STATS.resid), 1);
 v = polyval (b, fitted);  # Variance as a function of the fitted values
 figure("Name", "Regression of the absolute residuals on the fitted values");
 plot (fitted, abs (STATS.resid),'ob');hold on; plot(fitted,v,'-r'); hold off;
 xlabel("Fitted values"); ylabel("Absolute residuals");

 [P,ATAB,STATS] = anovan (y, g, "weights", v.^-1);
***** test
 score = [54 23 45 54 45 43 34 65 77 46 65]';
 gender = {'male' 'male' 'male' 'male' 'male' 'female' 'female' 'female' ...
           'female' 'female' 'female'}';

 [P, T, STATS] = anovan (score,gender,'display','off');
 assert (P(1), 0.2612876773271042, 1e-09);              # compared to p calculated by MATLAB anovan
 assert (sqrt(T{2,6}), abs(1.198608733288208), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
 assert (P(1), 0.2612876773271047, 1e-09);              # compared to p calculated by MATLAB ttest2
 assert (sqrt(T{2,6}), abs(-1.198608733288208), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
***** test
 score = [4.5 5.6; 3.7 6.4; 5.3 6.4; 5.4 6.0; 3.9 5.7]';
 treatment = {'before' 'after'; 'before' 'after'; 'before' 'after';
              'before' 'after'; 'before' 'after'}';
 subject = {'GS' 'GS'; 'JM' 'JM'; 'HM' 'HM'; 'JW' 'JW'; 'PS' 'PS'}';

 [P, ATAB, STATS] = anovan (score(:),{treatment(:),subject(:)},'display','off','sstype',2);
 assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB anovan
 assert (sqrt(ATAB{2,6}), abs(4.00941576558195), 1e-09);  # compared to abs(t) calculated from sqrt(F) by MATLAB anovan
 assert (P(1), 0.016004356735364, 1e-09);              # compared to p calculated by MATLAB ttest2
 assert (sqrt(ATAB{2,6}), abs(-4.00941576558195), 1e-09); # compared to abs(t) calculated by MATLAB ttest2
***** test
 strength = [82 86 79 83 84 85 86 87 74 82 ...
            78 75 76 77 79 79 77 78 82 79]';
 alloy = {'st','st','st','st','st','st','st','st', ...
          'al1','al1','al1','al1','al1','al1', ...
          'al2','al2','al2','al2','al2','al2'}';

 [P, ATAB, STATS] = anovan (strength,{alloy},'display','off');
 assert (P(1), 0.000152643638830491, 1e-09);
 assert (ATAB{2,6}, 15.4, 1e-09);
***** test
 words = [10 13 13; 6 8 8; 11 14 14; 22 23 25; 16 18 20; ...
          15 17 17; 1 1 4; 12 15 17;  9 12 12;  8 9 12];
 subject = [ 1  1  1;  2  2  2;  3  3  3;  4  4  4;  5  5  5; ...
             6  6  6;  7  7  7;  8  8  8;  9  9  9; 10 10 10];
 seconds = [1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5; ...
            1 2 5; 1 2 5; 1 2 5; 1 2 5; 1 2 5;];

 [P, ATAB, STATS] = anovan (words(:),{seconds(:),subject(:)},'model','full','random',2,'sstype',2,'display','off');
 assert (P(1), 1.51865926758752e-07, 1e-09);
 assert (ATAB{2,2}, 52.2666666666667, 1e-09);
 assert (ATAB{3,2}, 942.533333333333, 1e-09);
 assert (ATAB{4,2}, 11.0666666666667, 1e-09);
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 brands = {'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'};
 popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
           'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};

 [P, ATAB, STATS] = anovan (popcorn(:),{brands(:),popper(:)},'display','off','model','full');
 assert (P(1), 7.67895738278171e-07, 1e-09);
 assert (P(2), 0.000100373896304998, 1e-09);
 assert (P(3), 0.746215396636649, 1e-09);
 assert (ATAB{2,6}, 56.7, 1e-09);
 assert (ATAB{3,6}, 32.4, 1e-09);
 assert (ATAB{4,6}, 0.29999999999997, 1e-09);
***** test
 salary = [24 26 25 24 27 24 27 23 15 17 20 16, ...
           25 29 27 19 18 21 20 21 22 19]';
 gender = {'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f' 'f'...
           'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm' 'm'}';
 degree = [1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0]';

 [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',1,'display','off');
 assert (P(1), 0.747462549227232, 1e-09);
 assert (P(2), 1.03809316857694e-08, 1e-09);
 assert (P(3), 0.523689833702691, 1e-09);
 assert (ATAB{2,2}, 0.296969696969699, 1e-09);
 assert (ATAB{3,2}, 272.391841491841, 1e-09);
 assert (ATAB{4,2}, 1.17482517482512, 1e-09);
 assert (ATAB{5,2}, 50.0000000000001, 1e-09);
 [P, ATAB, STATS] = anovan (salary,{degree,gender},'model','full','sstype',1,'display','off');
 assert (P(1), 2.53445097305047e-08, 1e-09);
 assert (P(2), 0.00388133678528749, 1e-09);
 assert (P(3), 0.523689833702671, 1e-09);
 assert (ATAB{2,2}, 242.227272727273, 1e-09);
 assert (ATAB{3,2}, 30.4615384615384, 1e-09);
 assert (ATAB{4,2}, 1.17482517482523, 1e-09);
 assert (ATAB{5,2}, 50.0000000000001, 1e-09);
 [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',2,'display','off');
 assert (P(1), 0.00388133678528743, 1e-09);
 assert (P(2), 1.03809316857694e-08, 1e-09);
 assert (P(3), 0.523689833702691, 1e-09);
 assert (ATAB{2,2}, 30.4615384615385, 1e-09);
 assert (ATAB{3,2}, 272.391841491841, 1e-09);
 assert (ATAB{4,2}, 1.17482517482512, 1e-09);
 assert (ATAB{5,2}, 50.0000000000001, 1e-09);
 [P, ATAB, STATS] = anovan (salary,{gender,degree},'model','full','sstype',3,'display','off');
 assert (P(1), 0.00442898146583742, 1e-09);
 assert (P(2), 1.30634252053587e-08, 1e-09);
 assert (P(3), 0.523689833702691, 1e-09);
 assert (ATAB{2,2}, 29.3706293706294, 1e-09);
 assert (ATAB{3,2}, 264.335664335664, 1e-09);
 assert (ATAB{4,2}, 1.17482517482512, 1e-09);
 assert (ATAB{5,2}, 50.0000000000001, 1e-09);
***** test
 sugar = {'real' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'none' ...
          'fake' 'fake' 'fake' 'real' 'real' 'real' 'none' 'none' 'fake'}';
 milk = {'yes' 'no' 'no' 'yes' 'yes' 'no' 'yes' 'yes' 'yes' ...
         'no' 'no' 'yes' 'no' 'no' 'no' 'no' 'no' 'yes'}';
 babble = [4.6 4.4 3.9 5.6 5.1 5.5 3.9 3.5 3.7...
           5.6 4.7 5.9 6.0 5.4 6.6 5.8 5.3 5.7]';

 [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',1,'display','off');
 assert (P(1), 0.0108632139833963, 1e-09);
 assert (P(2), 0.0810606976703546, 1e-09);
 assert (P(3), 0.00175433329935627, 1e-09);
 assert (ATAB{2,2}, 3.55752380952381, 1e-09);
 assert (ATAB{3,2}, 0.956108477471702, 1e-09);
 assert (ATAB{4,2}, 5.94386771300448, 1e-09);
 assert (ATAB{5,2}, 3.1625, 1e-09);
 [P, ATAB, STATS] = anovan (babble,{milk,sugar},'model','full','sstype',1,'display','off');
 assert (P(1), 0.0373333189297505, 1e-09);
 assert (P(2), 0.017075098787169, 1e-09);
 assert (P(3), 0.00175433329935627, 1e-09);
 assert (ATAB{2,2}, 1.444, 1e-09);
 assert (ATAB{3,2}, 3.06963228699552, 1e-09);
 assert (ATAB{4,2}, 5.94386771300448, 1e-09);
 assert (ATAB{5,2}, 3.1625, 1e-09);
 [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',2,'display','off');
 assert (P(1), 0.017075098787169, 1e-09);
 assert (P(2), 0.0810606976703546, 1e-09);
 assert (P(3), 0.00175433329935627, 1e-09);
 assert (ATAB{2,2}, 3.06963228699552, 1e-09);
 assert (ATAB{3,2}, 0.956108477471702, 1e-09);
 assert (ATAB{4,2}, 5.94386771300448, 1e-09);
 assert (ATAB{5,2}, 3.1625,  1e-09);
 [P, ATAB, STATS] = anovan (babble,{sugar,milk},'model','full','sstype',3,'display','off');
 assert (P(1), 0.0454263063473954, 1e-09);
 assert (P(2), 0.0746719907091438, 1e-09);
 assert (P(3), 0.00175433329935627, 1e-09);
 assert (ATAB{2,2}, 2.13184977578476, 1e-09);
 assert (ATAB{3,2}, 1.00413461538462, 1e-09);
 assert (ATAB{4,2}, 5.94386771300448, 1e-09);
 assert (ATAB{5,2}, 3.1625, 1e-09);
***** test
 drug = {'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' ...
         'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X' 'X';
         'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' ...
         'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y' 'Y';
         'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' ...
         'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z' 'Z'};
 feedback = [1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0;
             1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0];
 diet = [0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1;
         0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1];
 BP = [170 175 165 180 160 158 161 173 157 152 181 190 ...
       173 194 197 190 176 198 164 190 169 164 176 175;
       186 194 201 215 219 209 164 166 159 182 187 174 ...
       189 194 217 206 199 195 171 173 196 199 180 NaN;
       180 187 199 170 204 194 162 184 183 156 180 173 ...
       202 228 190 206 224 204 205 199 170 160 NaN NaN];

 [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 1,'display','off');
 assert (P(1), 7.02561843825325e-05, 1e-09);
 assert (P(2), 0.000425806013389362, 1e-09);
 assert (P(3), 6.16780773446401e-07, 1e-09);
 assert (P(4), 0.261347622678438, 1e-09);
 assert (P(5), 0.0542278432357043, 1e-09);
 assert (P(6), 0.590353225626655, 1e-09);
 assert (P(7), 0.0861628249564267, 1e-09);
 assert (ATAB{2,2}, 3614.70355731226, 1e-09);
 assert (ATAB{3,2}, 2227.46639771024, 1e-09);
 assert (ATAB{4,2}, 5008.25614451819, 1e-09);
 assert (ATAB{5,2}, 437.066007908781, 1e-09);
 assert (ATAB{6,2}, 976.180770397332, 1e-09);
 assert (ATAB{7,2}, 46.616653365254, 1e-09);
 assert (ATAB{8,2}, 814.345251396648, 1e-09);
 assert (ATAB{9,2}, 9065.8,  1e-09);
 [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype',2,'display','off');
 assert (P(1), 9.4879638470754e-05, 1e-09);
 assert (P(2), 0.00124177666315809, 1e-09);
 assert (P(3), 6.86162012732911e-07, 1e-09);
 assert (P(4), 0.260856132341256, 1e-09);
 assert (P(5), 0.0523758623892078, 1e-09);
 assert (P(6), 0.590353225626655, 1e-09);
 assert (P(7), 0.0861628249564267, 1e-09);
 assert (ATAB{2,2}, 3481.72176560122, 1e-09);
 assert (ATAB{3,2}, 1837.08812970469, 1e-09);
 assert (ATAB{4,2}, 4957.20277938622, 1e-09);
 assert (ATAB{5,2}, 437.693674777847, 1e-09);
 assert (ATAB{6,2}, 988.431929811402, 1e-09);
 assert (ATAB{7,2}, 46.616653365254, 1e-09);
 assert (ATAB{8,2}, 814.345251396648, 1e-09);
 assert (ATAB{9,2}, 9065.8,  1e-09);
 [P, ATAB, STATS] = anovan (BP(:),{drug(:),feedback(:),diet(:)},'model','full','sstype', 3,'display','off');
 assert (P(1), 0.000106518678028207, 1e-09);
 assert (P(2), 0.00125371366571508, 1e-09);
 assert (P(3), 5.30813260778464e-07, 1e-09);
 assert (P(4), 0.308353667232981, 1e-09);
 assert (P(5), 0.0562901327343161, 1e-09);
 assert (P(6), 0.599091042141092, 1e-09);
 assert (P(7), 0.0861628249564267, 1e-09);
 assert (ATAB{2,2}, 3430.88156424581, 1e-09);
 assert (ATAB{3,2}, 1833.68031496063, 1e-09);
 assert (ATAB{4,2}, 5080.48346456693, 1e-09);
 assert (ATAB{5,2}, 382.07709497207, 1e-09);
 assert (ATAB{6,2}, 963.037988826813, 1e-09);
 assert (ATAB{7,2}, 44.4519685039322, 1e-09);
 assert (ATAB{8,2}, 814.345251396648, 1e-09);
 assert (ATAB{9,2}, 9065.8, 1e-09);
***** test
 measurement = [444 614 423 625 408  856 447 719 ...
                764 831 586 782 609 1002 606 766]';
 strain= {'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola', ...
          'NIH','NIH','BALB/C','BALB/C','A/J','A/J','129/Ola','129/Ola'}';
 treatment={'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T' 'C' 'T'}';
 block = [1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2]';

 [P, ATAB, STATS] = anovan (measurement/10,{strain,treatment,block},'model','full','random',3,'display','off');
 assert (P(1), 0.0914352969909372, 1e-09);
 assert (P(2), 5.04077373924908e-05, 1e-09);
 assert (P(4), 0.0283196918836667, 1e-09);
 assert (ATAB{2,2}, 286.132500000002, 1e-09);
 assert (ATAB{3,2}, 2275.29, 1e-09);
 assert (ATAB{4,2}, 1242.5625, 1e-09);
 assert (ATAB{5,2}, 495.905000000001, 1e-09);
 assert (ATAB{6,2}, 207.007499999999, 1e-09);
***** test
 pulse = [67.9 65.1 77.3 78.7 79.4 80.4 85.8 86.6 87.5 89.1 ...
          98.6 100.8 99.3 101.7 44.3 47.2 47.6 49.6 50.3 51.8 ...
          60 58.5 58.9 60.7 69.8 70.9 76.2 76.1 77 77.7 84.7]';
 temp = [20.8 20.8 24 24 24 24 26.2 26.2 26.2 26.2 28.4 ...
         29 30.4 30.4 17.2 18.3 18.3 18.3 18.9 18.9 20.4 ...
         21 21 22.1 23.5 24.2 25.9 26.5 26.5 26.5 28.6]';
 species = {'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' 'ex' ...
            'ex' 'ex' 'ex' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' ...
            'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv' 'niv'};

 [P, ATAB, STATS] = anovan (pulse,{species,temp},'model','linear','continuous',2,'sstype','h','display','off');
 assert (P(1), 6.27153318786007e-14, 1e-09);
 assert (P(2), 2.48773241196644e-25, 1e-09);
 assert (ATAB{2,2}, 598.003953318404, 1e-09);
 assert (ATAB{3,2}, 4376.08256843712, 1e-09);
 assert (ATAB{4,2}, 89.3498685376726, 1e-09);
 assert (ATAB{2,6}, 187.399388123951, 1e-09);
 assert (ATAB{3,6}, 1371.35413763454, 1e-09);
***** test
 score = [95.6 82.2 97.2 96.4 81.4 83.6 89.4 83.8 83.3 85.7 ...
          97.2 78.2 78.9 91.8 86.9 84.1 88.6 89.8 87.3 85.4 ...
          81.8 65.8 68.1 70.0 69.9 75.1 72.3 70.9 71.5 72.5 ...
          84.9 96.1 94.6 82.5 90.7 87.0 86.8 93.3 87.6 92.4 ...
          100. 80.5 92.9 84.0 88.4 91.1 85.7 91.3 92.3 87.9 ...
          91.7 88.6 75.8 75.7 75.3 82.4 80.1 86.0 81.8 82.5]';
 treatment = {'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
              'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
              'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' 'yes' ...
              'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
              'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  ...
              'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'  'no'}';
 exercise = {'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
             'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
             'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  ...
             'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  'lo'  ...
             'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' 'mid' ...
             'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'  'hi'}';
 age = [59 65 70 66 61 65 57 61 58 55 62 61 60 59 55 57 60 63 62 57 ...
        58 56 57 59 59 60 55 53 55 58 68 62 61 54 59 63 60 67 60 67 ...
        75 54 57 62 65 60 58 61 65 57 56 58 58 58 52 53 60 62 61 61]';

 [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model','full','continuous',3,'sstype','h','display','off');
 assert (P(5), 0.9245630968248468, 1e-09);
 assert (P(6), 0.791115159521822, 1e-09);
 assert (P(7), 0.9296668751457956, 1e-09);
 [P, ATAB, STATS] = anovan (score,{treatment,exercise,age},'model',[1 0 0; 0 1 0; 0 0 1; 1 1 0],'continuous',3,'sstype','h','display','off');
 assert (P(1), 0.00158132928938933, 1e-09);
 assert (P(2), 2.12537505039986e-07, 1e-09);
 assert (P(3), 0.00390292555160047, 1e-09);
 assert (P(4), 0.0164086580775543, 1e-09);
 assert (ATAB{2,6}, 11.0956027650549, 1e-09);
 assert (ATAB{3,6}, 20.8195665467178, 1e-09);
 assert (ATAB{4,6}, 9.10966630720186, 1e-09);
 assert (ATAB{5,6}, 4.4457923698584, 1e-09);
***** test
 dv =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 g = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
 C = [ 0.4001601  0.3333333  0.5  0.0
       0.4001601  0.3333333 -0.5  0.0
       0.4001601 -0.6666667  0.0  0.0
      -0.6002401  0.0000000  0.0  0.5
      -0.6002401  0.0000000  0.0 -0.5];

 [P,ATAB,STATS] = anovan (dv,g,'contrasts',{C},'display','off');
 assert (STATS.coeffs(1,1), 19.4001, 1e-04);
 assert (STATS.coeffs(2,1), -9.3297, 1e-04);
 assert (STATS.coeffs(3,1), -5.0000, 1e-04);
 assert (STATS.coeffs(4,1), -8.0000, 1e-04);
 assert (STATS.coeffs(5,1), -8.0000, 1e-04);
 assert (STATS.coeffs(1,2), 0.4831, 1e-04);
 assert (STATS.coeffs(2,2), 0.9694, 1e-04);
 assert (STATS.coeffs(3,2), 1.3073, 1e-04);
 assert (STATS.coeffs(4,2), 1.6411, 1e-04);
 assert (STATS.coeffs(5,2), 1.4507, 1e-04);
 assert (STATS.coeffs(1,5), 40.161, 1e-03);
 assert (STATS.coeffs(2,5), -9.624, 1e-03);
 assert (STATS.coeffs(3,5), -3.825, 1e-03);
 assert (STATS.coeffs(4,5), -4.875, 1e-03);
 assert (STATS.coeffs(5,5), -5.515, 1e-03);
 assert (STATS.coeffs(2,6), 5.74e-11, 1e-12);
 assert (STATS.coeffs(3,6), 0.000572, 1e-06);
 assert (STATS.coeffs(4,6), 2.86e-05, 1e-07);
 assert (STATS.coeffs(5,6), 4.44e-06, 1e-08);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/optimalleaforder.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/optimalleaforder.m
***** demo
 randn ("seed", 5)  # for reproducibility
 X = randn (10, 2);
 D = pdist (X);
 tree = linkage(D, 'average');
 optimalleaforder (tree, D, 'Transformation', 'linear')
***** error optimalleaforder ()
***** error optimalleaforder (1)
***** error <tree must be .*> optimalleaforder (ones (2, 2), 1)
***** error <character inputs expected> optimalleaforder ([1 2 3], [1 2; 3 4], "criteria", 5)
***** error <D must be .*> optimalleaforder ([1 2 1], [1 2 3])
***** error <unknown property .*> optimalleaforder ([1 2 1], 1, "xxx", "xxx")
***** error optimalleaforder ([1 2 1], 1, "Transformation", "xxx")
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/procrustes.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/procrustes.m
***** demo
 ## Create some random points in two dimensions
 n = 10;
 randn ("seed", 1);
 X = normrnd (0, 1, [n, 2]);

 ## Those same points, rotated, scaled, translated, plus some noise
 S = [0.5, -sqrt(3)/2; sqrt(3)/2, 0.5]; # rotate 60 degrees
 Y = normrnd (0.5*X*S + 2, 0.05, n, 2);

 ## Conform Y to X, plot original X and Y, and transformed Y
 [d, Z] = procrustes (X, Y);
 plot (X(:,1), X(:,2), "rx", Y(:,1), Y(:,2), "b.", Z(:,1), Z(:,2), "bx");
***** demo
 ## Find Procrustes distance and plot superimposed shape

 X = [40 88; 51 88; 35 78; 36 75; 39 72; 44 71; 48 71; 52 74; 55 77];
 Y = [36 43; 48 42; 31 26; 33 28; 37 30; 40 31; 45 30; 48 28; 51 24];
 plot (X(:,1),X(:,2),"x");
 hold on
 plot (Y(:,1),Y(:,2),"o");
 xlim ([0 100]);
 ylim ([0 100]);
 legend ("Target shape (X)", "Source shape (Y)");
 [d, Z] = procrustes (X, Y)
 plot (Z(:,1), Z(:,2), "s");
 legend ("Target shape (X)", "Source shape (Y)", "Transformed shape (Z)");
 hold off
***** demo
 ## Apply Procrustes transformation to larger set of points

 ## Create matrices with landmark points for two triangles
 X = [5, 0; 5, 5; 8, 5];   # target
 Y = [0, 0; 1, 0; 1, 1];   # source

 ## Create a matrix with more points on the source triangle
 Y_mp = [linspace(Y(1,1),Y(2,1),10)', linspace(Y(1,2),Y(2,2),10)'; ...
         linspace(Y(2,1),Y(3,1),10)', linspace(Y(2,2),Y(3,2),10)'; ...
         linspace(Y(3,1),Y(1,1),10)', linspace(Y(3,2),Y(1,2),10)'];

 ## Plot both shapes, including the larger set of points for the source shape
 plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
 hold on
 plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
 plot (Y_mp(:,1), Y_mp(:,2), "ro");
 xlim ([-1 10]);
 ylim ([-1 6]);
 legend ("Target shape (X)", "Source shape (Y)", ...
         "More points on Y", "Location", "northwest");
 hold off

 ## Obtain the Procrustes transformation
 [d, Z, transform] = procrustes (X, Y)

 ## Use the Procrustes transformation to superimpose the more points (Y_mp)
 ## on the source shape onto the target shape, and then visualize the results.
 Z_mp = transform.b * Y_mp * transform.T + transform.c(1,:);
 figure
 plot ([X(:,1); X(1,1)], [X(:,2); X(1,2)], "bx-");
 hold on
 plot ([Y(:,1); Y(1,1)], [Y(:,2); Y(1,2)], "ro-", "MarkerFaceColor", "r");
 plot (Y_mp(:,1), Y_mp(:,2), "ro");
 xlim ([-1 10]);
 ylim ([-1 6]);
 plot ([Z(:,1); Z(1,1)],[Z(:,2); Z(1,2)],"ks-","MarkerFaceColor","k");
 plot (Z_mp(:,1),Z_mp(:,2),"ks");
 legend ("Target shape (X)", "Source shape (Y)", ...
         "More points on Y", "Transformed source shape (Z)", ...
         "Transformed additional points", "Location", "northwest");
 hold off
***** demo
 ## Compare shapes without reflection

 T = [33, 93; 33, 87; 33, 80; 31, 72; 32, 65; 32, 58; 30, 72; ...
      28, 72; 25, 69; 22, 64; 23, 59; 26, 57; 30, 57];
 S = [48, 83; 48, 77; 48, 70; 48, 65; 49, 59; 49, 56; 50, 66; ...
      52, 66; 56, 65; 58, 61; 57, 57; 54, 56; 51, 55];
 plot (T(:,1), T(:,2), "x-");
 hold on
 plot (S(:,1), S(:,2), "o-");
 legend ("Target shape (d)", "Source shape (b)");
 hold off
 d_false = procrustes (T, S, "reflection", false);
 printf ("Procrustes distance without reflection: %f\n", d_false);
 d_true = procrustes (T, S, "reflection", true);
 printf ("Procrustes distance with reflection: %f\n", d_true);
 d_best = procrustes (T, S, "reflection", "best");
 printf ("Procrustes distance with best fit: %f\n", d_true);
***** error procrustes ();
***** error procrustes (1);
***** error procrustes (1, 2, 3, 4, 5, 6, 7);
***** error<procrustes: X and Y must be 2-dimensional matrices.> ...
 procrustes (ones (2, 2, 2), ones (2, 2, 2));
***** error<procrustes: values in X and Y must be real.> ...
 procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, 3+i]);
***** error<procrustes: values in X and Y must be real.> ...
 procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, NaN]);
***** error<procrustes: values in X and Y must be real.> ...
 procrustes ([1, 2; -3, 4; 2, 3], [1, 2; -3, 4; 2, Inf]);
***** error<procrustes: X and Y must have equal number of rows.> ...
 procrustes (ones (10 ,3), ones (11, 3));
***** error<procrustes: X must have at least as many columns as Y.> ...
 procrustes (ones (10 ,3), ones (10, 4));
***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
 procrustes (ones (10 ,3), ones (10, 3), "reflection");
***** error<procrustes: optional arguments must be in Name-Value pairs.> ...
 procrustes (ones (10 ,3), ones (10, 3), true);
***** error<procrustes: invalid value for scaling.> ...
 procrustes (ones (10 ,3), ones (10, 3), "scaling", 0);
***** error<procrustes: invalid value for scaling.> ...
 procrustes (ones (10 ,3), ones (10, 3), "scaling", [true true]);
***** error<procrustes: invalid value for reflection.> ...
 procrustes (ones (10 ,3), ones (10, 3), "reflection", 1);
***** error<procrustes: invalid value for reflection.> ...
 procrustes (ones (10 ,3), ones (10, 3), "reflection", "some");
***** error<procrustes: invalid name for optional arguments.> ...
 procrustes (ones (10 ,3), ones (10, 3), "param1", "some");
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/princomp.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/princomp.m
***** shared COEFF,SCORE,latent,tsquare,m,x,R,V,lambda,i,S,F
***** test
 x=[7 4 3
    4 1 8
    6 3 5
    8 6 1
    8 5 7
    7 2 9
    5 3 3
    9 5 8
    7 4 5
    8 2 2];
 R = corrcoef (x);
 [V, lambda] = eig (R);
 [~, i] = sort(diag(lambda), "descend"); #arrange largest PC first
 S = V(:, i) * diag(sqrt(diag(lambda)(i)));
 ## contribution of first 2 PCs to each original variable
***** assert(diag(S(:, 1:2)*S(:, 1:2)'), [0.8662; 0.8420; 0.9876], 1E-4);
 B = V(:, i) * diag( 1./ sqrt(diag(lambda)(i)));
 F = zscore(x)*B;
 [COEFF,SCORE,latent,tsquare] = princomp(zscore(x, 1));
***** assert(tsquare,sumsq(F, 2),1E4*eps);
***** test
 x=[1,2,3;2,1,3]';
 [COEFF,SCORE,latent,tsquare] = princomp(x);
 m=[sqrt(2),sqrt(2);sqrt(2),-sqrt(2);-2*sqrt(2),0]/2;
 m(:,1) = m(:,1)*sign(COEFF(1,1));
 m(:,2) = m(:,2)*sign(COEFF(1,2));
***** assert(COEFF,m(1:2,:),10*eps);
***** assert(SCORE,-m,10*eps);
***** assert(latent,[1.5;.5],10*eps);
***** assert(tsquare,[4;4;4]/3,10*eps);
***** test
 x=x';
 [COEFF,SCORE,latent,tsquare] = princomp(x);
 m=[sqrt(2),sqrt(2),0;-sqrt(2),sqrt(2),0;0,0,2]/2;
 m(:,1) = m(:,1)*sign(COEFF(1,1));
 m(:,2) = m(:,2)*sign(COEFF(1,2));
 m(:,3) = m(:,3)*sign(COEFF(3,3));
***** assert(COEFF,m,10*eps);
***** assert(SCORE(:,1),-m(1:2,1),10*eps);
***** assert(SCORE(:,2:3),zeros(2),10*eps);
***** assert(latent,[1;0;0],10*eps);
***** assert(tsquare,[0.5;0.5],10*eps)
***** test
 [COEFF,SCORE,latent,tsquare] = princomp(x, "econ");
***** assert(COEFF,m(:, 1),10*eps);
***** assert(SCORE,-m(1:2,1),10*eps);
***** assert(latent,[1],10*eps);
***** assert(tsquare,[0.5;0.5],10*eps)
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/hotelling_t2test.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/hotelling_t2test.m
***** error<Invalid call to hotelling_t2test.  Correct usage> hotelling_t2test ();
***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
 hotelling_t2test (1);
***** error<hotelling_t2test: X must be a vector or a 2D matrix.> ...
 hotelling_t2test (ones(2,2,2));
***** error<hotelling_t2test: invalid value for alpha.> ...
 hotelling_t2test (ones(20,2), [0, 0], "alpha", 1);
***** error<hotelling_t2test: invalid value for alpha.> ...
 hotelling_t2test (ones(20,2), [0, 0], "alpha", -0.2);
***** error<hotelling_t2test: invalid value for alpha.> ...
 hotelling_t2test (ones(20,2), [0, 0], "alpha", "a");
***** error<hotelling_t2test: invalid value for alpha.> ...
 hotelling_t2test (ones(20,2), [0, 0], "alpha", [0.01, 0.05]);
***** error<hotelling_t2test: invalid Name argument.> ...
 hotelling_t2test (ones(20,2), [0, 0], "name", 0.01);
***** error<hotelling_t2test: if X is a vector, M must be a scalar.> ...
 hotelling_t2test (ones(20,1), [0, 0]);
***** error<hotelling_t2test: X must have more rows than columns.> ...
 hotelling_t2test (ones(4,5), [0, 0, 0, 0, 0]);
***** error<hotelling_t2test: if X is a matrix, M must be a vector of length> ...
 hotelling_t2test (ones(20,5), [0, 0, 0, 0]);
***** test
 randn ("seed", 1);
 x = randn (50000, 5);
 [h, pval, stats] = hotelling_t2test (x);
 assert (h, 0);
 assert (stats.df1, 5);
 assert (stats.df2, 49995);
***** test
 randn ("seed", 1);
 x = randn (50000, 5);
 [h, pval, stats] = hotelling_t2test (x, ones (1, 5) * 10);
 assert (h, 1);
 assert (stats.df1, 5);
 assert (stats.df2, 49995);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/adtest.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/adtest.m
***** error<Invalid call to adtest.  Correct usage is:> adtest ();
***** error<adtest: X must be a vector of real numbers.> adtest (ones (20,2));
***** error<adtest: X must be a vector of real numbers.> adtest ([1+i,0-3i]);
***** error<adtest: invalid distribution family in char string.> ...
 adtest (ones (20,1), "Distribution", "normal");
***** error<adtest: invalid distribution family in cell array.> ...
 adtest (rand (20,1), "Distribution", {"normal", 5, 3});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"norm", 5});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"exp", 5, 4});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"ev", 5});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"logn", 5, 3, 2});
***** error<adtest: invalid distribution parameters in cell array.> ...
 adtest (rand (20,1), "Distribution", {"Weibull", 5});
***** error<adtest: invalid distribution option.> ...
 adtest (rand (20,1), "Distribution", 35);
***** error<adtest: invalid Name argument.> ...
 adtest (rand (20,1), "Name", "norm");
***** error<adtest: invalid Name argument.> ...
 adtest (rand (20,1), "Name", {"norm", 75, 10});
***** error<adtest: asymptotic option is not valid for the composite> ...
 adtest (rand (20,1), "Distribution", "norm", "Asymptotic", true);
***** error<adtest: asymptotic option is not valid for the composite> ...
 adtest (rand (20,1), "MCTol", 0.001, "Asymptotic", true);
***** error<adtest: asymptotic option is not valid for the Monte Carlo> ...
 adtest (rand (20,1), "Distribution", {"norm", 5, 3}, "MCTol", 0.001, ...
         "Asymptotic", true);
***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
 [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
                                 "Alpha", 0.000000001);
***** error<adtest: out of range invalid alpha - lower limit: 0.0005 upper> ...
 [h, pval, ADstat, CV] = adtest (ones (20,1), "Distribution", {"norm",5,3},...
                                 "Alpha", 0.999999999);
***** error<adtest: not enough data for composite testing.> ...
 adtest (10);
***** warning<adtest: out of range min p-value:> ...
 randn ("seed", 34);
 adtest (ones (20,1), "Alpha", 0.000001);
***** warning<adtest: alpha not within the lookup table.> ...
 randn ("seed", 34);
 adtest (normrnd(0,1,100,1), "Alpha", 0.99999);
***** warning<adtest: alpha not within the lookup table.> ...
 randn ("seed", 34);
 adtest (normrnd(0,1,100,1), "Alpha", 0.00001);
***** test
 load examgrades
 x = grades(:,1);
 [h, pval, adstat, cv] = adtest (x);
 assert (h, false);
 assert (pval, 0.1854, 1e-4);
 assert (adstat, 0.5194, 1e-4);
 assert (cv, 0.7470, 1e-4);
***** test
 load examgrades
 x = grades(:,1);
 [h, pval, adstat, cv] = adtest (x, "Distribution", "ev");
 assert (h, false);
 assert (pval, 0.071363, 1e-6);
***** test
 load examgrades
 x = grades(:,1);
 [h, pval, adstat, cv] = adtest (x, "Distribution", {"norm", 75, 10});
 assert (h, false);
 assert (pval, 0.4687, 1e-4);
25 tests, 25 passed, 0 known failure, 0 skipped
[inst/regression_ttest.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/regression_ttest.m
***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest ();
***** error<Invalid call to regression_ttest.  Correct usage> regression_ttest (1);
***** error<regression_ttest: Y must contain finite real numbers.> ...
 regression_ttest ([1 2 NaN]', [2 3 4]');
***** error<regression_ttest: Y must contain finite real numbers.> ...
 regression_ttest ([1 2 Inf]', [2 3 4]');
***** error<regression_ttest: Y must contain finite real numbers.> ...
 regression_ttest ([1 2 3+i]', [2 3 4]');
***** error<regression_ttest: X must contain finite real numbers.> ...
 regression_ttest ([1 2 3]', [2 3 NaN]');
***** error<regression_ttest: X must contain finite real numbers.> ...
 regression_ttest ([1 2 3]', [2 3 Inf]');
***** error<regression_ttest: X must contain finite real numbers.> ...
 regression_ttest ([1 2 3]', [3 4 3+i]');
***** error<regression_ttest: Y and X must be vectors of equal length.> ...
 regression_ttest ([1 2 3]', [3 4 4 5]');
***** error<regression_ttest: invalid value for alpha.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0);
***** error<regression_ttest: invalid value for alpha.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", 1.2);
***** error<regression_ttest: invalid value for alpha.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", [.02 .1]);
***** error<regression_ttest: invalid value for alpha.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", "a");
***** error<regression_ttest: invalid Name argument.> ...
 regression_ttest ([1 2 3]', [2 3 4]', "some", 0.05);
***** error<regression_ttest: invalid value for tail.>  ...
 regression_ttest ([1 2 3]', [2 3 4]', "tail", "val");
***** error<regression_ttest: invalid value for tail.>  ...
 regression_ttest ([1 2 3]', [2 3 4]', "alpha", 0.01, "tail", "val");
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/hotelling_t2test2.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/hotelling_t2test2.m
***** error<Invalid call to hotelling_t2test2.  Correct usage> hotelling_t2test2 ();
***** error<Invalid call to hotelling_t2test2.  Correct usage> ...
 hotelling_t2test2 ([2, 3, 4, 5, 6]);
***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
 hotelling_t2test2 (1, [2, 3, 4, 5, 6]);
***** error<hotelling_t2test2: X must be a vector or a 2D matrix.> ...
 hotelling_t2test2 (ones (2,2,2), [2, 3, 4, 5, 6]);
***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
 hotelling_t2test2 ([2, 3, 4, 5, 6], 2);
***** error<hotelling_t2test2: Y must be a vector or a 2D matrix.> ...
 hotelling_t2test2 ([2, 3, 4, 5, 6], ones (2,2,2));
***** error<hotelling_t2test2: invalid value for alpha.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", 1);
***** error<hotelling_t2test2: invalid value for alpha.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", -0.2);
***** error<hotelling_t2test2: invalid value for alpha.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", "a");
***** error<hotelling_t2test2: invalid value for alpha.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "alpha", [0.01, 0.05]);
***** error<hotelling_t2test2: invalid Name argument.> ...
 hotelling_t2test2 (ones (20,2), ones (20,2), "name", 0.01);
***** error<hotelling_t2test2: if X is a vector, Y must also be a vector.> ...
 hotelling_t2test2 (ones (20,1), ones (20,2));
***** error<hotelling_t2test2: X and Y must have the same number of columns.> ...
 hotelling_t2test2 (ones (20,2), ones (25,3));
***** test
 randn ("seed", 1);
 x1 = randn (60000, 5);
 randn ("seed", 5);
 x2 = randn (30000, 5);
 [h, pval, stats] = hotelling_t2test2 (x1, x2);
 assert (h, 0);
 assert (stats.df1, 5);
 assert (stats.df2, 89994);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/fitcnet.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/fitcnet.m
***** demo
 ## Train a Neural Network on the Fisher's Iris data set and display
 ## a confusion chart with the classification results.

 load fisheriris
 Mdl = fitcnet (meas, species);
 pred_species = resubPredict (Mdl);
 confusionchart (species, pred_species, "Title", ...
 "Fully Connected Neural Network classification on Fisher's Iris dataset");
***** test
 load fisheriris
 x = meas;
 y = grp2idx (species);
 Mdl = fitcnet (x, y, "IterationLimit", 50);
 assert (class (Mdl), "ClassificationNeuralNetwork");
 assert (numel (Mdl.ModelParameters.LayerWeights), 2);
 assert (size (Mdl.ModelParameters.LayerWeights{1}), [10, 5]);
 assert (size (Mdl.ModelParameters.LayerWeights{2}), [3, 11]);
***** error<fitcnet: too few arguments.> fitcnet ()
***** error<fitcnet: too few arguments.> fitcnet (ones (4,1))
***** error<fitcnet: Name-Value arguments must be in pairs.>
 fitcnet (ones (4,2), ones (4, 1), 'LayerSizes')
***** error<fitcnet: number of rows in X and Y must be equal.>
 fitcnet (ones (4,2), ones (3, 1))
***** error<fitcnet: number of rows in X and Y must be equal.>
 fitcnet (ones (4,2), ones (3, 1), 'LayerSizes', 2)
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/dummyvar.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dummyvar.m
***** assert (dummyvar ([]), [])
***** assert (dummyvar (ones (2, 0)), ones (2, 0))
***** test
 ## numeric grouping vector
 g = [1; 2; 1; 3; 2];
 D = dummyvar (g);
 assert (D, [1, 0, 0; 0, 1, 0; 1, 0, 0; 0, 0, 1; 0, 1, 0]);
***** test
 g = categorical ({'a'; 'b'; 'a'}, {'a', 'b', 'c'});
 D = dummyvar (g);
 cats = categories (g);
 g_str = cellstr (g);
 for k = 1:numel (cats)
   mask = strcmp (g_str, cats{k});
   assert (all (D(mask, k) == 1), true);
   assert (all (D(!mask, k) == 0), true);
 endfor
***** test
 g = categorical ({'a'; ''; 'b'}, {'a', 'b', 'c'});
 D = dummyvar (g);
 assert (D, [1, 0, 0; NaN, NaN, NaN; 0, 1, 0]);
***** test
 colors = categorical ({'Red'; 'Blue'; 'Green'; 'Red'; 'Green'; 'Blue'});
 D = dummyvar (colors);
 assert (D, [0, 0, 1; 1, 0, 0; 0, 1, 0; 0, 0, 1; 0, 1, 0; 1, 0, 0]);
***** test
 g1 = [1; 1; 1; 1; 2; 2; 2; 2];
 g2 = [1; 2; 3; 1; 2; 3; 1; 2];
 D = dummyvar ([g1, g2]);
 D1 = [1, 0, 1, 0, 0; 1, 0, 0, 1, 0; 1, 0, 0, 0, 1; 1, 0, 1, 0, 0; ...
       0, 1, 0, 1, 0; 0, 1, 0, 0, 1; 0, 1, 1, 0, 0; 0, 1, 0, 1, 0];
 assert (D, D1);
***** test
 phone = {'mob'; 'land'; 'mob';'mob';'mob';'land';'land'};
 codes = categorical ([202; 202; 103; 103; 202; 103; 202]);
 D = dummyvar ({phone, codes});
 D1 = [1, 0, 0, 1; 0, 1, 0, 1; 1, 0, 1, 0; 1, 0, 1, 0; ...
       1, 0, 0, 1; 0, 1, 1, 0; 0, 1, 0, 1];
 assert (D, D1);
***** test
 colors = {'red'; 'blue'; 'red'; 'green'; 'yellow'; 'blue'};
 D = dummyvar (categorical (colors));
 D1 = [0, 0, 1, 0; 1, 0, 0, 0; 0, 0, 1, 0; 0, 1, 0, 0; 0, 0, 0, 1; 1, 0, 0, 0];
 assert (D, D1);
***** test
 colors = {'red'; 'blue'; 'red'; 'green'; 'yellow'; 'blue'};
 D = dummyvar (colors);
 D1 = [1, 0, 0, 0; 0, 1, 0, 0; 1, 0, 0, 0; 0, 0, 1, 0; 0, 0, 0, 1; 0, 1, 0, 0];
 assert (D, D1);
 D = dummyvar ({colors});
 D1 = [1, 0, 0, 0; 0, 1, 0, 0; 1, 0, 0, 0; 0, 0, 1, 0; 0, 0, 0, 1; 0, 1, 0, 0];
 assert (D, D1);
***** test
 g = [1, 2, 1, 2, 1, 3, 2, 1];
 D = dummyvar (g);
 D1 = [1, 0, 0; 0, 1, 0; 1, 0, 0; 0, 1, 0; 1, 0, 0; 0, 0, 1; 0, 1, 0; 1, 0, 0];
 assert (D, D1);
***** error dummyvar ()
***** error<dummyvar: function called with too many inputs> dummyvar (1, 2)
***** error<dummyvar: categorical grouping variable must be a column vector.> ...
 dummyvar (categorical ({'a', 'b'}))
***** error<dummyvar: numeric grouping variable must be either a vector or a matrix.> ...
 dummyvar (ones (3, 3, 3))
***** error<dummyvar: numeric grouping variable must explicitly contain positive integers.> ...
 dummyvar ([2, 4, 0, 8, 1])
***** error<dummyvar: cellstring grouping variable must be a column vector.> ...
 dummyvar ({'a', 'b'})
***** error<dummyvar: all grouping variables in cell array must have the same number of observations.> ...
 dummyvar ({[2;3;4;5], [1;2;3]})
***** error<dummyvar: unsupported type of grouping variable.> dummyvar ([true; false])
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/wblplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/wblplot.m
***** demo
 x = [16 34 53 75 93 120];
 wblplot (x);
***** demo
 x = [2 3 5 7 11 13 17 19 23 29 31 37 41 43 47 53 59 61 67]';
 c = [0 1 0 1 0 1 1 1 0 0 1 0 1 0 1 1 0 1 1]';
 [h, p] = wblplot (x, c);
 p
***** demo
 x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
 [h, p] = wblplot (x, [], [], 0.05);
 p
 ## Benchmark Reliasoft eta = 146.2545 beta 1.1973 rho = 0.9999
***** demo
 x = [46 64 83 105 123 150 150];
 c = [0 0 0 0 0 0 1];
 f = [1 1 1 1 1 1 4];
 wblplot (x, c, f, 0.05);
***** demo
 x = [46 64 83 105 123 150 150];
 c = [0 0 0 0 0 0 1];
 f = [1 1 1 1 1 1 4];
 ## Subtract 30.92 from x to simulate a 3 parameter wbl with gamma = 30.92
 wblplot (x - 30.92, c, f, 0.05);
***** test
 hf = figure ("visible", "off");
 unwind_protect
   x = [16, 34, 53, 75, 93, 120, 150, 191, 240 ,339];
   [h, p] = wblplot (x, [], [], 0.05);
   assert (numel (h), 4)
   assert (p(1), 146.2545, 1E-4)
   assert (p(2), 1.1973, 1E-4)
   assert (p(3), 0.9999, 5E-5)
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
1 test, 1 passed, 0 known failure, 0 skipped
[inst/squareform.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/squareform.m
***** shared v, m
 v = 1:6;
 m = [0 1 2 3;1 0 4 5;2 4 0 6;3 5 6 0];
***** assert (squareform (v), m)
***** assert (squareform (squareform (v)), v)
***** assert (squareform (m), v)
***** assert (squareform (v'), m)
***** assert (squareform (1), [0 1;1 0])
***** assert (squareform (1, "tomatrix"), [0 1; 1 0])
***** assert (squareform (0, "tovector"), zeros (1, 0))
***** test
 for c = {@single, @double, @uint8, @uint32, @uint64}
   f = c{1};
   assert (squareform (f (v)), f (m))
   assert (squareform (f (m)), f (v))
 endfor
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/sampsizepwr.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/sampsizepwr.m
***** demo
 ## Compute the mean closest to 100 that can be determined to be
 ## significantly different from 100 using a t-test with a sample size
 ## of 60 and a power of 0.8.
 mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
 disp (mu1);
***** demo
 ## Compute the sample sizes required to distinguish mu0 = 100 from
 ## mu1 = 110 by a two-sample t-test with a ratio of the larger and the
 ## smaller sample sizes of 1.5 and a power of 0.6.
 [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5)
***** demo
 ## Compute the sample size N required to distinguish p=.26 from p=.2
 ## with a binomial test.  The result is approximate, so make a plot to
 ## see if any smaller N values also have the required power of 0.6.
 Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
 nn = 1:250;
 pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
 Nexact = min (nn(pwr >= 0.6));
 plot(nn,pwr,'b-', [Napprox Nexact],pwr([Napprox Nexact]),'ro');
 grid on
***** demo
 ## The company must test 52 bottles to detect the difference between a mean
 ## volume of 100 mL and 102 mL with a power of 0.80.  Generate a power curve
 ## to visualize how the sample size affects the power of the test.

 nout = sampsizepwr('t',[100 5],102,0.80);
 nn = 1:100;
 pwrout = sampsizepwr('t',[100 5],102,[],nn);

 figure;
 plot (nn, pwrout, "b-", nout, 0.8, "ro")
 title ("Power versus Sample Size")
 xlabel ("Sample Size")
 ylabel ("Power")
***** error<sampsizepwr: test type must be a non-empty string.> ...
 out = sampsizepwr ([], [100, 10], [], 0.8, 60);
***** error<sampsizepwr: test type must be a non-empty string.> ...
 out = sampsizepwr (3, [100, 10], [], 0.8, 60);
***** error<sampsizepwr: test type must be a non-empty string.> ...
 out = sampsizepwr ({"t", "t2"}, [100, 10], [], 0.8, 60);
***** error<sampsizepwr: invalid test type.> ...
 out = sampsizepwr ("reg", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: parameters must be numeric.> ...
 out = sampsizepwr ("t", ["a", "e"], [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("z", 100, [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("t", 100, [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("t2", 60, [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("var", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("p", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: invalid size of parameters for this test type.> ...
 out = sampsizepwr ("r", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("z", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("t", [100, 10], [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("var", 2, [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("p", 0.1, [], 0.8, 60);
***** error<sampsizepwr: wrong number of output arguments for this test type.> ...
 [out, N1] = sampsizepwr ("r", 0.5, [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("z", [100, 0], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("z", [100, -5], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("t", [100, 0], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("t", [100, -5], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 [out, N1] = sampsizepwr ("t2", [100, 0], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 [out, N1] = sampsizepwr ("t2", [100, -5], [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("var", 0, [], 0.8, 60);
***** error<sampsizepwr: negative or zero variance.> ...
 out = sampsizepwr ("var", -5, [], 0.8, 60);
***** error<sampsizepwr: out of range probability.> ...
 out = sampsizepwr ("p", 0, [], 0.8, 60);
***** error<sampsizepwr: out of range probability.> ...
 out = sampsizepwr ("p", 1.2, [], 0.8, 60);
***** error<sampsizepwr: out of range regression coefficient.> ...
 out = sampsizepwr ("r", -1.5, [], 0.8, 60);
***** error<sampsizepwr: out of range regression coefficient.> ...
 out = sampsizepwr ("r", -1, [], 0.8, 60);
***** error<sampsizepwr: out of range regression coefficient.> ...
 out = sampsizepwr ("r", 1.2, [], 0.8, 60);
***** error<sampsizepwr: regression coefficient must not be 0.> ...
 out = sampsizepwr ("r", 0, [], 0.8, 60);
***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", -0.2);
***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 0);
***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", 1.5);
***** error<sampsizepwr: invalid value for 'alpha' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "alpha", "zero");
***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", 1.5);
***** error<sampsizepwr: 'tail' parameter must be a non-empty string.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", {"both", "left"});
***** error<sampsizepwr: invalid value for 'tail' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "tail", "other");
***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", "some");
***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", 0.5);
***** error<sampsizepwr: invalid value for 'ratio' parameter.> ...
 out = sampsizepwr ("r", 0.2, [], 0.8, 60, "ratio", [2, 1.3, 0.3]);
***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
 out = sampsizepwr ("z", [100, 5], [], [], 60);
***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
 out = sampsizepwr ("z", [100, 5], 110, [], []);
***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
 out = sampsizepwr ("z", [100, 5], [], 0.8, []);
***** error<sampsizepwr: only one of either p1, power, or n must be missing.> ...
 out = sampsizepwr ("z", [100, 5], 110, 0.8, 60);
***** error<sampsizepwr: alternative hypothesis parameter must be numeric.> ...
 out = sampsizepwr ("z", [100, 5], "mu", [], 60);
***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
 out = sampsizepwr ("var", 5, -1, [], 60);
***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
 out = sampsizepwr ("p", 0.8, 1.2, [], 60, "tail", "right");
***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
 out = sampsizepwr ("r", 0.8, 1.2, [], 60);
***** error<sampsizepwr: alternative hypothesis parameter out of range.> ...
 out = sampsizepwr ("r", 0.8, -1.2, [], 60);
***** error<sampsizepwr: invalid value for POWER.> ...
 out = sampsizepwr ("z", [100, 5], 110, 1.2);
***** error<sampsizepwr: invalid value for POWER.> ...
 out = sampsizepwr ("z", [100, 5], 110, 0);
***** error<sampsizepwr: Cannot compute N or P1 unless POWER> ...
 out = sampsizepwr ("z", [100, 5], 110, 0.05, [], "alpha", 0.1);
***** error<sampsizepwr: input arguments size mismatch.> ...
 out = sampsizepwr ("z", [100, 5], [], [0.8, 0.7], [60, 80, 100]);
***** error<sampsizepwr: Same value for null and alternative hypothesis.> ...
 out = sampsizepwr ("t", [100, 5], 100, 0.8, []);
***** error<sampsizepwr: Invalid P1 for testing left tail.> ...
 out = sampsizepwr ("t", [100, 5], 110, 0.8, [], "tail", "left");
***** error<sampsizepwr: Invalid P1 for testing right tail.> ...
 out = sampsizepwr ("t", [100, 5], 90, 0.8, [], "tail", "right");
***** warning<sampsizepwr: approximate N.> ...
 Napprox = sampsizepwr ("p", 0.2, 0.26, 0.6);
***** warning<sampsizepwr: approximate N.> ...
 Napprox = sampsizepwr ("p", 0.30, 0.36, 0.8);
***** test
 mu1 = sampsizepwr ("t", [100, 10], [], 0.8, 60);
 assert (mu1, 103.67704316, 1e-8);
***** test
 [N1,N2] = sampsizepwr ("t2", [100, 10], 110, 0.6, [], "ratio", 1.5);
 assert (N1, 9);
 assert (N2, 14);
***** test
 nn = 1:250;
 pwr = sampsizepwr ("p", 0.2, 0.26, [], nn);
 pwr_out = [0, 0.0676, 0.0176, 0.0566, 0.0181, 0.0431, 0.0802, 0.0322];
 assert (pwr([1:8]), pwr_out, 1e-4 * ones (1,8));
 pwr_out = [0.59275, 0.6073, 0.62166, 0.6358, 0.6497, 0.6087, 0.6229, 0.6369];
 assert (pwr([243:end]), pwr_out, 1e-4 * ones (1,8));
***** test
 nout = sampsizepwr ("t", [100, 5], 102, 0.80);
 assert (nout, 52);
***** test
 power = sampsizepwr ("t", [20, 5], 25, [], 5, "Tail", "right");
 assert (power, 0.5797373588621888, 1e-14);
***** test
 nout = sampsizepwr ("t", [20, 5], 25, 0.99, [], "Tail", "right");
 assert (nout, 18);
***** test
 p1out = sampsizepwr ("t", [20, 5], [], 0.95, 10, "Tail", "right");
 assert (p1out, 25.65317979360237, 2e-14);
***** test
 pwr = sampsizepwr ("t2", [1.4, 0.2], 1.7, [], 5, "Ratio", 2);
 assert (pwr, 0.716504004686586, 1e-14);
***** test
 n = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, []);
 assert (n, 11);
***** test
 [n1, n2] = sampsizepwr ("t2", [1.4, 0.2], 1.7, 0.9, [], "Ratio", 2);
 assert ([n1, n2], [8, 16]);
68 tests, 68 passed, 0 known failure, 0 skipped
[inst/jackknife.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/jackknife.m
***** demo
 for k = 1:1000
   rand ("seed", k);  # for reproducibility
   x = rand (10, 1);
   s(k) = std (x);
   jackstat = jackknife (@std, x);
   j(k) = 10 * std (x) - 9 * mean (jackstat);
 endfor
 figure();
 hist ([s', j'], 0:sqrt(1/12)/10:2*sqrt(1/12))
***** demo
 for k = 1:1000
   randn ("seed", k); # for reproducibility
   x = randn (1, 50);
   rand ("seed", k);  # for reproducibility
   y = rand (1, 50);
   jackstat = jackknife (@(x) std(x{1})/std(x{2}), y, x);
   j(k) = 50 * std (y) / std (x) - 49 * mean (jackstat);
   v(k) = sumsq ((50 * std (y) / std (x) - 49 * jackstat) - j(k)) / (50 * 49);
 endfor
 t = (j - sqrt (1 / 12)) ./ sqrt (v);
 figure();
 plot (sort (tcdf (t, 49)), ...
       "-;Almost linear mapping indicates good fit with t-distribution.;")
***** test
 ##Example from Quenouille, Table 1
 d=[0.18 4.00 1.04 0.85 2.14 1.01 3.01 2.33 1.57 2.19];
 jackstat = jackknife ( @(x) 1/mean(x), d );
 assert ( 10 / mean(d) - 9 * mean(jackstat), 0.5240, 1e-5 );
1 test, 1 passed, 0 known failure, 0 skipped
[inst/einstein.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/einstein.m
***** demo
 einstein (0.4, 0.6)
***** demo
 einstein (0.2, 0.5)
***** demo
 einstein (0.6, 0.1)
***** test
 hf = figure ("visible", "off");
 unwind_protect
   tiles = einstein (0.4, 0.6);
   assert (isstruct (tiles), true);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error<Invalid call to einstein.  Correct usage is> einstein
***** error<Invalid call to einstein.  Correct usage is> einstein (0.5)
***** error<einstein: A and B must be within the open interval> einstein (0, 0.9)
***** error<einstein: A and B must be within the open interval> einstein (0.4, 1)
***** error<einstein: A and B must be within the open interval> einstein (-0.4, 1)
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/silhouette.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/silhouette.m
***** demo
 load fisheriris;
 X = meas(:,3:4);
 cidcs = kmeans (X, 3, "Replicates", 5);
 silhouette (X, cidcs);
 y_labels(cidcs([1 51 101])) = unique (species);
 set (gca, "yticklabel", y_labels);
 title ("Fisher's iris data");
***** error silhouette ();
***** error silhouette ([1 2; 1 1]);
***** error <X .* doesn't match .* clust> silhouette ([1 2; 1 1], [1 2 3]');
***** error <invalid metric> silhouette ([1 2; 1 1], [1 2]', "xxx");
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/anova1.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/anova1.m
***** demo
 x = meshgrid (1:6);
 randn ("seed", 15);    # for reproducibility
 x = x + normrnd (0, 1, 6, 6);
 anova1 (x, [], 'off');
***** demo
 x = meshgrid (1:6);
 randn ("seed", 15);    # for reproducibility
 x = x + normrnd (0, 1, 6, 6);
 [p, atab] = anova1(x);
***** demo
 x = ones (50, 4) .* [-2, 0, 1, 5];
 randn ("seed", 13);    # for reproducibility
 x = x + normrnd (0, 2, 50, 4);
 groups = {"A", "B", "C", "D"};
 anova1 (x, groups);
***** demo
 y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
 g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
 anova1 (y(:), g(:), "on", "unequal");
***** test
 data = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000, ...
         0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988, ...
         0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996, ...
         1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996, ...
         0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996, ...
         1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996, ...
         0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002, ...
         0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006, ...
         1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991, ...
         0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997];
 group = [1:10] .* ones (10,10);
 group = group(:);
 [p, tbl] = anova1 (data, group, "off");
 assert (p, 0.022661, 1e-6);
 assert (tbl{2,5}, 2.2969, 1e-4);
 assert (tbl{2,3}, 9, 0);
 assert (tbl{4,2}, 0.003903, 1e-6);
 data = reshape (data, 10, 10);
 [p, tbl, stats] = anova1 (data, [], "off");
 assert (p, 0.022661, 1e-6);
 assert (tbl{2,5}, 2.2969, 1e-4);
 assert (tbl{2,3}, 9, 0);
 assert (tbl{4,2}, 0.003903, 1e-6);
 means = [0.998, 0.9991, 0.9954, 0.9982, 0.9919, 0.9988, 1.0015, 1.0004, 0.9983, 0.9948];
 N = 10 * ones (1, 10);
 assert (stats.means, means, 1e-6);
 assert (length (stats.gnames), 10, 0);
 assert (stats.n, N, 0);
***** test
 y = [54 87 45; 23 98 39; 45 64 51; 54 77 49; 45 89 50; 47 NaN 55];
 g = [1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ; 1  2  3 ];
 [p, tbl] = anova1 (y(:), g(:), "off", "equal");
 assert (p, 0.00004163, 1e-6);
 assert (tbl{2,5}, 22.573418, 1e-6);
 assert (tbl{2,3}, 2, 0);
 assert (tbl{3,3}, 14, 0);
 [p, tbl] = anova1 (y(:), g(:), "off", "unequal");
 assert (p, 0.00208877, 1e-8);
 assert (tbl{2,5}, 15.523192, 1e-6);
 assert (tbl{2,3}, 2, 0);
 assert (tbl{2,4}, 7.5786897, 1e-6);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/cdfcalc.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/cdfcalc.m
***** test
 x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
 [yCDF, xCDF, n, emsg, eid] = cdfcalc (x);
 assert (yCDF, [0, 0.3, 0.5, 0.8, 0.9, 1]');
 assert (xCDF, [2, 3, 4, 5, 6]');
 assert (n, 10);
***** shared x
 x = [2, 4, 3, 2, 4, 3, 2, 5, 6, 4];
***** error yCDF = cdfcalc (x);
***** error [yCDF, xCDF] = cdfcalc ();
***** error [yCDF, xCDF] = cdfcalc (x, x);
***** warning [yCDF, xCDF] = cdfcalc (ones(10,2));
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/violin.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/violin.m
***** demo
 clf
 x = zeros (9e2, 10);
 for i=1:10
   x(:,i) = (0.1 * randn (3e2, 3) * (randn (3,1) + 1) + 2 * randn (1,3))(:);
 endfor
 h = violin (x, "color", "c");
 axis tight
 set (h.violin, "linewidth", 2);
 set (gca, "xgrid", "on");
 xlabel ("Variables")
 ylabel ("Values")
***** demo
 clf
 data = {randn(100,1)*5+140, randn(130,1)*8+135};
 subplot (1,2,1)
 title ("Grade 3 heights - vertical");
 set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
 violin (data, "Nbins", 10);
 axis tight

 subplot(1,2,2)
 title ("Grade 3 heights - horizontal");
 set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
 violin (data, "horizontal", "Nbins", 10);
 axis tight
***** demo
 clf
 data = exprnd (0.1, 500,4);
 violin (data, "nbins", {5,10,50,100});
 axis ([0 5 0 max(data(:))])
***** demo
 clf
 data = exprnd (0.1, 500,4);
 violin (data, "color", jet(4));
 axis ([0 5 0 max(data(:))])
***** demo
 clf
 data = repmat(exprnd (0.1, 500,1), 1, 4);
 violin (data, "width", linspace (0.1,0.5,4));
 axis ([0 5 0 max(data(:))])
***** demo
 clf
 data = repmat(exprnd (0.1, 500,1), 1, 4);
 violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
 axis ([0 5 0 max(data(:))])
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = exprnd (0.1, 500,4);
   violin (data, "color", jet(4));
   axis ([0 5 0 max(data(:))])
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = {randn(100,1)*5+140, randn(130,1)*8+135};
   subplot (1,2,1)
   title ("Grade 3 heights - vertical");
   set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
   violin (data, "Nbins", 10);
   axis tight
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = {randn(100,1)*5+140, randn(130,1)*8+135};
   subplot (1,2,1)
   title ("Grade 3 heights - vertical");
   set (gca, "xtick", 1:2, "xticklabel", {"girls"; "boys"});
   violin (data, "Nbins", 10);
   axis tight
   subplot(1,2,2)
   title ("Grade 3 heights - horizontal");
   set (gca, "ytick", 1:2, "yticklabel", {"girls"; "boys"});
   violin (data, "horizontal", "Nbins", 10);
   axis tight
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = repmat(exprnd (0.1, 500,1), 1, 4);
   violin (data, "nbins", [5,10,50,100], "smoothfactor", [4 4 8 10]);
   axis ([0 5 0 max(data(:))])
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   data = repmat(exprnd (0.1, 500,1), 1, 4);
   violin (data, "width", linspace (0.1,0.5,4));
   axis ([0 5 0 max(data(:))])
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/boxplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/boxplot.m
***** demo
 axis ([0, 3]);
 randn ("seed", 1);    # for reproducibility
 girls = randn (10, 1) * 5 + 140;
 randn ("seed", 2);    # for reproducibility
 boys = randn (13, 1) * 8 + 135;
 boxplot ({girls, boys});
 set (gca (), "xtick", [1 2], "xticklabel", {"girls", "boys"})
 title ("Grade 3 heights");
***** demo
 randn ("seed", 7);    # for reproducibility
 A = randn (10, 1) * 5 + 140;
 randn ("seed", 8);    # for reproducibility
 B = randn (25, 1) * 8 + 135;
 randn ("seed", 9);    # for reproducibility
 C = randn (20, 1) * 6 + 165;
 data = [A; B; C];
 groups = [(ones (10, 1)); (ones (25, 1) * 2); (ones (20, 1) * 3)];
 labels = {"Team A", "Team B", "Team C"};
 pos = [2, 1, 3];
 boxplot (data, groups, "Notch", "on", "Labels", labels, "Positions", pos, ...
          "OutlierTags", "on", "BoxStyle", "filled");
 title ("Example of Group splitting with paired vectors");
***** demo
 randn ("seed", 1);    # for reproducibility
 data = randn (100, 9);
 boxplot (data, "notch", "on", "boxstyle", "filled", ...
          "colors", "ygcwkmb", "whisker", 1.2);
 title ("Example of different colors specified with characters");
***** demo
 randn ("seed", 5);    # for reproducibility
 data = randn (100, 13);
 colors = [0.7 0.7 0.7; ...
           0.0 0.4 0.9; ...
           0.7 0.4 0.3; ...
           0.7 0.1 0.7; ...
           0.8 0.7 0.4; ...
           0.1 0.8 0.5; ...
           0.9 0.9 0.2];
 boxplot (data, "notch", "on", "boxstyle", "filled", ...
          "colors", colors, "whisker", 1.3, "boxwidth", "proportional");
 title ("Example of different colors specified as RGB values");
***** error <numerical array or cell array containing> boxplot ("a")
***** error <data cells must contain> boxplot ({[1 2 3], "a"})
***** error <grouping vector may only be passed> boxplot ([1 2 3], 1, {2, 3})
***** error <grouping vector must be numerical> boxplot ([1 2 3], {"a", "b"})
***** error <'Notch' input argument accepts> boxplot ([1:10], "notch", "any")
***** error <boxplot: invalid 'Notch' value.> boxplot ([1:10], "notch", i)
***** error <boxplot: invalid 'Notch' value.> boxplot ([1:10], "notch", {})
***** error <must be character> boxplot (1, "symbol", 1)
***** error <'Orientation' input argument accepts only> boxplot (1, "orientation", "diagonal")
***** error <boxplot: invalid 'Orientation' value.> boxplot (1, "orientation", {})
***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", "a")
***** error <'Whisker' input argument accepts only> boxplot (1, "whisker", [1 3])
***** error <'OutlierTags' input argument accepts only> boxplot (3, "OutlierTags", "maybe")
***** error <boxplot: invalid 'OutlierTags' value.> boxplot (3, "OutlierTags", {})
***** error <'Sample_IDs' input argument accepts only> boxplot (1, "sample_IDs", 1)
***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", 2)
***** error <'BoxWidth' input argument accepts only> boxplot (1, "boxwidth", "anything")
***** error <'Widths' input argument accepts only> boxplot (5, "widths", "a")
***** error <'Widths' input argument accepts only> boxplot (5, "widths", [1:4])
***** error <'Widths' input argument accepts only> boxplot (5, "widths", [])
***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", "a")
***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [1:4])
***** error <'CapWidths' input argument accepts only> boxplot (5, "capwidths", [])
***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", 1)
***** error <'BoxStyle' input argument accepts only> boxplot (1, "Boxstyle", "garbage")
***** error <'Positions' input argument accepts only> boxplot (1, "positions", "aa")
***** error <'Labels' input argument accepts only> boxplot (3, "labels", [1 5])
***** error <'Colors' input argument accepts only> boxplot (1, "colors", {})
***** error <'Colors' input argument accepts only> boxplot (2, "colors", [1 2 3 4])
***** error <Sample_IDs must match the data> boxplot (randn (10, 3), 'Sample_IDs', {"a", "b"})
***** error <with the formalism> boxplot (rand (3, 3), [1 2])
***** test
 hf = figure ("visible", "off");
 unwind_protect
   [a, b] = boxplot (rand (10, 3));
   assert (size (a), [7, 3]);
   assert (numel (b.box), 3);
   assert (numel (b.whisker), 12);
   assert (numel (b.median), 3);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   [~, b] = boxplot (rand (10, 3), "BoxStyle", "filled", "colors", "ybc");
   assert (numel (b.box_fill), 3);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   hold on
   [a, b] = boxplot (rand (10, 3));
   assert (ishold, true);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
34 tests, 34 passed, 0 known failure, 0 skipped
[inst/anova2.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/anova2.m
***** demo

 # Factorial (Crossed) Two-way ANOVA with Interaction

 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];

 [p, atab, stats] = anova2(popcorn, 3, "on");
***** demo

 # One-way Repeated Measures ANOVA (Rows are a crossed random factor)

 data = [54, 43, 78, 111;
         23, 34, 37, 41;
         45, 65, 99, 78;
         31, 33, 36, 35;
         15, 25, 30, 26];

 [p, atab, stats] = anova2 (data, 1, "on", "linear");
***** demo

 # Balanced Nested One-way ANOVA (Rows are a nested random factor)

 data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
         6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
         5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
         6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];

 [p, atab, stats] = anova2 (data, 4, "on", "nested");
***** test
 ## Test for anova2 ("interaction")
 ## comparison with results from Matlab for column effect
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [p, atab, stats] = anova2 (popcorn, 3, "off");
 assert (p(1), 7.678957383294716e-07, 1e-14);
 assert (p(2), 0.0001003738963050171, 1e-14);
 assert (p(3), 0.7462153966366274, 1e-14);
 assert (atab{2,5}, 56.700, 1e-14);
 assert (atab{2,3}, 2, 0);
 assert (atab{4,2}, 0.08333333333333348, 1e-14);
 assert (atab{5,4}, 0.1388888888888889, 1e-14);
 assert (atab{5,2}, 1.666666666666667, 1e-14);
 assert (atab{6,2}, 22);
 assert (stats.source, "anova2");
 assert (stats.colmeans, [6.25, 4.75, 4]);
 assert (stats.inter, true);
 assert (stats.pval, 0.7462153966366274, 1e-14);
 assert (stats.df, 12);
***** test
 ## Test for anova2 ("linear") - comparison with results from GraphPad Prism 8
 data = [54, 43, 78, 111;
         23, 34, 37, 41;
         45, 65, 99, 78;
         31, 33, 36, 35;
         15, 25, 30, 26];
 [p, atab, stats] = anova2 (data, 1, "off", "linear");
 assert (atab{2,2}, 2174.95, 1e-10);
 assert (atab{3,2}, 8371.7, 1e-10);
 assert (atab{4,2}, 2404.3, 1e-10);
 assert (atab{5,2}, 12950.95, 1e-10);
 assert (atab{2,4}, 724.983333333333, 1e-10);
 assert (atab{3,4}, 2092.925, 1e-10);
 assert (atab{4,4}, 200.358333333333, 1e-10);
 assert (atab{2,5}, 3.61843363972882, 1e-10);
 assert (atab{3,5}, 10.445909412303, 1e-10);
 assert (atab{2,6}, 0.087266112738617, 1e-10);
 assert (atab{3,6}, 0.000698397753556, 1e-10);
***** test
 ## Test for anova2 ("nested") - comparison with results from GraphPad Prism 8
 data = [4.5924 7.3809 21.322; -0.5488 9.2085 25.0426; ...
         6.1605 13.1147 22.66; 2.3374 15.2654 24.1283; ...
         5.1873 12.4188 16.5927; 3.3579 14.3951 10.2129; ...
         6.3092 8.5986 9.8934; 3.2831 3.4945 10.0203];
 [p, atab, stats] = anova2 (data, 4, "off", "nested");
 assert (atab{2,2}, 745.360306290833, 1e-10);
 assert (atab{3,2}, 278.01854140125, 1e-10);
 assert (atab{4,2}, 180.180377467501, 1e-10);
 assert (atab{5,2}, 1203.55922515958, 1e-10);
 assert (atab{2,4}, 372.680153145417, 1e-10);
 assert (atab{3,4}, 92.67284713375, 1e-10);
 assert (atab{4,4}, 10.0100209704167, 1e-10);
 assert (atab{2,5}, 4.02146005730833, 1e-10);
 assert (atab{3,5}, 9.25800729165627, 1e-10);
 assert (atab{2,6}, 0.141597630656771, 1e-10);
 assert (atab{3,6}, 0.000636643812875719, 1e-10);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/dist_stat/ncx2stat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/ncx2stat.m
***** error<ncx2stat: function called with too few input arguments.> ncx2stat ()
***** error<ncx2stat: function called with too few input arguments.> ncx2stat (1)
***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat ({}, 2)
***** error<ncx2stat: DF and LAMBDA must be numeric.> ncx2stat (1, "")
***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (i, 2)
***** error<ncx2stat: DF and LAMBDA must not be complex.> ncx2stat (1, i)
***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
 ncx2stat (ones (3), ones (2))
***** error<ncx2stat: DF and LAMBDA must be of common size or scalars.> ...
 ncx2stat (ones (2), ones (3))
***** shared df, d1
 df = [2, 0, -1, 1, 4];
 d1 = [1, NaN, 3, -1, 2];
***** assert (ncx2stat (df, d1), [3, NaN, NaN, NaN, 6]);
***** assert (ncx2stat ([df(1:2), df(4:5)], 1), [3, NaN, 2, 5]);
***** assert (ncx2stat ([df(1:2), df(4:5)], 3), [5, NaN, 4, 7]);
***** assert (ncx2stat ([df(1:2), df(4:5)], 2), [4, NaN, 3, 6]);
***** assert (ncx2stat (2, [d1(1), d1(3:5)]), [3, 5, NaN, 4]);
***** assert (ncx2stat (0, [d1(1), d1(3:5)]), [NaN, NaN, NaN, NaN]);
***** assert (ncx2stat (1, [d1(1), d1(3:5)]), [2, 4, NaN, 3]);
***** assert (ncx2stat (4, [d1(1), d1(3:5)]), [5, 7, NaN, 6]);
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_stat/gamstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/gamstat.m
***** error<gamstat: function called with too few input arguments.> gamstat ()
***** error<gamstat: function called with too few input arguments.> gamstat (1)
***** error<gamstat: A and B must be numeric.> gamstat ({}, 2)
***** error<gamstat: A and B must be numeric.> gamstat (1, "")
***** error<gamstat: A and B must not be complex.> gamstat (i, 2)
***** error<gamstat: A and B must not be complex.> gamstat (1, i)
***** error<gamstat: A and B must be of common size or scalars.> ...
 gamstat (ones (3), ones (2))
***** error<gamstat: A and B must be of common size or scalars.> ...
 gamstat (ones (2), ones (3))
***** test
 a = 1:6;
 b = 1:0.2:2;
 [m, v] = gamstat (a, b);
 expected_m = [1.00, 2.40, 4.20,  6.40,  9.00, 12.00];
 expected_v = [1.00, 2.88, 5.88, 10.24, 16.20, 24.00];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 a = 1:6;
 [m, v] = gamstat (a, 1.5);
 expected_m = [1.50, 3.00, 4.50, 6.00,  7.50,  9.00];
 expected_v = [2.25, 4.50, 6.75, 9.00, 11.25, 13.50];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/gpstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/gpstat.m
***** error<gpstat: function called with too few input arguments.> gpstat ()
***** error<gpstat: function called with too few input arguments.> gpstat (1)
***** error<gpstat: function called with too few input arguments.> gpstat (1, 2)
***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat ({}, 2, 3)
***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, "", 3)
***** error<gpstat: K, SIGMA, and MU must be numeric.> gpstat (1, 2, "")
***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (i, 2, 3)
***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, i, 3)
***** error<gpstat: K, SIGMA, and MU must not be complex.> gpstat (1, 2, i)
***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gpstat (ones (3), ones (2), 3)
***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gpstat (ones (2), 2, ones (3))
***** error<gpstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gpstat (1, ones (2), ones (3))
***** shared x, y
 x = [-Inf, -1, 0, 1/2, 1, Inf];
 y = [0, 0.5, 1, 2, Inf, Inf];
***** assert (gpstat (x, ones (1,6), zeros (1,6)), y, eps)
***** assert (gpstat (single (x), 1, 0), single (y), eps("single"))
***** assert (gpstat (x, single (1), 0), single (y), eps("single"))
***** assert (gpstat (x, 1, single (0)), single (y), eps("single"))
***** assert (gpstat (single ([x, NaN]), 1, 0), single ([y, NaN]), eps("single"))
***** assert (gpstat ([x, NaN], single (1), 0), single ([y, NaN]), eps("single"))
***** assert (gpstat ([x, NaN], 1, single (0)), single ([y, NaN]), eps("single"))
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_stat/burrstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/burrstat.m
***** error<burrstat: function called with too few input arguments.> burrstat ()
***** error<burrstat: function called with too few input arguments.> burrstat (1)
***** error<burrstat: function called with too few input arguments.> burrstat (1, 2)
***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat ({}, 2, 3)
***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, "", 3)
***** error<burrstat: LAMBDA, C, and K must be numeric.> burrstat (1, 2, "")
***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (i, 2, 3)
***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, i, 3)
***** error<burrstat: LAMBDA, C, and K must not be complex.> burrstat (1, 2, i)
***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
 burrstat (ones (3), ones (2), 3)
***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
 burrstat (ones (2), 2, ones (3))
***** error<burrstat: LAMBDA, C, and K must be of common size or scalars.> ...
 burrstat (1, ones (2), ones (3))
***** test
 [m, v] = burrstat (1, 2, 5);
 assert (m, 0.4295, 1e-4);
 assert (v, 0.0655, 1e-4);
***** test
 [m, v] = burrstat (1, 1, 1);
 assert (m, Inf);
 assert (v, Inf);
***** test
 [m, v] = burrstat (2, 4, 1);
 assert (m, 2.2214, 1e-4);
 assert (v, 1.3484, 1e-4);
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_stat/normstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/normstat.m
***** error<normstat: function called with too few input arguments.> normstat ()
***** error<normstat: function called with too few input arguments.> normstat (1)
***** error<normstat: MU and SIGMA must be numeric.> normstat ({}, 2)
***** error<normstat: MU and SIGMA must be numeric.> normstat (1, "")
***** error<normstat: MU and SIGMA must not be complex.> normstat (i, 2)
***** error<normstat: MU and SIGMA must not be complex.> normstat (1, i)
***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
 normstat (ones (3), ones (2))
***** error<normstat: MU and SIGMA must be of common size or scalars.> ...
 normstat (ones (2), ones (3))
***** test
 mu = 1:6;
 sigma = 0.2:0.2:1.2;
 [m, v] = normstat (mu, sigma);
 expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
 assert (m, mu);
 assert (v, expected_v, 0.001);
***** test
 sigma = 0.2:0.2:1.2;
 [m, v] = normstat (0, sigma);
 expected_mn = [0, 0, 0, 0, 0, 0];
 expected_v = [0.0400, 0.1600, 0.3600, 0.6400, 1.0000, 1.4400];
 assert (m, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/bisastat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/bisastat.m
***** error<bisastat: function called with too few input arguments.> bisastat ()
***** error<bisastat: function called with too few input arguments.> bisastat (1)
***** error<bisastat: BETA and GAMMA must be numeric.> bisastat ({}, 2)
***** error<bisastat: BETA and GAMMA must be numeric.> bisastat (1, "")
***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (i, 2)
***** error<bisastat: BETA and GAMMA must not be complex.> bisastat (1, i)
***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
 bisastat (ones (3), ones (2))
***** error<bisastat: BETA and GAMMA must be of common size or scalars.> ...
 bisastat (ones (2), ones (3))
***** test
 beta = 1:6;
 gamma = 1:0.2:2;
 [m, v] = bisastat (beta, gamma);
 expected_m = [1.50, 3.44, 5.94,  9.12,  13.10, 18];
 expected_v = [2.25, 16.128, 60.858, 172.032, 409.050, 864];
 assert (m, expected_m, 1e-2);
 assert (v, expected_v, 1e-3);
***** test
 beta = 1:6;
 [m, v] = bisastat (beta, 1.5);
 expected_m = [2.125, 4.25, 6.375, 8.5, 10.625, 12.75];
 expected_v = [8.5781, 34.3125, 77.2031, 137.2500, 214.4531, 308.8125];
 assert (m, expected_m, 1e-3);
 assert (v, expected_v, 1e-4);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/loglstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/loglstat.m
***** error<loglstat: function called with too few input arguments.> loglstat ()
***** error<loglstat: function called with too few input arguments.> loglstat (1)
***** error<loglstat: MU and SIGMA must be numeric.> loglstat ({}, 2)
***** error<loglstat: MU and SIGMA must be numeric.> loglstat (1, "")
***** error<loglstat: MU and SIGMA must not be complex.> loglstat (i, 2)
***** error<loglstat: MU and SIGMA must not be complex.> loglstat (1, i)
***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
 loglstat (ones (3), ones (2))
***** error<loglstat: MU and SIGMA must be of common size or scalars.> ...
 loglstat (ones (2), ones (3))
***** test
 [m, v] = loglstat (0, 1);
 assert (m, Inf, 0.001);
 assert (v, Inf, 0.001);
***** test
 [m, v] = loglstat (0, 0.8);
 assert (m, 4.2758, 0.001);
 assert (v, Inf, 0.001);
***** test
 [m, v] = loglstat (0, 0.6);
 assert (m, 1.9820, 0.001);
 assert (v, Inf, 0.001);
***** test
 [m, v] = loglstat (0, 0.4);
 assert (m, 1.3213, 0.001);
 assert (v, 2.5300, 0.001);
***** test
 [m, v] = loglstat (0, 0.2);
 assert (m, 1.0690, 0.001);
 assert (v, 0.1786, 0.001);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_stat/ricestat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/ricestat.m
***** error<ricestat: function called with too few input arguments.> ricestat ()
***** error<ricestat: function called with too few input arguments.> ricestat (1)
***** error<ricestat: S and SIGMA must be numeric.> ricestat ({}, 2)
***** error<ricestat: S and SIGMA must be numeric.> ricestat (1, "")
***** error<ricestat: S and SIGMA must not be complex.> ricestat (i, 2)
***** error<ricestat: S and SIGMA must not be complex.> ricestat (1, i)
***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
 ricestat (ones (3), ones (2))
***** error<ricestat: S and SIGMA must be of common size or scalars.> ...
 ricestat (ones (2), ones (3))
***** shared s, sigma
 s = [2, 0, -1, 1, 4];
 sigma = [1, NaN, 3, -1, 2];
***** assert (ricestat (s, sigma), [2.2724, NaN, NaN, NaN, 4.5448], 1e-4);
***** assert (ricestat ([s(1:2), s(4:5)], 1), [2.2724, 1.2533, 1.5486, 4.1272], 1e-4);
***** assert (ricestat ([s(1:2), s(4:5)], 3), [4.1665, 3.7599, 3.8637, 5.2695], 1e-4);
***** assert (ricestat ([s(1:2), s(4:5)], 2), [3.0971, 2.5066, 2.6609, 4.5448], 1e-4);
***** assert (ricestat (2, [sigma(1), sigma(3:5)]), [2.2724, 4.1665, NaN, 3.0971], 1e-4);
***** assert (ricestat (0, [sigma(1), sigma(3:5)]), [1.2533, 3.7599, NaN, 2.5066], 1e-4);
***** assert (ricestat (1, [sigma(1), sigma(3:5)]), [1.5486, 3.8637, NaN, 2.6609], 1e-4);
***** assert (ricestat (4, [sigma(1), sigma(3:5)]), [4.1272, 5.2695, NaN, 4.5448], 1e-4);
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_stat/tristat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/tristat.m
***** error<tristat: function called with too few input arguments.> tristat ()
***** error<tristat: function called with too few input arguments.> tristat (1)
***** error<tristat: function called with too few input arguments.> tristat (1, 2)
***** error<tristat: A, B, and C must be numeric.> tristat ("i", 2, 1)
***** error<tristat: A, B, and C must be numeric.> tristat (0, "d", 1)
***** error<tristat: A, B, and C must be numeric.> tristat (0, 3, {})
***** error<tristat: A, B, and C must be real.> tristat (i, 2, 1)
***** error<tristat: A, B, and C must be real.> tristat (0, i, 1)
***** error<tristat: A, B, and C must be real.> tristat (0, 3, i)
***** test
 a = 1:5;
 b = 3:7;
 c = 5:9;
 [m, v] = tristat (a, b, c);
 expected_m = [3, 4, 5, 6, 7];
 assert (m, expected_m);
 assert (v, ones (1, 5) * (2/3));
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/expstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/expstat.m
***** error<expstat: function called with too few input arguments.> expstat ()
***** error<expstat: MU must be numeric.> expstat ({})
***** error<expstat: MU must be numeric.> expstat ("")
***** error<expstat: MU must not be complex.> expstat (i)
***** test
 mu = 1:6;
 [m, v] = expstat (mu);
 assert (m, [1, 2, 3, 4, 5, 6], 0.001);
 assert (v, [1, 4, 9, 16, 25, 36], 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/plstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/plstat.m
***** shared x, Fx
 x = [0, 1, 3, 4, 7, 10];
 Fx = [0, 0.2, 0.5, 0.6, 0.7, 1];
***** assert (plstat (x, Fx), 4.15)
***** test
 [m, v] = plstat (x, Fx);
 assert (v, 10.3775, 1e-14)
***** error<plstat: function called with too few input arguments.> plstat ()
***** error<plstat: function called with too few input arguments.> plstat (1)
***** error<plstat: X and FX must be vectors of equal size.> ...
 plstat ([0, 1, 2], [0, 1])
***** error<plstat: X and FX must be at least two-elements long.> ...
 plstat ([0], [1])
***** error<plstat: FX must be bounded in the range> ...
 plstat ([0, 1, 2], [0, 1, 1.5])
***** error<plstat: FX must be bounded in the range> ...
 plstat ([0, 1, 2], [0, i, 1])
***** error<plstat: X and FX must not be complex.> ...
 plstat ([0, i, 2], [0, 0.5, 1])
***** error<plstat: X and FX must not be complex.> ...
 plstat ([0, i, 2], [0, 0.5i, 1])
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/raylstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/raylstat.m
***** error<raylstat: function called with too few input arguments.> raylstat ()
***** error<raylstat: SIGMA must be numeric.> raylstat ({})
***** error<raylstat: SIGMA must be numeric.> raylstat ("")
***** error<raylstat: SIGMA must not be complex.> raylstat (i)
***** test
 sigma = 1:6;
 [m, v] = raylstat (sigma);
 expected_m = [1.2533, 2.5066, 3.7599, 5.0133, 6.2666, 7.5199];
 expected_v = [0.4292, 1.7168, 3.8628, 6.8673, 10.7301, 15.4513];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/hygestat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/hygestat.m
***** error<hygestat: function called with too few input arguments.> hygestat ()
***** error<hygestat: function called with too few input arguments.> hygestat (1)
***** error<hygestat: function called with too few input arguments.> hygestat (1, 2)
***** error<hygestat: M, K, and N must be numeric.> hygestat ({}, 2, 3)
***** error<hygestat: M, K, and N must be numeric.> hygestat (1, "", 3)
***** error<hygestat: M, K, and N must be numeric.> hygestat (1, 2, "")
***** error<hygestat: M, K, and N must not be complex.> hygestat (i, 2, 3)
***** error<hygestat: M, K, and N must not be complex.> hygestat (1, i, 3)
***** error<hygestat: M, K, and N must not be complex.> hygestat (1, 2, i)
***** error<hygestat: M, K, and N must be of common size or scalars.> ...
 hygestat (ones (3), ones (2), 3)
***** error<hygestat: M, K, and N must be of common size or scalars.> ...
 hygestat (ones (2), 2, ones (3))
***** error<hygestat: M, K, and N must be of common size or scalars.> ...
 hygestat (1, ones (2), ones (3))
***** test
 m = 4:9;
 k = 0:5;
 n = 1:6;
 [mn, v] = hygestat (m, k, n);
 expected_mn = [0.0000, 0.4000, 1.0000, 1.7143, 2.5000, 3.3333];
 expected_v = [0.0000, 0.2400, 0.4000, 0.4898, 0.5357, 0.5556];
 assert (mn, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
***** test
 m = 4:9;
 k = 0:5;
 [mn, v] = hygestat (m, k, 2);
 expected_mn = [0.0000, 0.4000, 0.6667, 0.8571, 1.0000, 1.1111];
 expected_v = [0.0000, 0.2400, 0.3556, 0.4082, 0.4286, 0.4321];
 assert (mn, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_stat/fstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/fstat.m
***** error<fstat: function called with too few input arguments.> fstat ()
***** error<fstat: function called with too few input arguments.> fstat (1)
***** error<fstat: DF1 and DF2 must be numeric.> fstat ({}, 2)
***** error<fstat: DF1 and DF2 must be numeric.> fstat (1, "")
***** error<fstat: DF1 and DF2 must not be complex.> fstat (i, 2)
***** error<fstat: DF1 and DF2 must not be complex.> fstat (1, i)
***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
 fstat (ones (3), ones (2))
***** error<fstat: DF1 and DF2 must be of common size or scalars.> ...
 fstat (ones (2), ones (3))
***** test
 df1 = 1:6;
 df2 = 5:10;
 [m, v] = fstat (df1, df2);
 expected_mn = [1.6667, 1.5000, 1.4000, 1.3333, 1.2857, 1.2500];
 expected_v = [22.2222, 6.7500, 3.4844, 2.2222, 1.5869, 1.2153];
 assert (m, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
***** test
 df1 = 1:6;
 [m, v] = fstat (df1, 5);
 expected_mn = [1.6667, 1.6667, 1.6667, 1.6667, 1.6667, 1.6667];
 expected_v = [22.2222, 13.8889, 11.1111, 9.7222, 8.8889, 8.3333];
 assert (m, expected_mn, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/hnstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/hnstat.m
***** error<hnstat: function called with too few input arguments.> hnstat ()
***** error<hnstat: function called with too few input arguments.> hnstat (1)
***** error<hnstat: MU and SIGMA must be numeric.> hnstat ({}, 2)
***** error<hnstat: MU and SIGMA must be numeric.> hnstat (1, "")
***** error<hnstat: MU and SIGMA must not be complex.> hnstat (i, 2)
***** error<hnstat: MU and SIGMA must not be complex.> hnstat (1, i)
***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
 hnstat (ones (3), ones (2))
***** error<hnstat: MU and SIGMA must be of common size or scalars.> ...
 hnstat (ones (2), ones (3))
***** test
 [m, v] = hnstat (0, 1);
 assert (m, 0.7979, 1e-4);
 assert (v, 0.3634, 1e-4);
***** test
 [m, v] = hnstat (2, 1);
 assert (m, 2.7979, 1e-4);
 assert (v, 0.3634, 1e-4);
***** test
 [m, v] = hnstat (2, 2);
 assert (m, 3.5958, 1e-4);
 assert (v, 1.4535, 1e-4);
***** test
 [m, v] = hnstat (2, 2.5);
 assert (m, 3.9947, 1e-4);
 assert (v, 2.2711, 1e-4);
***** test
 [m, v] = hnstat (1.5, 0.5);
 assert (m, 1.8989, 1e-4);
 assert (v, 0.0908, 1e-4);
***** test
 [m, v] = hnstat (-1.5, 0.5);
 assert (m, -1.1011, 1e-4);
 assert (v, 0.0908, 1e-4);
14 tests, 14 passed, 0 known failure, 0 skipped
[inst/dist_stat/evstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/evstat.m
***** error<evstat: function called with too few input arguments.> evstat ()
***** error<evstat: function called with too few input arguments.> evstat (1)
***** error<evstat: MU and SIGMA must be numeric.> evstat ({}, 2)
***** error<evstat: MU and SIGMA must be numeric.> evstat (1, "")
***** error<evstat: MU and SIGMA must not be complex.> evstat (i, 2)
***** error<evstat: MU and SIGMA must not be complex.> evstat (1, i)
***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
 evstat (ones (3), ones (2))
***** error<evstat: MU and SIGMA must be of common size or scalars.> ...
 evstat (ones (2), ones (3))
***** shared x, y0, y1
 x = [-5, 0, 1, 2, 3];
 y0 = [NaN, NaN, 0.4228, 0.8456, 1.2684];
 y1 = [-5.5772, -3.4633, -3.0405, -2.6177, -2.1949];
***** assert (evstat (x, x), y0, 1e-4)
***** assert (evstat (x, x+6), y1, 1e-4)
***** assert (evstat (x, x-6), NaN (1,5))
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_stat/unifstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/unifstat.m
***** error<unifstat: function called with too few input arguments.> unifstat ()
***** error<unifstat: function called with too few input arguments.> unifstat (1)
***** error<unifstat: A and B must be numeric.> unifstat ({}, 2)
***** error<unifstat: A and B must be numeric.> unifstat (1, "")
***** error<unifstat: A and B must not be complex.> unifstat (i, 2)
***** error<unifstat: A and B must not be complex.> unifstat (1, i)
***** error<unifstat: A and B must be of common size or scalars.> ...
 unifstat (ones (3), ones (2))
***** error<unifstat: A and B must be of common size or scalars.> ...
 unifstat (ones (2), ones (3))
***** test
 a = 1:6;
 b = 2:2:12;
 [m, v] = unifstat (a, b);
 expected_m = [1.5000, 3.0000, 4.5000, 6.0000, 7.5000, 9.0000];
 expected_v = [0.0833, 0.3333, 0.7500, 1.3333, 2.0833, 3.0000];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 a = 1:6;
 [m, v] = unifstat (a, 10);
 expected_m = [5.5000, 6.0000, 6.5000, 7.0000, 7.5000, 8.0000];
 expected_v = [6.7500, 5.3333, 4.0833, 3.0000, 2.0833, 1.3333];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/wblstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/wblstat.m
***** error<wblstat: function called with too few input arguments.> wblstat ()
***** error<wblstat: function called with too few input arguments.> wblstat (1)
***** error<wblstat: LAMBDA and K must be numeric.> wblstat ({}, 2)
***** error<wblstat: LAMBDA and K must be numeric.> wblstat (1, "")
***** error<wblstat: LAMBDA and K must not be complex.> wblstat (i, 2)
***** error<wblstat: LAMBDA and K must not be complex.> wblstat (1, i)
***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
 wblstat (ones (3), ones (2))
***** error<wblstat: LAMBDA and K must be of common size or scalars.> ...
 wblstat (ones (2), ones (3))
***** test
 lambda = 3:8;
 k = 1:6;
 [m, v] = wblstat (lambda, k);
 expected_m = [3.0000, 3.5449, 4.4649, 5.4384, 6.4272, 7.4218];
 expected_v = [9.0000, 3.4336, 2.6333, 2.3278, 2.1673, 2.0682];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 k = 1:6;
 [m, v] = wblstat (6, k);
 expected_m = [ 6.0000, 5.3174, 5.3579, 5.4384, 5.5090, 5.5663];
 expected_v = [36.0000, 7.7257, 3.7920, 2.3278, 1.5923, 1.1634];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/tstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/tstat.m
***** error<tstat: function called with too few input arguments.> tstat ()
***** error<tstat: DF must be numeric.> tstat ({})
***** error<tstat: DF must be numeric.> tstat ("")
***** error<tstat: DF must not be complex.> tstat (i)
***** test
 df = 3:8;
 [m, v] = tstat (df);
 expected_m = [0, 0, 0, 0, 0, 0];
 expected_v = [3.0000, 2.0000, 1.6667, 1.5000, 1.4000, 1.3333];
 assert (m, expected_m);
 assert (v, expected_v, 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/chi2stat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/chi2stat.m
***** error<chi2stat: function called with too few input arguments.> chi2stat ()
***** error<chi2stat: DF must be numeric.> chi2stat ({})
***** error<chi2stat: DF must be numeric.> chi2stat ("")
***** error<chi2stat: DF must not be complex.> chi2stat (i)
***** test
 df = 1:6;
 [m, v] = chi2stat (df);
 assert (m, df);
 assert (v, [2, 4, 6, 8, 10, 12], 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/tlsstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/tlsstat.m
***** error<tlsstat: function called with too few input arguments.> tlsstat ()
***** error<tlsstat: function called with too few input arguments.> tlsstat (1)
***** error<tlsstat: function called with too few input arguments.> tlsstat (1, 2)
***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat ({}, 2, 3)
***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, "", 3)
***** error<tlsstat: MU, SIGMA, and NU must be numeric.> tlsstat (1, 2, ["d"])
***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (i, 2, 3)
***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, i, 3)
***** error<tlsstat: MU, SIGMA, and NU must not be complex.> tlsstat (1, 2, i)
***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsstat (ones (3), ones (2), 1)
***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsstat (ones (2), 1, ones (3))
***** error<tlsstat: MU, SIGMA, and NU must be of common size or scalars.> ...
 tlsstat (1, ones (2), ones (3))
***** test
 [m, v] = tlsstat (0, 1, 0);
 assert (m, NaN);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (0, 1, 1);
 assert (m, NaN);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (2, 1, 1);
 assert (m, NaN);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (-2, 1, 1);
 assert (m, NaN);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (0, 1, 2);
 assert (m, 0);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (2, 1, 2);
 assert (m, 2);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (-2, 1, 2);
 assert (m, -2);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (0, 2, 2);
 assert (m, 0);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (2, 2, 2);
 assert (m, 2);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (-2, 2, 2);
 assert (m, -2);
 assert (v, NaN);
***** test
 [m, v] = tlsstat (0, 1, 3);
 assert (m, 0);
 assert (v, 3);
***** test
 [m, v] = tlsstat (0, 2, 3);
 assert (m, 0);
 assert (v, 6);
***** test
 [m, v] = tlsstat (2, 1, 3);
 assert (m, 2);
 assert (v, 3);
***** test
 [m, v] = tlsstat (2, 2, 3);
 assert (m, 2);
 assert (v, 6);
***** test
 [m, v] = tlsstat (-2, 1, 3);
 assert (m, -2);
 assert (v, 3);
***** test
 [m, v] = tlsstat (-2, 2, 3);
 assert (m, -2);
 assert (v, 6);
28 tests, 28 passed, 0 known failure, 0 skipped
[inst/dist_stat/gevstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/gevstat.m
***** error<gevstat: function called with too few input arguments.> gevstat ()
***** error<gevstat: function called with too few input arguments.> gevstat (1)
***** error<gevstat: function called with too few input arguments.> gevstat (1, 2)
***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat ({}, 2, 3)
***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, "", 3)
***** error<gevstat: K, SIGMA, and MU must be numeric.> gevstat (1, 2, "")
***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (i, 2, 3)
***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, i, 3)
***** error<gevstat: K, SIGMA, and MU must not be complex.> gevstat (1, 2, i)
***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gevstat (ones (3), ones (2), 3)
***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gevstat (ones (2), 2, ones (3))
***** error<gevstat: K, SIGMA, and MU must be of common size or scalars.> ...
 gevstat (1, ones (2), ones (3))
***** test
 k = [-1, -0.5, 0, 0.2, 0.4, 0.5, 1];
 sigma = 2;
 mu = 1;
 [m, v] = gevstat (k, sigma, mu);
 expected_m = [1, 1.4551, 2.1544, 2.6423, 3.4460, 4.0898, Inf];
 expected_v = [4, 3.4336, 6.5797, 13.3761, 59.3288, Inf, Inf];
 assert (m, expected_m, -0.001);
 assert (v, expected_v, -0.001);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_stat/binostat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/binostat.m
***** error<binostat: function called with too few input arguments.> binostat ()
***** error<binostat: function called with too few input arguments.> binostat (1)
***** error<binostat: N and PS must be numeric.> binostat ({}, 2)
***** error<binostat: N and PS must be numeric.> binostat (1, "")
***** error<binostat: N and PS must not be complex.> binostat (i, 2)
***** error<binostat: N and PS must not be complex.> binostat (1, i)
***** error<binostat: N and PS must be of common size or scalars.> ...
 binostat (ones (3), ones (2))
***** error<binostat: N and PS must be of common size or scalars.> ...
 binostat (ones (2), ones (3))
***** test
 n = 1:6;
 ps = 0:0.2:1;
 [m, v] = binostat (n, ps);
 expected_m = [0.00, 0.40, 1.20, 2.40, 4.00, 6.00];
 expected_v = [0.00, 0.32, 0.72, 0.96, 0.80, 0.00];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 n = 1:6;
 [m, v] = binostat (n, 0.5);
 expected_m = [0.50, 1.00, 1.50, 2.00, 2.50, 3.00];
 expected_v = [0.25, 0.50, 0.75, 1.00, 1.25, 1.50];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 n = [-Inf -3 5 0.5 3 NaN 100, Inf];
 [m, v] = binostat (n, 0.5);
 assert (isnan (m), [true true false true false true false false])
 assert (isnan (v), [true true false true false true false false])
 assert (m(end), Inf);
 assert (v(end), Inf);
***** assert (nthargout (1:2, @binostat, 5, []), {[], []})
***** assert (nthargout (1:2, @binostat, [], 5), {[], []})
***** assert (size (binostat (randi (100, 10, 5, 4), rand (10, 5, 4))), [10 5 4])
***** assert (size (binostat (randi (100, 10, 5, 4), 7)), [10 5 4])
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_stat/unidstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/unidstat.m
***** error<unidstat: function called with too few input arguments.> unidstat ()
***** error<unidstat: N must be numeric.> unidstat ({})
***** error<unidstat: N must be numeric.> unidstat ("")
***** error<unidstat: N must not be complex.> unidstat (i)
***** test
 N = 1:6;
 [m, v] = unidstat (N);
 expected_m = [1.0000, 1.5000, 2.0000, 2.5000, 3.0000, 3.5000];
 expected_v = [0.0000, 0.2500, 0.6667, 1.2500, 2.0000, 2.9167];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/logistat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/logistat.m
***** error<logistat: function called with too few input arguments.> logistat ()
***** error<logistat: function called with too few input arguments.> logistat (1)
***** error<logistat: MU and SIGMA must be numeric.> logistat ({}, 2)
***** error<logistat: MU and SIGMA must be numeric.> logistat (1, "")
***** error<logistat: MU and SIGMA must not be complex.> logistat (i, 2)
***** error<logistat: MU and SIGMA must not be complex.> logistat (1, i)
***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
 logistat (ones (3), ones (2))
***** error<logistat: MU and SIGMA must be of common size or scalars.> ...
 logistat (ones (2), ones (3))
***** test
 [m, v] = logistat (0, 1);
 assert (m, 0);
 assert (v, 3.2899, 0.001);
***** test
 [m, v] = logistat (0, 0.8);
 assert (m, 0);
 assert (v, 2.1055, 0.001);
***** test
 [m, v] = logistat (1, 0.6);
 assert (m, 1);
 assert (v, 1.1844, 0.001);
***** test
 [m, v] = logistat (0, 0.4);
 assert (m, 0);
 assert (v, 0.5264, 0.001);
***** test
 [m, v] = logistat (-1, 0.2);
 assert (m, -1);
 assert (v, 0.1316, 0.001);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_stat/geostat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/geostat.m
***** error<geostat: function called with too few input arguments.> geostat ()
***** error<geostat: PS must be numeric.> geostat ({})
***** error<geostat: PS must be numeric.> geostat ("")
***** error<geostat: PS must not be complex.> geostat (i)
***** test
 ps = 1 ./ (1:6);
 [m, v] = geostat (ps);
 assert (m, [0, 1, 2, 3, 4, 5], 0.001);
 assert (v, [0, 2, 6, 12, 20, 30], 0.001);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/dist_stat/betastat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/betastat.m
***** error<betastat: function called with too few input arguments.> betastat ()
***** error<betastat: function called with too few input arguments.> betastat (1)
***** error<betastat: A and B must be numeric.> betastat ({}, 2)
***** error<betastat: A and B must be numeric.> betastat (1, "")
***** error<betastat: A and B must not be complex.> betastat (i, 2)
***** error<betastat: A and B must not be complex.> betastat (1, i)
***** error<betastat: A and B must be of common size or scalars.> ...
 betastat (ones (3), ones (2))
***** error<betastat: A and B must be of common size or scalars.> ...
 betastat (ones (2), ones (3))
***** test
 a = -2:6;
 b = 0.4:0.2:2;
 [m, v] = betastat (a, b);
 expected_m = [NaN NaN NaN 1/2 2/3.2 3/4.4 4/5.6 5/6.8 6/8];
 expected_v = [NaN NaN NaN 0.0833, 0.0558, 0.0402, 0.0309, 0.0250, 0.0208];
 assert (m, expected_m, eps*100);
 assert (v, expected_v, 0.001);
***** test
 a = -2:1:6;
 [m, v] = betastat (a, 1.5);
 expected_m = [NaN NaN NaN 1/2.5 2/3.5 3/4.5 4/5.5 5/6.5 6/7.5];
 expected_v = [NaN NaN NaN 0.0686, 0.0544, 0.0404, 0.0305, 0.0237, 0.0188];
 assert (m, expected_m);
 assert (v, expected_v, 0.001);
***** test
 a = [14  Inf   10  NaN  10];
 b = [12    9  NaN  Inf  12];
 [m, v] = betastat (a, b);
 expected_m = [14/26 NaN NaN NaN 10/22];
 expected_v = [168/18252 NaN NaN NaN 120/11132];
 assert (m, expected_m);
 assert (v, expected_v);
***** assert (nthargout (1:2, @betastat, 5, []), {[], []})
***** assert (nthargout (1:2, @betastat, [], 5), {[], []})
***** assert (size (betastat (rand (10, 5, 4), rand (10, 5, 4))), [10 5 4])
***** assert (size (betastat (rand (10, 5, 4), 7)), [10 5 4])
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_stat/lognstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/lognstat.m
***** error<lognstat: function called with too few input arguments.> lognstat ()
***** error<lognstat: function called with too few input arguments.> lognstat (1)
***** error<lognstat: MU and SIGMA must be numeric.> lognstat ({}, 2)
***** error<lognstat: MU and SIGMA must be numeric.> lognstat (1, "")
***** error<lognstat: MU and SIGMA must not be complex.> lognstat (i, 2)
***** error<lognstat: MU and SIGMA must not be complex.> lognstat (1, i)
***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
 lognstat (ones (3), ones (2))
***** error<lognstat: MU and SIGMA must be of common size or scalars.> ...
 lognstat (ones (2), ones (3))
***** test
 mu = 0:0.2:1;
 sigma = 0.2:0.2:1.2;
 [m, v] = lognstat (mu, sigma);
 expected_m = [1.0202, 1.3231, 1.7860, 2.5093,  3.6693,   5.5845];
 expected_v = [0.0425, 0.3038, 1.3823, 5.6447, 23.1345, 100.4437];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 sigma = 0.2:0.2:1.2;
 [m, v] = lognstat (0, sigma);
 expected_m = [1.0202, 1.0833, 1.1972, 1.3771, 1.6487,  2.0544];
 expected_v = [0.0425, 0.2036, 0.6211, 1.7002, 4.6708, 13.5936];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/invgstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/invgstat.m
***** error<invgstat: function called with too few input arguments.> invgstat ()
***** error<invgstat: function called with too few input arguments.> invgstat (1)
***** error<invgstat: MU and LAMBDA must be numeric.> invgstat ({}, 2)
***** error<invgstat: MU and LAMBDA must be numeric.> invgstat (1, "")
***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (i, 2)
***** error<invgstat: MU and LAMBDA must not be complex.> invgstat (1, i)
***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
 invgstat (ones (3), ones (2))
***** error<invgstat: MU and LAMBDA must be of common size or scalars.> ...
 invgstat (ones (2), ones (3))
***** test
 [m, v] = invgstat (1, 1);
 assert (m, 1);
 assert (v, 1);
***** test
 [m, v] = invgstat (2, 1);
 assert (m, 2);
 assert (v, 8);
***** test
 [m, v] = invgstat (2, 2);
 assert (m, 2);
 assert (v, 4);
***** test
 [m, v] = invgstat (2, 2.5);
 assert (m, 2);
 assert (v, 3.2);
***** test
 [m, v] = invgstat (1.5, 0.5);
 assert (m, 1.5);
 assert (v, 6.75);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_stat/nakastat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/nakastat.m
***** error<nakastat: function called with too few input arguments.> nakastat ()
***** error<nakastat: function called with too few input arguments.> nakastat (1)
***** error<nakastat: MU and OMEGA must be numeric.> nakastat ({}, 2)
***** error<nakastat: MU and OMEGA must be numeric.> nakastat (1, "")
***** error<nakastat: MU and OMEGA must not be complex.> nakastat (i, 2)
***** error<nakastat: MU and OMEGA must not be complex.> nakastat (1, i)
***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
 nakastat (ones (3), ones (2))
***** error<nakastat: MU and OMEGA must be of common size or scalars.> ...
 nakastat (ones (2), ones (3))
***** test
 [m, v] = nakastat (1, 1);
 assert (m, 0.8862269254, 1e-10);
 assert (v, 0.2146018366, 1e-10);
***** test
 [m, v] = nakastat (1, 2);
 assert (m, 1.25331413731, 1e-10);
 assert (v, 0.42920367321, 1e-10);
***** test
 [m, v] = nakastat (2, 1);
 assert (m, 0.93998560299, 1e-10);
 assert (v, 0.11642706618, 1e-10);
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_stat/ncfstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/ncfstat.m
***** error<ncfstat: function called with too few input arguments.> ncfstat ()
***** error<ncfstat: function called with too few input arguments.> ncfstat (1)
***** error<ncfstat: function called with too few input arguments.> ncfstat (1, 2)
***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat ({}, 2, 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, "", 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must be numeric.> ncfstat (1, 2, "")
***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (i, 2, 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, i, 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must not be complex.> ncfstat (1, 2, i)
***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfstat (ones (3), ones (2), 3)
***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfstat (ones (2), 2, ones (3))
***** error<ncfstat: DF1, DF2, and LAMBDA must be of common size or scalars.> ...
 ncfstat (1, ones (2), ones (3))
***** shared df1, df2, lambda
 df1 = [2, 0, -1, 1, 4, 5];
 df2 = [2, 4, -1, 5, 6, 7];
 lambda = [1, NaN, 3, 0, 2, -1];
***** assert (ncfstat (df1, df2, lambda), [NaN, NaN, NaN, 1.6667, 2.25, 1.12], 1e-4);
***** assert (ncfstat (df1(4:6), df2(4:6), 1), [3.3333, 1.8750, 1.6800], 1e-4);
***** assert (ncfstat (df1(4:6), df2(4:6), 2), [5.0000, 2.2500, 1.9600], 1e-4);
***** assert (ncfstat (df1(4:6), df2(4:6), 3), [6.6667, 2.6250, 2.2400], 1e-4);
***** assert (ncfstat (2, [df2(1), df2(4:6)], 5), [NaN,5.8333,5.2500,4.9000], 1e-4);
***** assert (ncfstat (0, [df2(1), df2(4:6)], 5), [NaN, Inf, Inf, Inf]);
***** assert (ncfstat (1, [df2(1), df2(4:6)], 5), [NaN, 10, 9, 8.4], 1e-14);
***** assert (ncfstat (4, [df2(1), df2(4:6)], 5), [NaN, 3.75, 3.375, 3.15], 1e-14);
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_stat/nbinstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/nbinstat.m
***** error<nbinstat: function called with too few input arguments.> nbinstat ()
***** error<nbinstat: function called with too few input arguments.> nbinstat (1)
***** error<nbinstat: R and PS must be numeric.> nbinstat ({}, 2)
***** error<nbinstat: R and PS must be numeric.> nbinstat (1, "")
***** error<nbinstat: R and PS must not be complex.> nbinstat (i, 2)
***** error<nbinstat: R and PS must not be complex.> nbinstat (1, i)
***** error<nbinstat: R and PS must be of common size or scalars.> ...
 nbinstat (ones (3), ones (2))
***** error<nbinstat: R and PS must be of common size or scalars.> ...
 nbinstat (ones (2), ones (3))
***** test
 r = 1:4;
 ps = 0.2:0.2:0.8;
 [m, v] = nbinstat (r, ps);
 expected_m = [ 4.0000, 3.0000, 2.0000, 1.0000];
 expected_v = [20.0000, 7.5000, 3.3333, 1.2500];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
***** test
 r = 1:4;
 [m, v] = nbinstat (r, 0.5);
 expected_m = [1, 2, 3, 4];
 expected_v = [2, 4, 6, 8];
 assert (m, expected_m, 0.001);
 assert (v, expected_v, 0.001);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_stat/nctstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/nctstat.m
***** error<nctstat: function called with too few input arguments.> nctstat ()
***** error<nctstat: function called with too few input arguments.> nctstat (1)
***** error<nctstat: DF and MU must be numeric.> nctstat ({}, 2)
***** error<nctstat: DF and MU must be numeric.> nctstat (1, "")
***** error<nctstat: DF and MU must not be complex.> nctstat (i, 2)
***** error<nctstat: DF and MU must not be complex.> nctstat (1, i)
***** error<nctstat: DF and MU must be of common size or scalars.> ...
 nctstat (ones (3), ones (2))
***** error<nctstat: DF and MU must be of common size or scalars.> ...
 nctstat (ones (2), ones (3))
***** shared df, mu
 df = [2, 0, -1, 1, 4];
 mu = [1, NaN, 3, -1, 2];
***** assert (nctstat (df, mu), [1.7725, NaN, NaN, NaN, 2.5066], 1e-4);
***** assert (nctstat ([df(1:2), df(4:5)], 1), [1.7725, NaN, NaN, 1.2533], 1e-4);
***** assert (nctstat ([df(1:2), df(4:5)], 3), [5.3174, NaN, NaN, 3.7599], 1e-4);
***** assert (nctstat ([df(1:2), df(4:5)], 2), [3.5449, NaN, NaN, 2.5066], 1e-4);
***** assert (nctstat (2, [mu(1), mu(3:5)]), [1.7725,5.3174,-1.7725,3.5449], 1e-4);
***** assert (nctstat (0, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
***** assert (nctstat (1, [mu(1), mu(3:5)]), [NaN, NaN, NaN, NaN]);
***** assert (nctstat (4, [mu(1), mu(3:5)]), [1.2533,3.7599,-1.2533,2.5066], 1e-4);
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_stat/poisstat.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_stat/poisstat.m
***** error<poisstat: function called with too few input arguments.> poisstat ()
***** error<poisstat: SIGMA must be numeric.> poisstat ({})
***** error<poisstat: SIGMA must be numeric.> poisstat ("")
***** error<poisstat: SIGMA must not be complex.> poisstat (i)
***** test
 lambda = 1 ./ (1:6);
 [m, v] = poisstat (lambda);
 assert (m, lambda);
 assert (v, lambda);
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/ranksum.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/ranksum.m
***** test
 mileage = [33.3, 34.5, 37.4; 33.4, 34.8, 36.8; ...
            32.9, 33.8, 37.6; 32.6, 33.4, 36.6; ...
            32.5, 33.7, 37.0; 33.0, 33.9, 36.7];
 [p,h,stats] = ranksum(mileage(:,1),mileage(:,2));
 assert (p, 0.004329004329004329, 1e-14);
 assert (h, true);
 assert (stats.ranksum, 21.5);
***** test
 year1 = [51 52 62 62 52 52 51 53 59 63 59 56 63 74 68 86 82 70 69 75 73 ...
          49 47 50 60 59 60 62 61 71]';
 year2 = [54 53 64 66 57 53 54 54 62 66 59 59 67 76 75 86 82 67 74 80 75 ...
          54 50 53 62 62 62 72 60 67]';
 [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left");
 assert (p, 0.1270832752950605, 1e-14);
 assert (h, false);
 assert (stats.ranksum, 837.5);
 assert (stats.zval, -1.140287483634606, 1e-14);
 [p,h,stats] = ranksum(year1, year2, "alpha", 0.01, "tail", "left", ...
                       "method", "exact");
 assert (p, 0.127343916432862, 1e-14);
 assert (h, false);
 assert (stats.ranksum, 837.5);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/ridge.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/ridge.m
***** demo
 ## Perform ridge regression for a range of ridge parameters and observe
 ## how the coefficient estimates change based on the acetylene dataset.

 load acetylene

 X = [x1, x2, x3];

 x1x2 = x1 .* x2;
 x1x3 = x1 .* x3;
 x2x3 = x2 .* x3;

 D = [x1, x2, x3, x1x2, x1x3, x2x3];

 k = 0:1e-5:5e-3;

 b = ridge (y, D, k);

 figure
 plot (k, b, "LineWidth", 2)
 ylim ([-100, 100])
 grid on
 xlabel ("Ridge Parameter")
 ylabel ("Standardized Coefficient")
 title ("Ridge Trace")
 legend ("x1", "x2", "x3", "x1x2", "x1x3", "x2x3")

***** demo

 load carbig
 X = [Acceleration Weight Displacement Horsepower];
 y = MPG;

 n = length(y);

 rand("seed",1); % For reproducibility

 c = cvpartition(n,'HoldOut',0.3);
 idxTrain = training(c,1);
 idxTest = ~idxTrain;

 idxTrain = training(c,1);
 idxTest = ~idxTrain;

 k = 5;
 b = ridge(y(idxTrain),X(idxTrain,:),k,0);

 % Predict MPG values for the test data using the model.
 yhat = b(1) + X(idxTest,:)*b(2:end);
 scatter(y(idxTest),yhat)

 hold on
 plot(y(idxTest),y(idxTest),"r")
 xlabel('Actual MPG')
 ylabel('Predicted MPG')
 hold off

***** test
 b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 1);
 assert (b, [0.5533; 0.5533], 1e-4);
***** test
 b = ridge ([1 2 3 4]', [1 2 3 4; 2 3 4 5]', 2);
 assert (b, [0.4841; 0.4841], 1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0);
 assert (b,[10.2273;1.97128;-0.601818],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.0005);
 assert (b,[10.2233;1.9712;-0.6056],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.001);
 assert (b,[10.2194;1.9711;-0.6094],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.002);
 assert (b,[10.2116;1.9709;-0.6169],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.005);
 assert (b,[10.1882;1.9704;-0.6393],1e-4);
***** test
 load acetylene
 x = [x1, x2, x3];
 b = ridge (y, x, 0.01);
 assert (b,[10.1497;1.9695;-0.6761],1e-4);
***** error<ridge: function called with too few input arguments.> ridge (1)
***** error<ridge: function called with too few input arguments.> ridge (1, 2)
***** error<ridge: Y must be a numeric column vector.> ridge (ones (3), ones (3), 2)
***** error<ridge: Y must be a numeric column vector.> ridge ([1, 2], ones (2), 2)
***** error<ridge: Y must be a numeric column vector.> ridge ([], ones (3), 2)
***** error<ridge: X must be a numeric matrix.> ridge (ones (5,1), [], 2)
***** error<ridge: Y and X must contain the same number of rows.> ...
 ridge ([1; 2; 3; 4; 5], ones (3), 3)
***** error<ridge: wrong value for SCALED argument.> ...
 ridge ([1; 2; 3], ones (3), 3, 2)
***** error<ridge: wrong value for SCALED argument.> ...
 ridge ([1; 2; 3], ones (3), 3, "some")
17 tests, 17 passed, 0 known failure, 0 skipped
[inst/clusterdata.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/clusterdata.m
***** demo
 randn ("seed", 1)  # for reproducibility
 r1 = randn (10, 2) * 0.25 + 1;
 randn ("seed", 5)  # for reproducibility
 r2 = randn (20, 2) * 0.5 - 1;
 X = [r1; r2];

 wnl = warning ("off", "Octave:linkage_savemem", "local");
 T = clusterdata (X, "linkage", "ward", "MaxClust", 2);
 scatter (X(:,1), X(:,2), 36, T, "filled");
***** error<clusterdata: function called with too few input arguments.> ...
 clusterdata ()
***** error<clusterdata: function called with too few input arguments.> ...
 clusterdata (1)
***** error <unknown property .*> clusterdata ([1 1], "Bogus", 1)
***** error <specify .* 'MaxClust' or 'Cutoff' .*> clusterdata ([1 1], "Depth", 1)
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/fishertest.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/fishertest.m
***** demo
 ## A Fisher's exact test example

 x = [3, 1; 1, 3]
 [h, p, stats] = fishertest(x)
***** assert (fishertest ([3, 4; 5, 7]), false);
***** assert (isa (fishertest ([3, 4; 5, 7]), "logical"), true);
***** test
 [h, pval, stats] = fishertest ([3, 4; 5, 7]);
 assert (pval, 1, 1e-14);
 assert (stats.OddsRatio, 1.05);
 CI = [0.159222057151289, 6.92429189601808];
 assert (stats.ConfidenceInterval, CI, 1e-14)
***** test
 [h, pval, stats] = fishertest ([3, 4; 5, 0]);
 assert (pval, 0.08080808080808080, 1e-14);
 assert (stats.OddsRatio, 0);
 assert (stats.ConfidenceInterval, [-Inf, Inf])
***** error fishertest ();
***** error fishertest (1, 2, 3, 4, 5, 6);
***** error<fishertest: X must be a 2-dimensional matrix.> ...
 fishertest (ones (2, 2, 2));
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([1, 2; -3, 4]);
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([1, 2; 3, 4+i]);
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([1, 2; 3, 4.2]);
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([NaN, 2; 3, 4]);
***** error<fishertest: X must contain only non-negative real integers.> ...
 fishertest ([1, Inf; 3, 4]);
***** error<fishertest: cannot handle large entries.> ...
 fishertest (ones (2) * 1e8);
***** error<fishertest: invalid value for alpha.> ...
 fishertest ([1, 2; 3, 4], "alpha", 0);
***** error<fishertest: invalid value for alpha.> ...
 fishertest ([1, 2; 3, 4], "alpha", 1.2);
***** error<fishertest: invalid value for alpha.> ...
 fishertest ([1, 2; 3, 4], "alpha", "val");
***** error<fishertest: invalid value for tail.>  ...
 fishertest ([1, 2; 3, 4], "tail", "val");
***** error<fishertest: invalid value for tail.>  ...
 fishertest ([1, 2; 3, 4], "alpha", 0.01, "tail", "val");
***** error<fishertest: invalid name for optional arguments.> ...
 fishertest ([1, 2; 3, 4], "alpha", 0.01, "badoption", 3);
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/tabulate.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/tabulate.m
***** demo
 ## Generate a frequency table for a vector of data in a cell array
 load patients

 ## Display the first seven entries of the Gender variable
 gender = Gender(1:7)

 ## Compute the frequency table that shows the number and
 ## percentage of Male and Female patients
 tabulate (Gender)
***** demo
 ## Create a frequency table for a vector of positive integers
 load patients

 ## Display the first seven entries of the Gender variable
 height = Height(1:7)

 ## Create a frequency table that shows, in its second and third columns,
 ## the number and percentage of patients with a particular height.
 table = tabulate (Height);

 ## Display the first and last seven entries of the frequency table
 first = table(1:7,:)

 last = table(end-6:end,:)
***** demo
 ## Create a frequency table from a character array
 load carsmall

 ## Tabulate the data in the Origin variable, which shows the
 ## country of origin of each car in the data set
 tabulate (Origin)
***** demo
 ## Create a frequency table from a numeric vector with NaN values
 load carsmall

 ## The carsmall dataset contains measurements of 100 cars
 total_cars = length (MPG)
 ## For six cars, the MPG value is missing
 missingMPG = length (MPG(isnan (MPG)))

 ## Create a frequency table using MPG
 tabulate (MPG)
 table = tabulate (MPG);

 ## Only 94 cars were used
 valid_cars = sum (table(:,2))
***** test
 load patients
 table = tabulate (Gender);
 assert (table{1,1}, "Male");
 assert (table{2,1}, "Female");
 assert (table{1,2}, 47);
 assert (table{2,2}, 53);
***** test
 load patients
 table = tabulate (Height);
 assert (table(end-4,:), [68, 15, 15]);
 assert (table(end-3,:), [69, 8, 8]);
 assert (table(end-2,:), [70, 11, 11]);
 assert (table(end-1,:), [71, 10, 10]);
 assert (table(end,:), [72, 4, 4]);
***** test
 ## Test numeric vector including NaNs
 x = [1; 1; 2; 3; 1; NaN; 2];
 tbl = tabulate (x);
 assert (isnumeric (tbl));
 assert (size (tbl), [3, 3]);
 assert (tbl(:,1), [1; 2; 3]);
 assert (tbl(:,2), [3; 2; 1]);
 assert (tbl(:,3), [50; 33.3333; 16.6667], 3e-4);
***** test
 ## Test positive integers with gaps
 x = [1; 3; 3];
 tbl = tabulate (x);
 assert (isnumeric (tbl));
 assert (size (tbl), [3, 3]);
 assert (tbl(:,1), [1; 2; 3]);
 assert (tbl(:,2), [1; 0; 2]);
 assert (tbl(:,3), [33.3333; 0; 66.6667], 3e-4);
***** test
 ## Test logical inputs (should return cell array with '0'/'1')
 x = [true; false; true; true];
 tbl = tabulate (x);
 assert (iscell (tbl));
 assert (size (tbl), [2, 3]);
 assert (tbl(:,1), {'0'; '1'});
 assert ([tbl{:,2}]', [1; 3]);
 assert ([tbl{:,3}]', [25; 75]);
***** test
 ## Test character array
 x = ['a'; 'b'; 'a'];
 tbl = tabulate (x);
 assert (iscell (tbl));
 assert (size (tbl), [2, 3]);
 assert (tbl(:,1), {'a'; 'b'});
 assert ([tbl{:,2}]', [2; 1]);
***** test
 ## Test cell array of character vectors
 x = {'a', 'b', 'a'};
 tbl = tabulate (x);
 assert (iscell (tbl));
 assert (size (tbl), [2, 3]);
 assert (tbl(:,1), {'a'; 'b'});
 assert ([tbl{:,2}]', [2; 1]);
***** test
 ## Test string array with missing values
 x = string ({'a', 'b', 'a'});
 x(4) = missing;
 tbl = tabulate (x);
 assert (iscell (tbl));
 assert (size (tbl), [2, 3]);
 assert (tbl(:,1), {'a'; 'b'});
 assert ([tbl{:,2}]', [2; 1]);
***** test
 ## Test categorical array with undefined values and vacuous levels
 x = categorical ({'a', 'a', 'b'}, {'a', 'b', 'c'});
 tbl = tabulate (x);
 assert (iscell (tbl));
 assert (size (tbl), [3, 3]);
 assert (tbl(:,1), {'a'; 'b'; 'c'});
 assert ([tbl{:,2}]', [2; 1; 0]);
 assert ([tbl{:,3}]', [66.6667; 33.3333; 0], 1e-3);
***** test
 ## Test empty input
 tbl = tabulate ([]);
 assert (isempty (tbl));
***** test
 ## fisheriris (Categorical/CellStr)
 load fisheriris;
 tbl = tabulate (species);
 assert (size (tbl), [3, 3]);
 assert (tbl(:,1), {'setosa'; 'versicolor'; 'virginica'});
 assert ([tbl{:,2}]', [50; 50; 50]);
 assert ([tbl{:,3}]', [33.3333; 33.3333; 33.3333], 1e-4);
***** test
 ## carsmall (Char/CellStr)
 load carsmall;
 tbl = tabulate (Origin);
 origins = tbl(:,1);
 counts = [tbl{:,2}];
 assert (counts(strcmp (origins, 'USA')), 69);
 assert (counts(strcmp (origins, 'Japan')), 15);
 assert (counts(strcmp (origins, 'Germany')), 9);
 assert (counts(strcmp (origins, 'France')), 4);
 assert (counts(strcmp (origins, 'Sweden')), 2);
 assert (counts(strcmp (origins, 'Italy')), 1);
***** test
 ## patients (Logical)
 load patients;
 tbl = tabulate (Smoker);
 assert (size (tbl), [2, 3]);
 assert (tbl(:,1), {'0'; '1'});
 assert ([tbl{:,2}]', [66; 34]);
***** test
 ## patients (String)
 load patients;
 tbl = tabulate (Gender);
 vals = tbl(:,1);
 counts = [tbl{:,2}];
 assert (counts(strcmp (vals, 'Male')), 47);
 assert (counts(strcmp (vals, 'Female')), 53);
***** error<tabulate: X must be either a numeric vector> tabulate (ones (3))
***** error<tabulate: X must be either a numeric vector> tabulate ({1, 2, 3, 4})
***** error<tabulate: X must be either a numeric vector> ...
 tabulate ({"a", "b"; "a", "c"})
***** test
 x = categorical ({'a','b','c'});
 x(:) = categorical (missing);
 tbl = tabulate (x);
 assert (iscell (tbl));
 assert ([tbl{:,2}]', [0; 0; 0]);
 assert ([tbl{:,3}]', [0; 0; 0]);
***** test
 x = categorical ({}, {'low','med','high'});
 tbl = tabulate (x);
 assert (iscell (tbl));
 assert ([tbl{:,2}]', [0; 0; 0]);
 assert ([tbl{:,3}]', [0; 0; 0]);
***** test
 x = string ({'a','b'});
 x(:) = missing;
 tbl = tabulate (x);
 assert (iscell (tbl));
 assert (isempty (tbl));
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/ff2n.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/ff2n.m
***** error ff2n ();
***** error ff2n (2, 5);
***** error ff2n (2.5);
***** error ff2n (0);
***** error ff2n (-3);
***** error ff2n (3+2i);
***** error ff2n (Inf);
***** error ff2n (NaN);
***** test
 A = ff2n (3);
 assert (A, [0, 0, 0; 0, 0, 1; 0, 1, 0; 0, 1, 1; ...
             1, 0, 0; 1, 0, 1; 1, 1, 0; 1, 1, 1]);
***** test
 A = ff2n (2);
 assert (A, [0, 0; 0, 1; 1, 0; 1, 1]);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/logistic_regression.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/logistic_regression.m
***** test
 # Output compared to following MATLAB commands
 # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
 # P = mnrval(B,X)
 X = [1.489381332449196, 1.1534152241851305; ...
      1.8110085304863965, 0.9449666896938425; ...
      -0.04453299665130296, 0.34278203449678646; ...
      -0.36616019468850347, 1.130254275908322; ...
       0.15339143291005095, -0.7921044310668951; ...
      -1.6031878794469698, -1.8343471035233376; ...
      -0.14349521143198166, -0.6762996896828459; ...
      -0.4403818557740143, -0.7921044310668951; ...
      -0.7372685001160434, -0.027793137932169563; ...
      -0.11875465773681024, 0.5512305689880763];
 Y = [1,1,1,1,1,0,0,0,0,0]';
 [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (Y, X, false);
***** test
 # Output compared to following MATLAB commands
 # [B, DEV, STATS] = mnrfit(X,Y+1,'model','ordinal');
 load carbig
 X = [Acceleration Displacement Horsepower Weight];
 miles = [1,1,1,1,1,1,1,1,1,1,NaN,NaN,NaN,NaN,NaN,1,1,NaN,1,1,2,2,1,2,2,2, ...
          2,2,2,2,2,1,1,1,1,2,2,2,2,NaN,2,1,1,2,1,1,1,1,1,1,1,1,1,2,2,1,2, ...
          2,3,3,3,3,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,2,2,2,2,2, ...
          2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,1,1,1,1,1,2,2,2,1,2,2, ...
          2,1,1,3,2,2,2,1,2,2,1,2,2,2,1,3,2,3,2,1,1,1,1,1,1,1,1,3,2,2,3,3, ...
          2,2,2,2,2,3,2,1,1,1,1,1,1,1,1,1,1,1,2,2,1,3,2,2,2,2,2,2,1,3,2,2, ...
          2,2,2,3,2,2,2,2,2,1,1,1,1,2,2,2,2,3,2,3,3,2,1,1,1,3,3,2,2,2,1,2, ...
          2,1,1,1,1,1,3,3,3,2,3,1,1,1,1,1,2,2,1,1,1,1,1,3,2,2,2,3,3,3,3,2, ...
          2,2,4,3,3,4,3,2,2,2,2,2,2,2,2,2,2,2,1,1,2,1,1,1,3,2,2,3,2,2,2,2, ...
          2,1,2,1,3,3,2,2,2,2,2,1,1,1,1,1,1,2,1,3,3,3,2,2,2,2,2,3,3,3,3,2, ...
          2,2,3,4,3,3,3,2,2,2,2,3,3,3,3,3,4,2,4,4,4,3,3,4,4,3,3,3,2,3,2,3, ...
          2,2,2,2,3,4,4,3,3,3,3,3,3,3,3,3,3,3,3,3,3,2,NaN,3,2,2,2,2,2,1,2, ...
          2,3,3,3,2,2,2,3,3,3,3,3,3,3,3,3,3,3,2,3,2,2,3,3,2,2,4,3,2,3]';
 [INTERCEPT, SLOPE, DEV, DL, D2L, P] = logistic_regression (miles, X, false);
 assert (DEV, 433.197174495549, 1e-05);
 assert (INTERCEPT(1), -16.6895155618903, 1e-05);
 assert (INTERCEPT(2), -11.7207818178493, 1e-05);
 assert (INTERCEPT(3), -8.0605768506075, 1e-05);
 assert (SLOPE(1), 0.104762463756714, 1e-05);
 assert (SLOPE(2), 0.0103357623191891, 1e-05);
 assert (SLOPE(3), 0.0645199313242276, 1e-05);
 assert (SLOPE(4), 0.00166377028388103, 1e-05);
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/tiedrank.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/tiedrank.m
***** test
 [r,tieadj] = tiedrank ([10, 20, 30, 40, 20]);
 assert (r, [1, 2.5, 4, 5, 2.5]);
 assert (tieadj, 3);
***** test
 [r,tieadj] = tiedrank ([10; 20; 30; 40; 20]);
 assert (r, [1; 2.5; 4; 5; 2.5]);
 assert (tieadj, 3);
***** test
 [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1);
 assert (r, [1, 2.5, 4, 5, 2.5]);
 assert (tieadj, [1; 0; 18]);
***** test
 [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 0, 1);
 assert (r, [1, 2.5, 2, 1, 2.5]);
 assert (tieadj, 3);
***** test
 [r,tieadj] = tiedrank ([10, 20, 30, 40, 20], 1, 1);
 assert (r, [1, 2.5, 2, 1, 2.5]);
 assert (tieadj, [1; 0; 18]);
***** error <tiedrank: X must be a vector.> tiedrank (ones (2))
***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], [1, 1])
***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], "A")
***** error <tiedrank: TIEFLAG must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], [true, true])
***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], 0, [1, 1])
***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], 0, "A")
***** error <tiedrank: BIDIR must be a numeric or boolean scalar.> ...
 tiedrank ([1, 2, 3, 4, 5], 0, [true, true])
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/signtest.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/signtest.m
***** test
 [pval, h, stats] = signtest ([-ones(1, 1000) 1], 0, "tail", "left");
 assert (pval, 1.091701889420221e-218, 1e-14);
 assert (h, 1);
 assert (stats.zval, -31.5437631079266, 1e-14);
***** test
 [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0);
 assert (pval, 0.6875000000000006, 1e-14);
 assert (h, 0);
 assert (stats.zval, NaN);
 assert (stats.sign, 4);
***** test
 [pval, h, stats] = signtest ([-2 -1 0 2 1 3 1], 0, "method", "approximate");
 assert (pval, 0.6830913983096086, 1e-14);
 assert (h, 0);
 assert (stats.zval, 0.4082482904638631, 1e-14);
 assert (stats.sign, 4);
***** error <signtest: X must be a vector.> signtest (ones (2))
***** error <signtest: Y must be either a scalar of a vector.> ...
 signtest ([1, 2, 3, 4], ones (2))
***** error <signtest: X and Y vectors have different lengths.> ...
 signtest ([1, 2, 3, 4], [1, 2, 3])
***** error <signtest: optional arguments must be in pairs.> ...
 signtest ([1, 2, 3, 4], [], 'tail')
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', 1.2)
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', 0)
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', -0.05)
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', "a")
***** error <signtest: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signtest ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
***** error <signtest: 'tail' argument must be a character vector.> ...
 signtest ([1, 2, 3, 4], [], 'tail', 0.01)
***** error <signtest: 'tail' argument must be a character vector.> ...
 signtest ([1, 2, 3, 4], [], 'tail', {"both"})
***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
 signtest ([1, 2, 3, 4], [], 'tail', "some")
***** error <signtest: 'tail' value must be either 'both', right' or 'left'.> ...
 signtest ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
***** error <signtest: 'method' argument must be a character vector.> ...
 signtest ([1, 2, 3, 4], [], 'method', 0.01)
***** error <signtest: 'method' argument must be a character vector.> ...
 signtest ([1, 2, 3, 4], [], 'method', {"exact"})
***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
 signtest ([1, 2, 3, 4], [], 'method', "some")
***** error <signtest: 'method' value must be either 'exact' or 'approximate'.> ...
 signtest ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/probit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/probit.m
***** assert (probit ([-1, 0, 0.5, 1, 2]), [NaN, -Inf, 0, Inf, NaN])
***** assert (probit ([0.2, 0.99]), norminv ([0.2, 0.99]))
***** error probit ()
***** error probit (1, 2)
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/pcares.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/pcares.m
***** demo
 x = [ 7    26     6    60;
       1    29    15    52;
      11    56     8    20;
      11    31     8    47;
       7    52     6    33;
      11    55     9    22;
       3    71    17     6;
       1    31    22    44;
       2    54    18    22;
      21    47     4    26;
       1    40    23    34;
      11    66     9    12;
      10    68     8    12];

 ## As we increase the number of principal components, the norm
 ## of the residuals matrix will decrease
 r1 = pcares (x,1);
 n1 = norm (r1)
 r2 = pcares (x,2);
 n2 = norm (r2)
 r3 = pcares (x,3);
 n3 = norm (r3)
 r4 = pcares (x,4);
 n4 = norm (r4)
***** test
 load hald
 r1 = pcares (ingredients,1);
 r2 = pcares (ingredients,2);
 r3 = pcares (ingredients,3);
 assert (r1(1,:), [2.0350,  2.8304, -6.8378, 3.0879], 1e-4);
 assert (r2(1,:), [-2.4037, 2.6930, -1.6482, 2.3425], 1e-4);
 assert (r3(1,:), [ 0.2008, 0.1957,  0.2045, 0.1921], 1e-4);
***** error<pcares: too few input arguments.> pcares (ones (20, 3))
***** error<pcares: NDIM must be less than or equal to the column of X.> ...
 pcares (ones (30, 2), 3)
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationGAM.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Classification/ClassificationGAM.m
***** demo
 ## Train a GAM classifier for binary classification
 ## using specific data and plot the decision boundaries.

 ## Define specific data
 X = [1, 2; 2, 3; 3, 3; 4, 5; 5, 5; ...
     6, 7; 7, 8; 8, 8; 9, 9; 10, 10];
 Y = [0; 0; 0; 0; 0; ...
     1; 1; 1; 1; 1];

 ## Train the GAM model
 obj = fitcgam (X, Y, "Interactions", "all")

 ## Create a grid of values for prediction
 x1 = [min(X(:,1)):0.1:max(X(:,1))];
 x2 = [min(X(:,2)):0.1:max(X(:,2))];
 [x1G, x2G] = meshgrid (x1, x2);
 XGrid = [x1G(:), x2G(:)];
 [labels, score] = predict (obj, XGrid);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
 a = ClassificationGAM (x, y, "PredictorNames", PredictorNames);
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
 assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.PredictorNames, PredictorNames)
 assert (a.BaseModel.Intercept, 0)
***** test
 load fisheriris
 inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
 X = meas(inds, :);
 Y = species(inds, :)';
 Y = strcmp (Y, 'virginica')';
 a = ClassificationGAM (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {X, Y, 100})
 assert ({a.NumPredictors, a.ResponseName}, {4, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
 assert (a.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
 assert (a.ModelwInt.Intercept, 0)
***** test
 X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
 Y = [0; 1; 0; 1; 1];
 a = ClassificationGAM (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {X, Y, 5})
 assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.PredictorNames, {'x1', 'x2', 'x3'})
 assert (a.Knots, [4, 4, 4])
 assert (a.Order, [3, 3, 3])
 assert (a.DoF, [7, 7, 7])
 assert (a.BaseModel.Intercept, 0.4055, 1e-1)
***** test
 ## Test uniform prior
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = [0; 0; 1; 1];
 a = ClassificationGAM (x, y, 'Prior', 'uniform');
 assert (a.Prior, [0.5, 0.5], 1e-6);
***** test
 ## Test empirical prior
 x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
 y = [0; 0; 0; 1; 1];
 a = ClassificationGAM (x, y, 'Prior', 'empirical');
 assert (a.Prior, [0.6; 0.4], 1e-6);
***** test
 ## Test numeric prior
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = [0; 0; 1; 1];
 a = ClassificationGAM (x, y, 'Prior', [0.7, 0.3]);
 assert (a.Prior, [0.7, 0.3], 1e-6);
***** test
 ## Test default prior (empirical)
 x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10; 11, 12];
 y = [0; 0; 0; 1; 1; 1];
 a = ClassificationGAM (x, y);
 assert (a.Prior, [0.5; 0.5], 1e-6);
***** test
 ## Test prior normalization
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = [0; 0; 1; 1];
 a = ClassificationGAM (x, y, 'Prior', [2, 1]);
 assert (a.Prior, [2/3, 1/3], 1e-6);
***** error<ClassificationGAM: 'Prior' must be a 2-element vector.> ...
 ClassificationGAM (ones(4,2), ones(4,1), "Prior", [1])
***** error<ClassificationGAM: 'Prior' must be a 2-element vector.> ...
 ClassificationGAM (ones(4,2), ones(4,1), "Prior", [1, 2, 3])
***** error<ClassificationGAM: 'Prior' must be a numeric vector or a string.> ...
 ClassificationGAM (ones(4,2), ones(4,1), "Prior", {1, 2})
***** error<ClassificationGAM: 'Prior' must be> ...
 ClassificationGAM (ones(4,2), ones(4,1), "Prior", "invalid")
***** error<ClassificationGAM: too few input arguments.> ClassificationGAM ()
***** error<ClassificationGAM: too few input arguments.> ...
 ClassificationGAM (ones(4, 1))
***** error<ClassificationGAM: number of rows in X and Y must be equal.> ...
 ClassificationGAM (ones (4,2), ones (1,4))
***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", ["A"])
***** error<ClassificationGAM: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", "A")
***** error<ClassificationGAM: 'PredictorNames' must equal the number of columns in X.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", {"Y"})
***** error<ClassificationGAM: 'ResponseName' must be a character vector.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "ResponseName", 1)
***** error<ClassificationGAM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
 ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
***** error<ClassificationGAM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
 ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", {1})
***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
 ClassificationGAM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
 ClassificationGAM (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
 ClassificationGAM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationGAM: not all 'ClassNames' are present in Y.> ...
 ClassificationGAM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "Cost", [1, 2])
***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "Cost", "string")
***** error<ClassificationGAM: 'Cost' must be a numeric square matrix.> ...
 ClassificationGAM (ones (5,2), ones (5,1), "Cost", {eye(2)})
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
 y = [1; 0; 1; 0; 1];
 a = ClassificationGAM (x, y, "interactions", "all");
 l = {'1'; '1'; '1'; '1'; '1'};
 s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
      0.4259, 0.5741; 0.3760, 0.6240];
 [labels, scores] = predict (a, x);
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 5})
 assert ({a.NumPredictors, a.ResponseName}, {2, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.PredictorNames, {'x1', 'x2'})
 assert (a.ModelwInt.Intercept, 0.4055, 1e-1)
 assert (labels, l)
 assert (scores, s, 1e-1)
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 interactions = [false, true, false; true, false, true; false, true, false];
 a = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
 [label, score] = predict (a, x, "includeinteractions", true);
 l = {'0'; '0'; '1'; '1'};
 s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
 assert (class (a), "ClassificationGAM");
 assert ({a.X, a.Y, a.NumObservations}, {x, y, 4})
 assert ({a.NumPredictors, a.ResponseName}, {3, "Y"})
 assert (a.ClassNames, {'0'; '1'})
 assert (a.PredictorNames, {'x1', 'x2', 'x3'})
 assert (a.ModelwInt.Intercept, 0)
 assert (label, l)
 assert (score, s, 1e-1)
***** error<ClassificationGAM.predict: too few input arguments.> ...
 predict (ClassificationGAM (ones (4,2), ones (4,1)))
***** error<ClassificationGAM.predict: XC is empty.> ...
 predict (ClassificationGAM (ones (4,2), ones (4,1)), [])
***** error<ClassificationGAM.predict: XC must have the same number of predictors as the trained model.> ...
 predict (ClassificationGAM (ones (4,2), ones (4,1)), 1)
***** shared x, y, obj
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6];
 y = [0; 0; 1; 1; 0];
 obj = fitcgam (x, y);
***** test
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (obj);
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
 assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
***** test
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (obj, "KFold", 2);
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 2)
 assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
 assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
***** test
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (obj, "HoldOut", 0.2);
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
 assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
***** test
 status = warning;
 warning ('off');
 rand ("seed", 23);
 partition = cvpartition (y, 'KFold', 3);
 warning (status);
 CVMdl = crossval (obj, 'cvPartition', partition);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert (CVMdl.KFold == 3)
 assert (class (CVMdl.Trained{1}), "CompactClassificationGAM")
 assert (CVMdl.CrossValidatedModel, "ClassificationGAM")
***** error<ClassificationGAM.crossval: Name-Value arguments must be in pairs.> ...
 crossval (obj, "kfold")
***** error<ClassificationGAM.crossval: specify only one of the optional Name-Value paired arguments.>...
 crossval (obj, "kfold", 12, "holdout", 0.2)
***** error<ClassificationGAM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (obj, "kfold", 'a')
***** error<ClassificationGAM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (obj, "holdout", 2)
***** error<ClassificationGAM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (obj, "leaveout", 1)
***** error<ClassificationGAM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (obj, "cvpartition", 1)
44 tests, 44 passed, 0 known failure, 0 skipped
[inst/Classification/CompactClassificationGAM.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Classification/CompactClassificationGAM.m
***** demo
 ## Create a generalized additive model classifier and its compact version
 # and compare their size

 load fisheriris
 X = meas;
 Y = species;

 Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
 CMdl = crossval (Mdl)
***** test
 Mdl = CompactClassificationGAM ();
 assert (class (Mdl), "CompactClassificationGAM")
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 PredictorNames = {'Feature1', 'Feature2', 'Feature3'};
 Mdl = fitcgam (x, y, "PredictorNames", PredictorNames);
 CMdl = compact (Mdl);
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
 assert (CMdl.ClassNames, {'0'; '1'})
 assert (CMdl.PredictorNames, PredictorNames)
 assert (CMdl.BaseModel.Intercept, 0)
***** test
 load fisheriris
 inds = strcmp (species,'versicolor') | strcmp (species,'virginica');
 X = meas(inds, :);
 Y = species(inds, :)';
 Y = strcmp (Y, 'virginica')';
 Mdl = fitcgam (X, Y, 'Formula', 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3');
 CMdl = compact (Mdl);
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {4, "Y"})
 assert (CMdl.ClassNames, {'0'; '1'})
 assert (CMdl.Formula, 'Y ~ x1 + x2 + x3 + x4 + x1:x2 + x2:x3')
 assert (CMdl.PredictorNames, {'x1', 'x2', 'x3', 'x4'})
 assert (CMdl.ModelwInt.Intercept, 0)
***** test
 X = [2, 3, 5; 4, 6, 8; 1, 2, 3; 7, 8, 9; 5, 4, 3];
 Y = [0; 1; 0; 1; 1];
 Mdl = fitcgam (X, Y, 'Knots', [4, 4, 4], 'Order', [3, 3, 3]);
 CMdl = compact (Mdl);
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
 assert (CMdl.ClassNames, {'0'; '1'})
 assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
 assert (CMdl.Knots, [4, 4, 4])
 assert (CMdl.Order, [3, 3, 3])
 assert (CMdl.DoF, [7, 7, 7])
 assert (CMdl.BaseModel.Intercept, 0.4055, 1e-1)
***** error<CompactClassificationGAM: invalid classification object.> ...
 CompactClassificationGAM (1)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8; 9, 10];
 y = [1; 0; 1; 0; 1];
 Mdl = fitcgam (x, y, "interactions", "all");
 CMdl = compact (Mdl);
 l = {'1'; '1'; '1'; '1'; '1'};
 s = [0.3760, 0.6240; 0.4259, 0.5741; 0.3760, 0.6240; ...
      0.4259, 0.5741; 0.3760, 0.6240];
 [labels, scores] = predict (CMdl, x);
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {2, "Y"})
 assert (CMdl.ClassNames, {'0'; '1'})
 assert (CMdl.PredictorNames, {'x1', 'x2'})
 assert (CMdl.ModelwInt.Intercept, 0.4055, 1e-1)
 assert (labels, l)
 assert (scores, s, 1e-1)
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [0; 0; 1; 1];
 interactions = [false, true, false; true, false, true; false, true, false];
 Mdl = fitcgam (x, y, "learningrate", 0.2, "interactions", interactions);
 CMdl = compact (Mdl);
 [label, score] = predict (CMdl, x, "includeinteractions", true);
 l = {'0'; '0'; '1'; '1'};
 s = [0.5106, 0.4894; 0.5135, 0.4865; 0.4864, 0.5136; 0.4847, 0.5153];
 assert (class (CMdl), "CompactClassificationGAM");
 assert ({CMdl.NumPredictors, CMdl.ResponseName}, {3, "Y"})
 assert (CMdl.ClassNames, {'0'; '1'})
 assert (CMdl.PredictorNames, {'x1', 'x2', 'x3'})
 assert (CMdl.ModelwInt.Intercept, 0)
 assert (label, l)
 assert (score, s, 1e-1)
***** shared CMdl
 Mdl = fitcgam (ones (4,2), ones (4,1));
 CMdl = compact (Mdl);
***** error<CompactClassificationGAM.predict: too few input arguments.> ...
 predict (CMdl)
***** error<CompactClassificationGAM.predict: XC is empty.> ...
 predict (CMdl, [])
***** error<CompactClassificationGAM.predict: XC must have the same number of features as the trained model.> ...
 predict (CMdl, 1)
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/Classification/CompactClassificationSVM.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Classification/CompactClassificationSVM.m
***** demo
 ## Create a support vectors machine classifier and its compact version
 # and compare their size

 load fisheriris
 X = meas;
 Y = species;

 selected_classes = unique (Y)(randperm (3, 2));
 selected_indices = ismember (Y, selected_classes);
 X_selected = X(selected_indices, :);
 Y_selected = Y(selected_indices);
 Mdl = fitcsvm (X_selected, Y_selected, 'ClassNames', selected_classes);
 CMdl = crossval (Mdl)
***** error<CompactClassificationSVM: invalid classification object.> ...
 CompactClassificationSVM (1)
***** shared x, y, CMdl
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
***** test
 xc = [min(x); mean(x); max(x)];
 Mdl = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
 CMdl = compact (Mdl);
 assert (isempty (CMdl.Alpha), true)
 assert (sum (CMdl.IsSupportVector), numel (CMdl.Beta))
 [label, score] = predict (CMdl, xc);
 assert (label, [1; 2; 2]);
 assert (score(:,1), [0.99285; -0.080296; -0.93694], 1e-5);
 assert (score(:,1), -score(:,2), eps)
***** test
 Mdl = fitcsvm (x, y);
 CMdl = compact (Mdl);
 assert (isempty (CMdl.Beta), true)
 assert (sum (CMdl.IsSupportVector), numel (CMdl.Alpha))
 assert (numel (CMdl.Alpha), 24)
 assert (CMdl.Bias, -14.415, 1e-3)
 xc = [min(x); mean(x); max(x)];
 label = predict (CMdl, xc);
 assert (label, [1; 2; 2]);
***** error<CompactClassificationSVM.predict: too few input arguments.> ...
 predict (CMdl)
***** error<CompactClassificationSVM.predict: XC is empty.> ...
 predict (CMdl, [])
***** error<CompactClassificationSVM.predict: XC must have the same number of predictors as the trained SVM model.> ...
 predict (CMdl, 1)
***** test
 CMdl.ScoreTransform = "a";
***** error<CompactClassificationSVM.predict: 'ScoreTransform' must be a 'function_handle' object.> ...
 [labels, scores] = predict (CMdl, x);
***** test
 rand ("seed", 1);
 C = cvpartition (y, 'HoldOut', 0.15);
 Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
                'KernelFunction', 'rbf', 'Tolerance', 1e-7);
 CMdl = compact (Mdl);
 testInds = test (C);
 expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
                    2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
                   -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
 computed_margin = margin (CMdl, x(testInds,:), y(testInds,:));
 assert (computed_margin, expected_margin, 1e-4);
***** error<CompactClassificationSVM.margin: too few input arguments.> ...
 margin (CMdl)
***** error<CompactClassificationSVM.margin: too few input arguments.> ...
 margin (CMdl, zeros (2))
***** error<CompactClassificationSVM.margin: X is empty.> ...
 margin (CMdl, [], 1)
***** error<CompactClassificationSVM.margin: X must have the same number of predictors as the trained SVM model.> ...
 margin (CMdl, 1, 1)
***** error<CompactClassificationSVM.margin: Y is empty.> ...
 margin (CMdl, [1, 2], [])
***** error<CompactClassificationSVM.margin: Y must have the same number of rows as X.> ...
 margin (CMdl, [1, 2], [1; 2])
***** test
 rand ("seed", 1);
 C = cvpartition (y, 'HoldOut', 0.15);
 Mdl = fitcsvm (x(training (C),:), y(training (C)), ...
                'KernelFunction', 'rbf', 'Tolerance', 1e-7);
 CMdl = compact (Mdl);
 testInds = test (C);
 L1 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
 L2 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
 L3 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
 L4 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
 L5 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
 L6 = loss (CMdl, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
 assert (L1, 2.8711, 1e-4);
 assert (L2, 0.5333, 1e-4);
 assert (L3, 10.9685, 1e-4);
 assert (L4, 1.9827, 1e-4);
 assert (L5, 1.5849, 1e-4);
 assert (L6, 7.6739, 1e-4);
***** error<CompactClassificationSVM.loss: too few input arguments.> ...
 loss (CMdl)
***** error<CompactClassificationSVM.loss: too few input arguments.> ...
 loss (CMdl, zeros (2))
***** error<CompactClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
 loss (CMdl, [1, 2], 1, "LossFun")
***** error<CompactClassificationSVM.loss: X is empty.> ...
 loss (CMdl, [], zeros (2))
***** error<CompactClassificationSVM.loss: X must have the same number of predictors as the trained SVM model.> ...
 loss (CMdl, 1, zeros (2))
***** error<CompactClassificationSVM.loss: Y is empty.> ...
 loss (CMdl, [1, 2], [])
***** error<CompactClassificationSVM.loss: Y must have the same number of rows as X.> ...
 loss (CMdl, [1, 2], [1; 2])
***** error<CompactClassificationSVM.loss: 'LossFun' must be a character vector.> ...
 loss (CMdl, [1, 2], 1, "LossFun", 1)
***** error<CompactClassificationSVM.loss: unsupported Loss function.> ...
 loss (CMdl, [1, 2], 1, "LossFun", "some")
***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
 loss (CMdl, [1, 2], 1, "Weights", ['a', 'b'])
***** error<CompactClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
 loss (CMdl, [1, 2], 1, "Weights", 'a')
***** error<CompactClassificationSVM.loss: size of 'Weights' must be equal to the number of rows in X.> ...
 loss (CMdl, [1, 2], 1, "Weights", [1, 2])
***** error<CompactClassificationSVM.loss: invalid parameter name in optional pair arguments.> ...
 loss (CMdl, [1, 2], 1, "some", "some")
29 tests, 29 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationPartitionedModel.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Classification/ClassificationPartitionedModel.m
***** demo

 load fisheriris
 x = meas;
 y = species;

 ## Create a KNN classifier model
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);

 ## Create a partition for 5-fold cross-validation
 partition = cvpartition (y, "KFold", 5);

 ## Create the ClassificationPartitionedModel object
 cvModel = crossval (obj, 'cvPartition', partition)
***** demo

 load fisheriris
 x = meas;
 y = species;

 ## Create a KNN classifier model
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);

 ## Create the ClassificationPartitionedModel object
 cvModel = crossval (obj);

 ## Predict the class labels for the observations not used for training
 [label, score, cost] = kfoldPredict (cvModel);
 fprintf ("Cross-validated accuracy = %1.2f%% (%d/%d)\n", ...
          sum (strcmp (label, y)) / numel (y) *100, ...
          sum (strcmp (label, y)), numel (y))
***** test
 load fisheriris
 a = fitcdiscr (meas, species, "gamma", 0.3);
 cvModel = crossval (a, "KFold", 5);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (cvModel.NumObservations, 150);
 assert (numel (cvModel.Trained), 5);
 assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
 assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
 assert (cvModel.KFold, 5);
***** test
 load fisheriris
 a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
 cvModel = crossval (a, "HoldOut", 0.3);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {meas, species});
 assert (cvModel.NumObservations, 150);
 assert (numel (cvModel.Trained), 1);
 assert (class (cvModel.Trained{1}), "CompactClassificationDiscriminant");
 assert (cvModel.CrossValidatedModel, "ClassificationDiscriminant");
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcgam (x, y, "Interactions", "all");
 cvModel = crossval (a, "KFold", 2);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (cvModel.NumObservations, 4);
 assert (numel (cvModel.Trained), 2);
 assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
 assert (cvModel.CrossValidatedModel, "ClassificationGAM");
 assert (cvModel.KFold, 2);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcgam (x, y);
 cvModel = crossval (a, "LeaveOut", "on");
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (numel (cvModel.Trained), 4);
 assert (class (cvModel.Trained{1}), "CompactClassificationGAM");
 assert (cvModel.CrossValidatedModel, "ClassificationGAM");
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y);
 partition = cvpartition (y, "KFold", 2);
 cvModel = ClassificationPartitionedModel (a, partition);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (class (cvModel.Trained{1}), "ClassificationKNN");
 assert (cvModel.NumObservations, 4);
 assert (cvModel.ModelParameters.NumNeighbors, 1);
 assert (cvModel.ModelParameters.NSMethod, "kdtree");
 assert (cvModel.ModelParameters.Distance, "euclidean");
 assert (! cvModel.ModelParameters.Standardize);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = fitcknn (x, y, "NSMethod", "exhaustive");
 partition = cvpartition (y, "HoldOut", 0.2);
 cvModel = ClassificationPartitionedModel (a, partition);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (class (cvModel.Trained{1}), "ClassificationKNN");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (cvModel.ModelParameters.NumNeighbors, 1);
 assert (cvModel.ModelParameters.NSMethod, "exhaustive");
 assert (cvModel.ModelParameters.Distance, "euclidean");
 assert (! cvModel.ModelParameters.Standardize);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 2;
 a = fitcknn (x, y, "NumNeighbors" ,k);
 partition = cvpartition (numel (y), "LeaveOut");
 cvModel = ClassificationPartitionedModel (a, partition);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (class (cvModel.Trained{1}), "ClassificationKNN");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (cvModel.ModelParameters.NumNeighbors, k);
 assert (cvModel.ModelParameters.NSMethod, "kdtree");
 assert (cvModel.ModelParameters.Distance, "euclidean");
 assert (! cvModel.ModelParameters.Standardize);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = {"a"; "a"; "b"; "b"};
 a = fitcnet (x, y, "IterationLimit", 50);
 cvModel = crossval (a, "KFold", 2);
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert (cvModel.NumObservations, 4);
 assert (numel (cvModel.Trained), 2);
 assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
 assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
 assert (cvModel.KFold, 2);
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = {"a"; "a"; "b"; "b"};
 a = fitcnet (x, y, "LayerSizes", [5, 3]);
 cvModel = crossval (a, "LeaveOut", "on");
 assert (class (cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (numel (cvModel.Trained), 4);
 assert (class (cvModel.Trained{1}), "CompactClassificationNeuralNetwork");
 assert (cvModel.CrossValidatedModel, "ClassificationNeuralNetwork");
***** test
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
 SVMModel = fitcsvm (x,y);
 CVMdl = crossval (SVMModel, "KFold", 5);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
***** test
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
 obj = fitcsvm (x, y);
 CVMdl = crossval (obj, "HoldOut", 0.2);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
***** test
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
 obj = fitcsvm (x, y);
 CVMdl = crossval (obj, "LeaveOut", 'on');
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM");
***** error<ClassificationPartitionedModel: too few input arguments.> ...
 ClassificationPartitionedModel ()
***** error<ClassificationPartitionedModel: too few input arguments.> ...
 ClassificationPartitionedModel (ClassificationKNN (ones (4,2), ones (4,1)))
***** error<ClassificationPartitionedModel: unsupported model type.> ...
 ClassificationPartitionedModel (RegressionGAM (ones (40,2), ...
 randi ([1, 2], 40, 1)), cvpartition (randi ([1, 2], 40, 1), 'Holdout', 0.3))
***** error<ClassificationPartitionedModel: invalid 'cvpartition' object.> ...
 ClassificationPartitionedModel (ClassificationKNN (ones (4,2), ...
 ones (4,1)), 'Holdout')
***** test
 load fisheriris
 a = fitcdiscr (meas, species, "gamma", 0.5, "fillcoeffs", "off");
 cvModel = crossval (a, "Kfold", 4);
 [label, score, cost] = kfoldPredict (cvModel);
 assert (class(cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {meas, species});
 assert (cvModel.NumObservations, 150);
***** # assert (label, {"b"; "b"; "a"; "a"});
***** # assert (score, [4.5380e-01, 5.4620e-01; 2.4404e-01, 7.5596e-01; ...
***** #         9.9392e-01, 6.0844e-03; 9.9820e-01, 1.8000e-03], 1e-4);
***** # assert (cost, [5.4620e-01, 4.5380e-01; 7.5596e-01, 2.4404e-01; ...
***** #         6.0844e-03, 9.9392e-01; 1.8000e-03, 9.9820e-01], 1e-4);
***** test
 x = ones(4, 11);
 y = {"a"; "a"; "b"; "b"};
 k = 3;
 a = fitcknn (x, y, "NumNeighbors", k);
 partition = cvpartition (numel (y), "LeaveOut");
 cvModel = ClassificationPartitionedModel (a, partition);
 [label, score, cost] = kfoldPredict (cvModel);
 assert (class(cvModel), "ClassificationPartitionedModel");
 assert ({cvModel.X, cvModel.Y}, {x, y});
 assert (cvModel.NumObservations, 4);
 assert (cvModel.ModelParameters.NumNeighbors, k);
 assert (cvModel.ModelParameters.NSMethod, "exhaustive");
 assert (cvModel.ModelParameters.Distance, "euclidean");
 assert (! cvModel.ModelParameters.Standardize);
 assert (label, {"b"; "b"; "a"; "a"});
 assert (score, [0.3333, 0.6667; 0.3333, 0.6667; 0.6667, 0.3333; ...
          0.6667, 0.3333], 1e-4);
 assert (cost, [0.6667, 0.3333; 0.6667, 0.3333; 0.3333, 0.6667; ...
          0.3333, 0.6667], 1e-4);
***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationSVM cross validated models.> ...
 [label, score, cost] = kfoldPredict (crossval (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1))))
***** error<ClassificationPartitionedModel.kfoldPredict: 'Cost' output is not supported for ClassificationNeuralNetwork cross validated models.> ...
 [label, score, cost] = kfoldPredict (crossval (ClassificationNeuralNetwork (ones (40,2), randi ([1, 2], 40, 1))))
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationNeuralNetwork.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Classification/ClassificationNeuralNetwork.m
***** error<ClassificationNeuralNetwork: too few input arguments.> ...
 ClassificationNeuralNetwork ()
***** error<ClassificationNeuralNetwork: too few input arguments.> ...
 ClassificationNeuralNetwork (ones(10,2))
***** error<ClassificationNeuralNetwork: number of rows in X and Y must be equal.> ...
 ClassificationNeuralNetwork (ones(10,2), ones (5,1))
***** error<ClassificationNeuralNetwork: 'Standardize' must be either true or false.> ...
 ClassificationNeuralNetwork (ones (5,3), ones (5,1), "standardize", "a")
***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", ["A"])
***** error<ClassificationNeuralNetwork: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", "A")
***** error<ClassificationNeuralNetwork: 'PredictorNames' must have the same number of columns as X.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", {"Y"})
***** error<ClassificationNeuralNetwork: 'ResponseName' must be a character vector.> ...
 ClassificationNeuralNetwork (ones (5,2), ones (5,1), "ResponseName", 1)
***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
 ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", @(x)x)
***** error<ClassificationNeuralNetwork: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
 ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", {1})
***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
 ClassificationNeuralNetwork (ones(10,2), ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
 ClassificationNeuralNetwork (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
 ClassificationNeuralNetwork (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationNeuralNetwork: not all 'ClassNames' are present in Y.> ...
 ClassificationNeuralNetwork (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", -1)
***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", 0.5)
***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [1,-2])
***** error<ClassificationNeuralNetwork: 'LayerSizes' must be a positive integer vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10,20,30.5])
***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", -0.1)
***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", [0.1, 0.01])
***** error<ClassificationNeuralNetwork: 'LearningRate' must be a positive scalar.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LearningRate", "a")
***** error<ClassificationNeuralNetwork: 'Activations' must be a character vector or a cellstring vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", 123)
***** error<ClassificationNeuralNetwork: unsupported 'Activation' function.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", "unsupported_type")
***** error<ClassificationNeuralNetwork: unsupported 'Activation' functions.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "LayerSizes", [10, 5], ...
 "Activations", {"sigmoid", "unsupported_type"})
***** error<ClassificationNeuralNetwork: 'Activations' vector does not match the number of layers.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "Activations", {"sigmoid", "relu", "softmax"})
***** error<ClassificationNeuralNetwork: 'OutputLayerActivation' must be a character vector.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", 123)
***** error<ClassificationNeuralNetwork: unsupported 'OutputLayerActivation' function.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "OutputLayerActivation", "unsupported_type")
***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", -1)
***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", 0.5)
***** error<ClassificationNeuralNetwork: 'IterationLimit' must be a positive integer.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "IterationLimit", [1,2])
***** error<ClassificationNeuralNetwork: 'ScoreTransform' must be a character vector or a function handle.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", [1,2])
***** error<ClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "ScoreTransform", "unsupported_type")
***** error<ClassificationNeuralNetwork: invalid parameter name in optional pair arguments.> ...
 ClassificationNeuralNetwork (ones(10,2), ones(10,1), "some", "some")
***** error<ClassificationNeuralNetwork: invalid values in X.> ...
 ClassificationNeuralNetwork ([1;2;3;'a';4], ones (5,1))
***** error<ClassificationNeuralNetwork: invalid values in X.> ...
 ClassificationNeuralNetwork ([1;2;3;Inf;4], ones (5,1))
***** shared x, y, objST, Mdl
 load fisheriris
 x = meas;
 y = grp2idx (species);
 Mdl = fitcnet (x, y, "IterationLimit", 100);
***** error<ClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
 Mdl.ScoreTransform = "a";
***** error<ClassificationNeuralNetwork.predict: too few input arguments.> ...
 predict (Mdl)
***** error<ClassificationNeuralNetwork.predict: XC is empty.> ...
 predict (Mdl, [])
***** error<ClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained model.> ...
 predict (Mdl, 1)
***** test
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (Mdl, "KFold", 5);
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
 assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
***** test
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (Mdl, "HoldOut", 0.2);
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationNeuralNetwork")
 assert (CVMdl.CrossValidatedModel, "ClassificationNeuralNetwork")
***** error<ClassificationNeuralNetwork.crossval: Name-Value arguments must be in pairs.> ...
 crossval (Mdl, "KFold")
***** error<ClassificationNeuralNetwork.crossval: specify only one of the optional Name-Value paired arguments.> ...
 crossval (Mdl, "KFold", 5, "leaveout", 'on')
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", 'a')
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", 1)
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", -1)
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", 11.5)
***** error<ClassificationNeuralNetwork.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (Mdl, "KFold", [1,2])
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", 'a')
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", 11.5)
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", -1)
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", 0)
***** error<ClassificationNeuralNetwork.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (Mdl, "Holdout", 1)
***** error<ClassificationNeuralNetwork.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (Mdl, "Leaveout", 1)
***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (Mdl, "CVPartition", 1)
***** error<ClassificationNeuralNetwork.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (Mdl, "CVPartition", 'a')
***** error<ClassificationNeuralNetwork.crossval: invalid parameter name in optional paired arguments> ...
 crossval (Mdl, "some", "some")
58 tests, 58 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationSVM.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Classification/ClassificationSVM.m
***** demo
 ## Create a Support Vector Machine classifier and determine margin for test
 ## data.
 load fisheriris
 rng(1);  ## For reproducibility

 ## Select indices of the non-setosa species
 inds = !strcmp(species, 'setosa');

  ## Select features and labels for non-setosa species
 X = meas(inds, 3:4);
 Y = grp2idx(species(inds));

 ##  Convert labels to +1 and -1
 unique_classes = unique(Y);
 Y(Y == unique_classes(1)) = -1;
 Y(Y == unique_classes(2)) = 1;

 ## Partition data for training and testing
 cv = cvpartition(Y, 'HoldOut', 0.15);
 X_train = X(training(cv), :);
 Y_train = Y(training(cv));
 X_test = X(test(cv), :);
 Y_test = Y(test(cv));

 ## Train the SVM model
 CVSVMModel = fitcsvm(X_train, Y_train);

 ## Calculate margins
 m = margin(CVSVMModel, X_test, Y_test);
 disp(m);
***** demo
 ## Create a Support Vector Machine classifier and determine loss for test
 ## data.
 load fisheriris
 rng(1);  ## For reproducibility

  ## Select indices of the non-setosa species
 inds = !strcmp(species, 'setosa');

  ## Select features and labels for non-setosa species
 X = meas(inds, 3:4);
 Y = grp2idx(species(inds));

 ##  Convert labels to +1 and -1
 unique_classes = unique(Y);
 Y(Y == unique_classes(1)) = -1;
 Y(Y == unique_classes(2)) = 1;

 ## Randomly partition the data into training and testing sets
 cv = cvpartition(Y, 'HoldOut', 0.3); # 30% data for testing, 60% for training

 X_train = X(training(cv), :);
 Y_train = Y(training(cv));

 X_test = X(test(cv), :);
 Y_test = Y(test(cv));

 ## Train the SVM model
 SVMModel = fitcsvm(X_train, Y_train);

 ## Calculate loss

 L = loss(SVMModel,X_test,Y_test,'LossFun','binodeviance')
 L = loss(SVMModel,X_test,Y_test,'LossFun','classiferror')
 L = loss(SVMModel,X_test,Y_test,'LossFun','exponential')
 L = loss(SVMModel,X_test,Y_test,'LossFun','hinge')
 L = loss(SVMModel,X_test,Y_test,'LossFun','logit')
 L = loss(SVMModel,X_test,Y_test,'LossFun','quadratic')
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; ...
 3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = [1; 2; 3; 4; 2; 3; 4; 2; 3; 4; 2; 3; 4];
 a = ClassificationSVM (x, y, "ClassNames", [1, 2]);
 assert (class (a), "ClassificationSVM");
 assert (a.RowsUsed, [1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0]');
 assert ({a.X, a.Y}, {x, y})
 assert (a.NumObservations, 5)
 assert ({a.ResponseName, a.PredictorNames}, {"Y", {"x1", "x2", "x3"}})
 assert ({a.ClassNames, a.ModelParameters.SVMtype}, {[1; 2], "c_svc"})
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = ClassificationSVM (x, y);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "linear"})
 assert (a.ModelParameters.BoxConstraint, 1)
 assert (a.ClassNames, [-1; 1])
 assert (a.ModelParameters.KernelOffset, 0)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = ClassificationSVM (x, y, "KernelFunction", "rbf", "BoxConstraint", 2, ...
 "KernelOffset", 2);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "rbf"})
 assert (a.ModelParameters.BoxConstraint, 2)
 assert (a.ModelParameters.KernelOffset, 2)
***** test
 x = [1, 2; 2, 3; 3, 4; 4, 5; 2, 3; 3, 4; 2, 3; 3, 4; 2, 3; 3, 4];
 y = [1; 1; -1; -1; 1; -1; -1; -1; -1; -1];
 a = ClassificationSVM (x, y, "KernelFunction", "polynomial", ...
 "PolynomialOrder", 3);
 assert (class (a), "ClassificationSVM");
 assert ({a.X, a.Y, a.ModelParameters.KernelFunction}, {x, y, "polynomial"})
 assert (a.ModelParameters.PolynomialOrder, 3)
***** error<ClassificationSVM: too few input arguments.> ClassificationSVM ()
***** error<ClassificationSVM: too few input arguments.> ...
 ClassificationSVM (ones(10,2))
***** error<ClassificationSVM: number of rows in X and Y must be equal.> ...
 ClassificationSVM (ones(10,2), ones (5,1))
***** error<ClassificationSVM: 'Standardize' must be either true or false.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "Standardize", 'a')
***** error<ClassificationSVM: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", ['x1';'x2'])
***** error<ClassificationSVM: 'PredictorNames' must have the same number of columns as X.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "PredictorNames", {'x1','x2','x3'})
***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", {'Y'})
***** error<ClassificationSVM: 'ResponseName' must be a character vector.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ResponseName", 21)
***** error<ClassificationSVM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", @(x)x)
***** error<ClassificationSVM: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", {1})
***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
 ClassificationSVM (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
 ClassificationSVM (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationSVM: not all 'ClassNames' are present in Y.> ...
 ClassificationSVM (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 123)
***** error<ClassificationSVM: 'SVMtype' must be 'c_svc', 'nu_svc', or 'one_class_svm'.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "svmtype", 'some_type')
***** error<ClassificationSVM: 'OutlierFraction' must be a positive scalar in the range 0 =< OutlierFraction < 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "OutlierFraction", -1)
***** error<ClassificationSVM: 'KernelFunction' must be a character vector.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", 123)
***** error<ClassificationSVM: unsupported Kernel function.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "KernelFunction", "fcn")
***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", -1)
***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", 0.5)
***** error<ClassificationSVM: 'PolynomialOrder' must be a positive integer.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "PolynomialOrder", [1,2])
***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", -1)
***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", 0)
***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", [1, 2])
***** error<ClassificationSVM: 'KernelScale' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "KernelScale", "invalid")
***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", -1)
***** error<ClassificationSVM: 'KernelOffset' must be a non-negative scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "KernelOffset", [1,2])
***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", -1)
***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", 0)
***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", [1, 2])
***** error<ClassificationSVM: 'BoxConstraint' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones (10,1), "BoxConstraint", "invalid")
***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "nu", -0.5)
***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "nu", 0)
***** error<ClassificationSVM: 'Nu' must be a positive scalar in the range 0 < Nu <= 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "nu", 1.5)
***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", -1)
***** error<ClassificationSVM: 'CacheSize' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "CacheSize", [1,2])
***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", -0.1)
***** error<ClassificationSVM: 'Tolerance' must be a positive scalar.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "Tolerance", [0.1,0.2])
***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "shrinking", 2)
***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "shrinking", -1)
***** error<ClassificationSVM: 'Shrinking' must be either 0 or 1.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "shrinking", [1 0])
***** error<ClassificationSVM: invalid parameter name in optional pair arguments.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "invalid_name", 'c_svc')
***** error<ClassificationSVM: cannot train a binary problem with only one class available.> ...
 ClassificationSVM (ones(10,2), ones(10,1), "SVMtype", 'c_svc')
***** error<ClassificationSVM: can only be used for one-class or two-class learning.> ...
 ClassificationSVM (ones(10,2), [1;1;1;1;2;2;2;2;3;3])
***** error<ClassificationSVM: invalid values in X.> ...
 ClassificationSVM ([ones(9,2);2,Inf], ones(10,1))
***** shared x, y, x_train, x_test, y_train, y_test, objST
 load fisheriris
 inds = ! strcmp (species, 'setosa');
 x = meas(inds, 3:4);
 y = grp2idx (species(inds));
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcsvm (x, y, 'KernelFunction', 'rbf', 'Tolerance', 1e-7);
 assert (isempty (obj.Alpha), true)
 assert (sum (obj.IsSupportVector), numel (obj.Beta))
 [label, score] = predict (obj, xc);
 assert (label, [1; 2; 2]);
 assert (score(:,1), [0.99285; -0.080296; -0.93694], 2e-5);
 assert (score(:,1), -score(:,2), eps)
***** test
 obj = fitcsvm (x, y);
 assert (isempty (obj.Beta), true)
 assert (sum (obj.IsSupportVector), numel (obj.Alpha))
 assert (numel (obj.Alpha), 24)
 assert (obj.Bias, -14.415, 1e-3)
 xc = [min(x); mean(x); max(x)];
 label = predict (obj, xc);
 assert (label, [1; 2; 2]);
***** error<ClassificationSVM.predict: too few input arguments.> ...
 predict (ClassificationSVM (ones (40,2), ones (40,1)))
***** error<ClassificationSVM.predict: XC is empty.> ...
 predict (ClassificationSVM (ones (40,2), ones (40,1)), [])
***** error<ClassificationSVM.predict: XC must have the same number of predictors as the trained model.> ...
 predict (ClassificationSVM (ones (40,2), ones (40,1)), 1)
***** test
 objST = fitcsvm (x, y);
***** error<ClassificationSVM.subsasgn: 'ScoreTransform' must be a 'function_handle' object.> ...
 objST.ScoreTransform = "a";
 [labels, scores] = predict (objST, x);
 [labels, scores] = resubPredict (objST);
***** test
 rand ("seed", 1);
 CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15, ...
                       'Tolerance', 1e-7);
 obj = CVSVMModel.Trained{1};
 testInds = test (CVSVMModel.Partition);
 expected_margin = [2.0000;  0.8579;  1.6690;  3.4141;  3.4552; ...
                    2.6605;  3.5251; -4.0000; -6.3411; -6.4511; ...
                   -3.0532; -7.5054; -1.6700; -5.6227; -7.3640];
 computed_margin = margin (obj, x(testInds,:), y(testInds,:));
 assert (computed_margin, expected_margin, 1e-4);
***** error<ClassificationSVM.margin: too few input arguments.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
***** error<ClassificationSVM.margin: too few input arguments.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
***** error<ClassificationSVM.margin: X is empty.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
***** error<ClassificationSVM.margin: X must have the same number of predictors as the trained model.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
***** error<ClassificationSVM.margin: Y is empty.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
***** error<ClassificationSVM.margin: Y must have the same number of rows as X.> ...
 margin (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
***** test
 rand ("seed", 1);
 CVSVMModel = fitcsvm (x, y, 'KernelFunction', 'rbf', 'HoldOut', 0.15);
 obj = CVSVMModel.Trained{1};
 testInds = test (CVSVMModel.Partition);
 L1 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'binodeviance');
 L2 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'classiferror');
 L3 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'exponential');
 L4 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'hinge');
 L5 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'logit');
 L6 = loss (obj, x(testInds,:), y(testInds,:), 'LossFun', 'quadratic');
 assert (L1, 2.8711, 1e-4);
 assert (L2, 0.5333, 1e-4);
 assert (L3, 10.9685, 1e-4);
 assert (L4, 1.9827, 1e-4);
 assert (L5, 1.5849, 1e-4);
 assert (L6, 7.6739, 1e-4);
***** error<ClassificationSVM.loss: too few input arguments.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)))
***** error<ClassificationSVM.loss: too few input arguments.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2))
***** error<ClassificationSVM.loss: Name-Value arguments must be in pairs.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones(2,1), "LossFun")
***** error<ClassificationSVM.loss: X is empty.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
***** error<ClassificationSVM.loss: X must have the same number of predictors as the trained model.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
***** error<ClassificationSVM.loss: Y is empty.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), [])
***** error<ClassificationSVM.loss: Y must have the same number of rows as X.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), 1)
***** error<ClassificationSVM.loss: 'LossFun' must be a character vector.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "LossFun", 1)
***** error<ClassificationSVM.loss: unsupported Loss function.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "LossFun", "some")
***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "Weights", ['a','b'])
***** error<ClassificationSVM.loss: 'Weights' must be a numeric vector.> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "Weights", 'a')
***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "Weights", [1,2,3])
***** error<ClassificationSVM.loss: size of 'Weights' must be equal to the number> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "Weights", 3)
***** error<ClassificationSVM.loss: invalid parameter name in optional pair arg> ...
 loss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), zeros (2), ...
 ones (2,1), "some", "some")
***** error<ClassificationSVM.resubLoss: Name-Value arguments must be in pairs.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun")
***** error<ClassificationSVM.resubLoss: 'LossFun' must be a character vector.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", 1)
***** error<ClassificationSVM.resubLoss: unsupported Loss function.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "LossFun", "some")
***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", ['a','b'])
***** error<ClassificationSVM.resubLoss: 'Weights' must be a numeric vector.> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 'a')
***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", [1,2,3])
***** error<ClassificationSVM.resubLoss: size of 'Weights' must be equal to the n> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "Weights", 3)
***** error<ClassificationSVM.resubLoss: invalid parameter name in optional pai> ...
 resubLoss (ClassificationSVM (ones (40,2), randi ([1, 2], 40, 1)), "some", "some")
***** test
 SVMModel = fitcsvm (x, y);
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (SVMModel, "KFold", 5);
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
***** test
 obj = fitcsvm (x, y);
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (obj, "HoldOut", 0.2);
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
***** test
 obj = fitcsvm (x, y);
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (obj, "LeaveOut", 'on');
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationSVM")
 assert (CVMdl.CrossValidatedModel, "ClassificationSVM")
***** error<ClassificationSVM.crossval: Name-Value arguments must be in pairs.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold")
***** error<ClassificationSVM.crossval: specify only one of the optional Name-Value paired arguments.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), ...
 "KFold", 5, "leaveout", 'on')
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 'a')
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 1)
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", -1)
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", 11.5)
***** error<ClassificationSVM.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "KFold", [1,2])
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 'a')
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 11.5)
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", -1)
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 0)
***** error<ClassificationSVM.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Holdout", 1)
***** error<ClassificationSVM.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "Leaveout", 1)
***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 1)
***** error<ClassificationSVM.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "CVPartition", 'a')
***** error<ClassificationSVM.crossval: invalid parameter name in optional paired arguments> ...
 crossval (ClassificationSVM (ones (40,2),randi([1, 2], 40, 1)), "some", "some")
106 tests, 106 passed, 0 known failure, 0 skipped
[inst/Classification/ConfusionMatrixChart.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Classification/ConfusionMatrixChart.m
***** demo
 ## Create a simple ConfusionMatrixChart Object

 cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"})
 NormalizedValues = cm.NormalizedValues
 ClassLabels = cm.ClassLabels
***** test
 hf = figure ("visible", "off");
 unwind_protect
   cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"}, {"XLabel","LABEL A"});
   assert (isa (cm, "ConfusionMatrixChart"), true);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
1 test, 1 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationDiscriminant.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Classification/ClassificationDiscriminant.m
***** demo
 ## Create discriminant classifier
 ## Evaluate some model predictions on new data.

 load fisheriris
 x = meas;
 y = species;
 xc = [min(x); mean(x); max(x)];
 obj = fitcdiscr (x, y);
 [label, score, cost] = predict (obj, xc);
***** demo
 load fisheriris
 model = fitcdiscr (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 ## Compute loss for discriminant model
 L = loss (model, X, Y)
***** demo
 load fisheriris
 mdl = fitcdiscr (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 ## Margin for discriminant model
 m = margin (mdl, X, Y)
***** demo
 load fisheriris
 x = meas;
 y = species;
 obj = fitcdiscr (x, y, "gamma", 0.4);
 ## Cross-validation for discriminant model
 CVMdl = crossval (obj)
***** test
 load fisheriris
 x = meas;
 y = species;
 PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
 Mdl = ClassificationDiscriminant (x, y, "PredictorNames", PredictorNames);
 sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
          0.092721, 0.115388, 0.055244, 0.032710; ...
          0.167514, 0.055244, 0.185188, 0.042665; ...
          0.038401, 0.032710, 0.042665, 0.041882];
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
              -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
              -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
 assert (class (Mdl), "ClassificationDiscriminant");
 assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {x, y, 150})
 assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
 assert ({Mdl.Gamma, Mdl.MinGamma}, {0, 0}, 1e-15)
 assert (Mdl.ClassNames, unique (species))
 assert (Mdl.Sigma, sigma, 1e-6)
 assert (Mdl.Mu, mu, 1e-14)
 assert (Mdl.XCentered([1:3],:), xCentered, 1e-14)
 assert (Mdl.LogDetSigma, -9.9585, 1e-4)
 assert (Mdl.PredictorNames, PredictorNames)
***** test
 load fisheriris
 x = meas;
 y = species;
 Mdl = ClassificationDiscriminant (x, y, "Gamma", 0.5);
 sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
          0.046361, 0.115388, 0.027622, 0.016355; ...
          0.083757, 0.027622, 0.185188, 0.021333; ...
          0.019201, 0.016355, 0.021333, 0.041882];
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
              -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
              -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
 assert (class (Mdl), "ClassificationDiscriminant");
 assert ({Mdl.X, Mdl.Y, Mdl.NumObservations}, {x, y, 150})
 assert ({Mdl.DiscrimType, Mdl.ResponseName}, {"linear", "Y"})
 assert ({Mdl.Gamma, Mdl.MinGamma}, {0.5, 0})
 assert (Mdl.ClassNames, unique (species))
 assert (Mdl.Sigma, sigma, 1e-6)
 assert (Mdl.Mu, mu, 1e-14)
 assert (Mdl.XCentered([1:3],:), xCentered, 1e-14)
 assert (Mdl.LogDetSigma, -8.6884, 1e-4)
***** shared X, Y, MODEL
 X = rand (10,2);
 Y = [ones(5,1);2*ones(5,1)];
 MODEL = ClassificationDiscriminant (X, Y);
***** error<ClassificationDiscriminant: too few input arguments.> ClassificationDiscriminant ()
***** error<ClassificationDiscriminant: too few input arguments.> ...
 ClassificationDiscriminant (ones(4, 1))
***** error<ClassificationDiscriminant: Name-Value arguments must be in pairs.> ...
 ClassificationDiscriminant (X, Y, "prior")
***** error<ClassificationDiscriminant: number of rows in X and Y must be equal.> ...
 ClassificationDiscriminant (ones (4,2), ones (1,4))
***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationDiscriminant (X, Y, "PredictorNames", ["A"])
***** error<ClassificationDiscriminant: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationDiscriminant (X, Y, "PredictorNames", "A")
***** error<ClassificationDiscriminant: 'PredictorNames' must equal the number of columns in X.> ...
 ClassificationDiscriminant (X, Y, "PredictorNames", {"A", "B", "C"})
***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
 ClassificationDiscriminant (X, Y, "ResponseName", {"Y"})
***** error<ClassificationDiscriminant: 'ResponseName' must be a character vector.> ...
 ClassificationDiscriminant (X, Y, "ResponseName", 1)
***** error<ClassificationDiscriminant: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
 ClassificationDiscriminant (X, Y, "ClassNames", @(x)x)
***** error<ClassificationDiscriminant: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
 ClassificationDiscriminant (X, Y, "ClassNames", {1})
***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
 ClassificationDiscriminant (X, ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
 ClassificationDiscriminant ([1;2;3;4;5], ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
 ClassificationDiscriminant ([1;2;3;4;5], {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationDiscriminant: not all 'ClassNames' are present in Y.> ...
 ClassificationDiscriminant (X, logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationDiscriminant: 'Prior' must be either a numeric or a character vector.> ...
 ClassificationDiscriminant (X, Y, "Prior", {"1", "2"})
***** error<ClassificationDiscriminant: the elements in 'Prior' do not correspond to the selected classes in Y.> ...
 ClassificationDiscriminant (X, ones (10,1), "Prior", [1 2])
***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
 ClassificationDiscriminant (X, Y, "Cost", [1, 2])
***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
 ClassificationDiscriminant (X, Y, "Cost", "string")
***** error<ClassificationDiscriminant: 'Cost' must be a numeric square matrix.> ...
 ClassificationDiscriminant (X, Y, "Cost", {eye(2)})
***** error<ClassificationDiscriminant: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
 ClassificationDiscriminant (X, Y, "Cost", ones (3))
***** error<ClassificationDiscriminant: Predictor 'x1' has zero within-class variance.> ...
 ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2])
***** error<ClassificationDiscriminant: Predictor 'A' has zero within-class variance.> ...
 ClassificationDiscriminant (ones (5,2), [1; 1; 2; 2; 2], "PredictorNames", {"A", "B"})
***** error<ClassificationDiscriminant: Predictor 'x2' has zero within-class variance.> ...
 ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1))
***** error<ClassificationDiscriminant: Predictor 'B' has zero within-class variance.> ...
 ClassificationDiscriminant ([1,2;2,2;3,2;4,2;5,2], ones (5, 1), "PredictorNames", {"A", "B"})
***** test
 load fisheriris
 x = meas;
 y = species;
 Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
 [label, score, cost] = predict (Mdl, [2, 2, 2, 2]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.9999, 0.0001], 1e-4)
 assert (cost, [1, 0.0001, 0.9999], 1e-4)
 [label, score, cost] = predict (Mdl, [2.5, 2.5, 2.5, 2.5]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.6368, 0.3632], 1e-4)
 assert (cost, [1, 0.3632, 0.6368], 1e-4)
***** test
 load fisheriris
 x = meas;
 y = species;
 xc = [min(x); mean(x); max(x)];
 Mdl = fitcdiscr (x, y);
 [label, score, cost] = predict (Mdl, xc);
 l = {'setosa'; 'versicolor'; 'virginica'};
 s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
 c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
 assert (label, l)
 assert (score, s, 1e-4)
 assert (cost, c, 1e-4)
***** error<ClassificationDiscriminant.predict: too few input arguments.> ...
 predict (MODEL)
***** error<ClassificationDiscriminant.predict: XC is empty.> ...
 predict (MODEL, [])
***** error<ClassificationDiscriminant.predict: XC must have the same number of predictors as the trained model.> ...
 predict (MODEL, 1)
***** test
 load fisheriris
 model = fitcdiscr (meas, species);
 x = mean (meas);
 y = {'versicolor'};
 L = loss (model, x, y);
 assert (L, 0)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y, "Gamma", 0.4);
 x_test = [1, 6; 3, 3];
 y_test = {'A'; 'B'};
 L = loss (model, x_test, y_test);
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3];
 y_test = ['1'];
 L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
 assert (L, 0.2423, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 L = loss (model, x_test, y_test, 'LossFun', 'hinge');
 assert (L, 0.5886, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 W = [1; 2];
 L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
 assert (L, 0.5107, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_with_nan = [1, 2; NaN, 4];
 y_test = {'A'; 'B'};
 L = loss (model, x_with_nan, y_test);
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y);
 x_with_nan = [1, 2; NaN, 4];
 y_test = {'A'; 'B'};
 L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
 assert (isnan (L))
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y);
 customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
 L = loss (model, x, y, 'LossFun', customLossFun);
 assert (L, 0.8889, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = [1; 2; 1];
 model = fitcdiscr (x, y);
 L = loss (model, x, y, 'LossFun', 'classiferror');
 assert (L, 0.3333, 1e-4)
***** error<ClassificationDiscriminant.loss: too few input arguments.> ...
 loss (MODEL)
***** error<ClassificationDiscriminant.loss: too few input arguments.> ...
 loss (MODEL, ones (4,2))
***** error<ClassificationDiscriminant.loss: X is empty.> ...
 loss (MODEL, [], zeros (2))
***** error<ClassificationDiscriminant.loss: X must have the same number of predictors as the trained model.> ...
 loss (MODEL, 1, zeros (2))
***** error<ClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
***** error<ClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
 loss (MODEL, ones (4,2), ones (3,1))
***** error<ClassificationDiscriminant.loss: invalid loss function.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
***** error<ClassificationDiscriminant.loss: invalid 'Weights'.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
 load fisheriris
 mdl = fitcdiscr (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 m = margin (mdl, X, Y);
 assert (m, 1, 1e-6)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 1];
 mdl = fitcdiscr (X, Y, "gamma", 0.5);
 m = margin (mdl, X, Y);
 assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
***** error<ClassificationDiscriminant.margin: too few input arguments.> ...
 margin (MODEL)
***** error<ClassificationDiscriminant.margin: too few input arguments.> ...
 margin (MODEL, ones (4,2))
***** error<ClassificationDiscriminant.margin: X is empty.> ...
 margin (MODEL, [], zeros (2))
***** error<ClassificationDiscriminant.margin: X must have the same number of predictors as the trained model.> ...
 margin (MODEL, 1, zeros (2))
***** error<ClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
 margin (MODEL, ones (4,2), ones (3,1))
***** shared x, y, obj
 load fisheriris
 x = meas;
 y = species;
 obj = fitcdiscr (x, y, "gamma", 0.4);
***** test
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (obj);
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 10)
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** test
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (obj, "KFold", 3);
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 3)
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** test
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (obj, "HoldOut", 0.2);
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** test
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (obj, "LeaveOut", 'on');
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** test
 status = warning;
 warning ('off');
 rand ("seed", 23);
 partition = cvpartition (y, 'KFold', 3);
 warning (status);
 CVMdl = crossval (obj, 'cvPartition', partition);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert (CVMdl.KFold == 3)
 assert (class (CVMdl.Trained{1}), "CompactClassificationDiscriminant")
 assert (CVMdl.CrossValidatedModel, "ClassificationDiscriminant")
***** error<ClassificationDiscriminant.crossval: Name-Value arguments must be in pairs.> ...
 crossval (obj, "kfold")
***** error<ClassificationDiscriminant.crossval: specify only one of the optional Name-Value paired arguments.>...
 crossval (obj, "kfold", 12, "holdout", 0.2)
***** error<ClassificationDiscriminant.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (obj, "kfold", 'a')
***** error<ClassificationDiscriminant.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (obj, "holdout", 2)
***** error<ClassificationDiscriminant.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (obj, "leaveout", 1)
***** error<ClassificationDiscriminant.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (obj, "cvpartition", 1)
67 tests, 67 passed, 0 known failure, 0 skipped
[inst/Classification/CompactClassificationNeuralNetwork.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Classification/CompactClassificationNeuralNetwork.m
***** demo
 ## Create a neural network classifier and its compact version
 # and compare their size

 load fisheriris
 X = meas;
 Y = species;

 Mdl = fitcnet (X, Y, 'ClassNames', unique (species))
 CMdl = crossval (Mdl)
***** error<CompactClassificationDiscriminant: invalid classification object.> ...
 CompactClassificationDiscriminant (1)
***** shared x, y, CMdl
 load fisheriris
 x = meas;
 y = grp2idx (species);
 Mdl = fitcnet (x, y, "IterationLimit", 100);
 CMdl = compact (Mdl);
***** error<CompactClassificationNeuralNetwork.predict: too few input arguments.> ...
 predict (CMdl)
***** error<CompactClassificationNeuralNetwork.predict: XC is empty.> ...
 predict (CMdl, [])
***** error<CompactClassificationNeuralNetwork.predict: XC must have the same number of predictors as the trained neural network.> ...
 predict (CMdl, 1)
***** error<CompactClassificationNeuralNetwork: unrecognized 'ScoreTransform' function.> ...
 CMdl.ScoreTransform = "a";
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/Classification/ClassificationKNN.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Classification/ClassificationKNN.m
***** demo
 ## Create a k-nearest neighbor classifier for Fisher's iris data with k = 5.
 ## Evaluate some model predictions on new data.

 load fisheriris
 x = meas;
 y = species;
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
 [label, score, cost] = predict (obj, xc)
***** demo
 load fisheriris
 x = meas;
 y = species;
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);

 ## Create a cross-validated model
 CVMdl = crossval (obj)
***** demo
 load fisheriris
 x = meas;
 y = species;
 covMatrix = cov (x);

 ## Fit the k-NN model using the 'mahalanobis' distance
 ## and the custom covariance matrix
 obj = fitcknn(x, y, 'NumNeighbors', 5, 'Distance','mahalanobis', ...
 'Cov', covMatrix);

 ## Create a partition model using cvpartition
 Partition = cvpartition (size (x, 1), 'kfold', 12);

 ## Create cross-validated model using 'cvPartition' name-value argument
 CVMdl = crossval (obj, 'cvPartition', Partition)

 ## Access the trained model from first fold of cross-validation
 CVMdl.Trained{1}
***** demo
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
 ## Calculate loss using custom loss function
 L = loss (model, X, Y, 'LossFun', customLossFun)
***** demo
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 ## Calculate loss using 'mincost' loss function
 L = loss (model, X, Y, 'LossFun', 'mincost')
***** demo
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3; 5, 7];
 Y_test = ['1'; '2'];
 ## Specify custom Weights
 W = [1; 2];
 L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
***** demo
 load fisheriris
 mdl = fitcknn (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 m = margin (mdl, X, Y)
***** demo
 X = [1, 2; 4, 5; 7, 8; 3, 2];
 Y = [2; 1; 3; 2];
 ## Train the model
 mdl = fitcknn (X, Y);
 ## Specify Vars and Labels
 Vars = 1;
 Labels = 2;
 ## Calculate partialDependence
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
***** demo
 X = [1, 2; 4, 5; 7, 8; 3, 2];
 Y = [2; 1; 3; 2];
 ## Train the model
 mdl = fitcknn (X, Y);
 ## Specify Vars and Labels
 Vars = 1;
 Labels = 1;
 queryPoints = [linspace(0, 1, 3)', linspace(0, 1, 3)'];
 ## Calculate partialDependence using queryPoints
 [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
 queryPoints)
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = ClassificationKNN (x, y, "NumNeighbors" ,k);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = ones (4, 11);
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = ClassificationKNN (x, y, "NumNeighbors" ,k);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = ClassificationKNN (x, y, "NumNeighbors" ,k, "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 k = 10;
 a = ClassificationKNN (x, y, "NumNeighbors" ,k, "Distance", "hamming");
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 10})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 weights = ones (4,1);
 a = ClassificationKNN (x, y, "Standardize", 1);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.Standardize}, {true})
 assert ({a.Sigma}, {std(x, [], 1)})
 assert ({a.Mu}, {[3.75, 4.25, 4.75]})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 weights = ones (4,1);
 a = ClassificationKNN (x, y, "Standardize", false);
 assert (class (a), "ClassificationKNN");
 assert ({a.X, a.Y, a.NumNeighbors}, {x, y, 1})
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.Standardize}, {false})
 assert ({a.Sigma}, {[]})
 assert ({a.Mu}, {[]})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 s = ones (1, 3);
 a = ClassificationKNN (x, y, "Scale" , s, "Distance", "seuclidean");
 assert (class (a), "ClassificationKNN");
 assert ({a.DistParameter}, {s})
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "seuclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski");
 assert (class (a), "ClassificationKNN");
 assert (a.DistParameter, 5)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "minkowski"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "Exponent" , 5, "Distance", "minkowski", ...
                        "NSMethod", "exhaustive");
 assert (class (a), "ClassificationKNN");
 assert (a.DistParameter, 5)
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "minkowski"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "BucketSize" , 20, "distance", "mahalanobis");
 assert (class (a), "ClassificationKNN");
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "mahalanobis"})
 assert ({a.BucketSize}, {20})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y, "IncludeTies", true);
 assert (class (a), "ClassificationKNN");
 assert (a.IncludeTies, true);
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y);
 assert (class (a), "ClassificationKNN");
 assert (a.IncludeTies, false);
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 a = ClassificationKNN (x, y);
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, [0.5; 0.5])
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 prior = [0.5; 0.5];
 a = ClassificationKNN (x, y, "Prior", "empirical");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "a"; "b"];
 prior = [0.75; 0.25];
 a = ClassificationKNN (x, y, "Prior", "empirical");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "a"; "b"];
 prior = [0.5; 0.5];
 a = ClassificationKNN (x, y, "Prior", "uniform");
 assert (class (a), "ClassificationKNN")
 assert (a.Prior, prior)
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 cost = eye (2);
 a = ClassificationKNN (x, y, "Cost", cost);
 assert (class (a), "ClassificationKNN")
 assert (a.Cost, [1, 0; 0, 1])
 assert ({a.NSMethod, a.Distance}, {"kdtree", "euclidean"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2, 3; 4, 5, 6; 7, 8, 9; 3, 2, 1];
 y = ["a"; "a"; "b"; "b"];
 cost = eye (2);
 a = ClassificationKNN (x, y, "Cost", cost, "Distance", "hamming" );
 assert (class (a), "ClassificationKNN")
 assert (a.Cost, [1, 0; 0, 1])
 assert ({a.NSMethod, a.Distance}, {"exhaustive", "hamming"})
 assert ({a.BucketSize}, {50})
***** test
 x = [1, 2; 3, 4; 5,6; 5, 8];
 y = {'9'; '9'; '6'; '7'};
 a = ClassificationKNN (x, y);
 assert (a.Prior, [0.25; 0.25; 0.5])
***** test
 load fisheriris
 x = meas;
 y = species;
 ClassNames = {'setosa', 'versicolor', 'virginica'};
 a = ClassificationKNN (x, y, 'ClassNames', ClassNames);
 assert (a.ClassNames, ClassNames')
***** error<ClassificationKNN: too few input arguments.> ClassificationKNN ()
***** error<ClassificationKNN: too few input arguments.> ...
 ClassificationKNN (ones(4, 1))
***** error<ClassificationKNN: number of rows in X and Y must be equal.> ...
 ClassificationKNN (ones (4,2), ones (1,4))
***** error<ClassificationKNN: 'Standardize' must be either true or false.> ...
 ClassificationKNN (ones (5,3), ones (5,1), "standardize", "a")
***** error<ClassificationKNN: 'Standardize' cannot simultaneously be specified with> ...
 ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "standardize", true)
***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", ["A"])
***** error<ClassificationKNN: 'PredictorNames' must be supplied as a cellstring array.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", "A")
***** error<ClassificationKNN: 'PredictorNames' must have the same number of columns as X.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "PredictorNames", {"A", "B", "C"})
***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", {"Y"})
***** error<ClassificationKNN: 'ResponseName' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "ResponseName", 1)
***** error<ClassificationKNN: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
 ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", @(x)x)
***** error<ClassificationKNN: 'ClassNames' must be a cell array of character vectors, a logical vector, a numeric vector, or a character array.> ...
 ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", {1})
***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
 ClassificationKNN (ones(10,2), ones (10,1), "ClassNames", [1, 2])
***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
 ClassificationKNN (ones(5,2), ['a';'b';'a';'a';'b'], "ClassNames", ['a';'c'])
***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
 ClassificationKNN (ones(5,2), {'a';'b';'a';'a';'b'}, "ClassNames", {'a','c'})
***** error<ClassificationKNN: not all 'ClassNames' are present in Y.> ...
 ClassificationKNN (ones(10,2), logical (ones (10,1)), "ClassNames", [true, false])
***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", 1)
***** error<ClassificationKNN: 'BreakTies' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", {"1"})
***** error<ClassificationKNN: invalid value for 'BreakTies'.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BreakTies", "some")
***** error<ClassificationKNN: 'Prior' must be either a numeric vector or a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Prior", {"1", "2"})
***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1, 2])
***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cost", "string")
***** error<ClassificationKNN: 'Cost' must be a numeric square matrix.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cost", {eye(2)})
***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 0)
***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", 15.2)
***** error<ClassificationKNN: 'NumNeighbors' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NumNeighbors", "asd")
***** error<ClassificationKNN: unsupported distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", "somemetric")
***** error<ClassificationKNN: invalid function handle for distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
                    @(v,m)sqrt(repmat(v,rows(m),1)-m,2))
***** error<ClassificationKNN: custom distance function produces wrong output size.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", ...
                    @(v,m)sqrt(sum(sumsq(repmat(v,rows(m),1)-m,2))))
***** error<ClassificationKNN: invalid distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", [1 2 3])
***** error<ClassificationKNN: invalid distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", {"mahalanobis"})
***** error<ClassificationKNN: invalid distance metric.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", logical (5))
***** error<ClassificationKNN: function handle for distance weight must return the> ...
 ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", @(x)sum(x))
***** error<ClassificationKNN: invalid distance weight.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", "text")
***** error<ClassificationKNN: invalid distance weight.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "DistanceWeight", [1 2 3])
***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", "scale")
***** error<ClassificationKNN: 'Scale' must be a numeric vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", {[1 2 3]})
***** error<ClassificationKNN: 'Scale' cannot simultaneously be specified with> ...
 ClassificationKNN (ones (5,2), ones (5,1), "standardize", true, "scale", [1 1])
***** error<ClassificationKNN: 'Cov' must be a symmetric positive definite matrix.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cov", ones (2), "Distance", "mahalanobis")
***** error<ClassificationKNN: 'Cov' cannot simultaneously be specified with> ...
 ClassificationKNN (ones (5,2), ones (5,1), "scale", [1 1], "Cov", ones (2))
***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 12.5)
***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", -3)
***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", "three")
***** error<ClassificationKNN: 'Exponent' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", {3})
***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", {"kdtree"})
***** error<ClassificationKNN: 'NSMethod' must be a character vector.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", 3)
***** error<ClassificationKNN: 'NSMethod' must be either 'kdtree' or 'exhaustive'.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "NSMethod", "some")
***** error<ClassificationKNN: 'IncludeTies' must be either true or false.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "IncludeTies", "some")
***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", 42.5)
***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", -50)
***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", "some")
***** error<ClassificationKNN: 'BucketSize' must be a positive integer.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "BucketSize", {50})
***** error<ClassificationKNN: invalid parameter name in optional pair arguments.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "some", "some")
***** error<ClassificationKNN: invalid values in X.> ...
 ClassificationKNN ([1;2;3;'a';4], ones (5,1))
***** error<ClassificationKNN: invalid values in X.> ...
 ClassificationKNN ([1;2;3;Inf;4], ones (5,1))
***** error<ClassificationKNN: the elements in 'Prior' do not correspond to the selected classes in Y.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Prior", [1 2])
***** error<ClassificationKNN: the number of rows and columns in 'Cost' must correspond to selected classes in Y.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cost", [1 2; 1 3])
***** error<ClassificationKNN: 'Scale' is only valid when distance metric is seuclidean.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1])
***** error<ClassificationKNN: 'Scale' vector must have equal length to the number of columns in X.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 1 1], "Distance", "seuclidean")
***** error<ClassificationKNN: 'Scale' vector must contain nonnegative scalar values.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Scale", [1 -1], "Distance", "seuclidean")
***** error<ClassificationKNN: 'Cov' is only valid when distance metric is 'mahalanobis'.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (2))
***** error<ClassificationKNN: 'Cov' matrix must have equal columns as X.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Cov", eye (3), "Distance", "mahalanobis")
***** error<ClassificationKNN: 'Exponent' is only valid when distance metric is 'minkowski'.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Exponent", 3)
***** error<ClassificationKNN: 'kdtree' method is only valid for 'euclidean', 'cityblock', 'manhattan', 'minkowski', and 'chebychev' distance metrics.> ...
 ClassificationKNN (ones (5,2), ones (5,1), "Distance", "hamming", "NSMethod", "kdtree")
***** shared x, y
 load fisheriris
 x = meas;
 y = species;
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 5);
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"; "versicolor"; "virginica"})
 assert (s, [1, 0, 0; 0, 1, 0; 0, 0, 1])
 assert (c, [0, 1, 1; 1, 0, 1; 1, 1, 0])
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 5, "Standardize", 1);
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"; "versicolor"; "virginica"})
 assert (s, [0.4, 0.6, 0; 0, 1, 0; 0, 0, 1])
 assert (c, [0.6, 0.4, 1; 1, 0, 1; 1, 1, 0])
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
 [l, s, c] = predict (obj, xc);
 assert (s, [0.3, 0.7, 0; 0, 0.9, 0.1; 0.2, 0.2, 0.6], 1e-4)
 assert (c, [0.7, 0.3, 1; 1, 0.1, 0.9; 0.8, 0.8, 0.4], 1e-4)
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"; "versicolor"; "virginica"})
 assert (s, [1, 0, 0; 0, 1, 0; 0, 0.3, 0.7], 1e-4)
 assert (c, [0, 1, 1; 1, 0, 1; 1, 0.7, 0.3], 1e-4)
***** test
 xc = [5.2, 4.1, 1.5, 0.1; 5.1, 3.8, 1.9, 0.4; ...
         5.1, 3.8, 1.5, 0.3; 4.9, 3.6, 1.4, 0.1];
 obj = fitcknn (x, y, "NumNeighbors", 5);
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"; "setosa"; "setosa"; "setosa"})
 assert (s, [1, 0, 0; 1, 0, 0; 1, 0, 0; 1, 0, 0])
 assert (c, [0, 1, 1; 0, 1, 1; 0, 1, 1; 0, 1, 1])
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 5);
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 0.6, 0.4], 1e-4)
 assert (c, [1, 0.4, 0.6], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "minkowski", "Exponent", 5);
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 0.5, 0.5], 1e-4)
 assert (c, [1, 0.5, 0.5], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "jaccard");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"})
 assert (s, [0.9, 0.1, 0], 1e-4)
 assert (c, [0.1, 0.9, 1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "mahalanobis");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0.1000, 0.5000, 0.4000], 1e-4)
 assert (c, [0.9000, 0.5000, 0.6000], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "jaccard");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"})
 assert (s, [0.8, 0.2, 0], 1e-4)
 assert (c, [0.2, 0.8, 1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "seuclidean");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 1, 0], 1e-4)
 assert (c, [1, 0, 1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "chebychev");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 0.7, 0.3], 1e-4)
 assert (c, [1, 0.3, 0.7], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cityblock");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 0.6, 0.4], 1e-4)
 assert (c, [1, 0.4, 0.6], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "cosine");
 [l, s, c] = predict (obj, xc);
 assert (l, {"virginica"})
 assert (s, [0, 0.1, 0.9], 1e-4)
 assert (c, [1, 0.9, 0.1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
 [l, s, c] = predict (obj, xc);
 assert (l, {"virginica"})
 assert (s, [0, 0.1, 0.9], 1e-4)
 assert (c, [1, 0.9, 0.1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "spearman");
 [l, s, c] = predict (obj, xc);
 assert (l, {"versicolor"})
 assert (s, [0, 1, 0], 1e-4)
 assert (c, [1, 0, 1], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 30, "distance", "hamming");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"})
 assert (s, [0.4333, 0.3333, 0.2333], 1e-4)
 assert (c, [0.5667, 0.6667, 0.7667], 1e-4)
***** test
 xc = [5, 3, 5, 1.45];
 obj = fitcknn (x, y, "NumNeighbors", 5, "distance", "hamming");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"})
 assert (s, [0.8, 0.2, 0], 1e-4)
 assert (c, [0.2, 0.8, 1], 1e-4)
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "correlation");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa"; "versicolor"; "virginica"})
 assert (s, [1, 0, 0; 0, 1, 0; 0, 0.4, 0.6], 1e-4)
 assert (c, [0, 1, 1; 1, 0, 1; 1, 0.6, 0.4], 1e-4)
***** test
 xc = [min(x); mean(x); max(x)];
 obj = fitcknn (x, y, "NumNeighbors", 10, "distance", "hamming");
 [l, s, c] = predict (obj, xc);
 assert (l, {"setosa";"setosa";"setosa"})
 assert (s, [0.9, 0.1, 0; 1, 0, 0; 0.5, 0, 0.5], 1e-4)
 assert (c, [0.1, 0.9, 1; 0, 1, 1; 0.5, 1, 0.5], 1e-4)
***** error<ClassificationKNN.predict: too few input arguments.> ...
 predict (ClassificationKNN (ones (4,2), ones (4,1)))
***** error<ClassificationKNN.predict: XC is empty.> ...
 predict (ClassificationKNN (ones (4,2), ones (4,1)), [])
***** error<ClassificationKNN.predict: XC must have the same number of predictors as the trained model.> ...
 predict (ClassificationKNN (ones (4,2), ones (4,1)), 1)
***** test
 load fisheriris
 model = fitcknn (meas, species, 'NumNeighbors', 5);
 X = mean (meas);
 Y = {'versicolor'};
 L = loss (model, X, Y);
 assert (L, 0)
***** test
 load fisheriris
 model = fitcknn (meas, species, 'NumNeighbors', 5);
 L = loss (model, meas, species, 'LossFun', 'binodeviance');
 assert (L, 0.1413, 1e-4)
***** test
 load fisheriris
 model = fitcknn (meas, species);
 L = loss (model, meas, species, 'LossFun', 'binodeviance');
 assert (L, 0.1269, 1e-4)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 X_test = [1, 6; 3, 3];
 Y_test = {'A'; 'B'};
 L = loss (model, X_test, Y_test);
 assert (abs (L - 0.6667) > 1e-5)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 X_with_nan = [1, 2; NaN, 4];
 Y_test = {'A'; 'B'};
 L = loss (model, X_with_nan, Y_test);
 assert (abs (L - 0.3333) < 1e-4)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 X_with_nan = [1, 2; NaN, 4];
 Y_test = {'A'; 'B'};
 L = loss (model, X_with_nan, Y_test, 'LossFun', 'logit');
 assert (isnan (L))
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = {'A'; 'B'; 'A'};
 model = fitcknn (X, Y);
 customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
 L = loss (model, X, Y, 'LossFun', customLossFun);
 assert (L, 0)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 1];
 model = fitcknn (X, Y);
 L = loss (model, X, Y, 'LossFun', 'classiferror');
 assert (L, 0)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [true; false; true];
 model = fitcknn (X, Y);
 L = loss (model, X, Y, 'LossFun', 'binodeviance');
 assert (abs (L - 0.1269) < 1e-4)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '1'];
 model = fitcknn (X, Y);
 L = loss (model, X, Y, 'LossFun', 'classiferror');
 assert (L, 0)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3];
 Y_test = ['1'];
 L = loss (model, X_test, Y_test, 'LossFun', 'quadratic');
 assert (L, 1)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3; 5, 7];
 Y_test = ['1'; '2'];
 L = loss (model, X_test, Y_test, 'LossFun', 'classifcost');
 assert (L, 1)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3; 5, 7];
 Y_test = ['1'; '2'];
 L = loss (model, X_test, Y_test, 'LossFun', 'hinge');
 assert (L, 1)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = ['1'; '2'; '3'];
 model = fitcknn (X, Y);
 X_test = [3, 3; 5, 7];
 Y_test = ['1'; '2'];
 W = [1; 2];
 L = loss (model, X_test, Y_test, 'LossFun', 'logit', 'Weights', W);
 assert (abs (L - 0.6931) < 1e-4)
***** error<ClassificationKNN.loss: too few input arguments.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)))
***** error<ClassificationKNN.loss: too few input arguments.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
***** error<ClassificationKNN.loss: X is empty.> ...
 loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
***** error<ClassificationKNN.loss: X must have the same number of predictors as the trained model.> ...
 loss (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
***** error<ClassificationKNN.loss: name-value arguments must be in pairs.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
        ones (4,1), 'LossFun')
***** error<ClassificationKNN.loss: Y must have the same number of rows as X.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
***** error<ClassificationKNN.loss: invalid loss function.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
        ones (4,1), 'LossFun', 'a')
***** error<ClassificationKNN.loss: invalid Weights.> ...
 loss (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ...
        ones (4,1), 'Weights', 'w')
***** test
 load fisheriris
 mdl = fitcknn (meas, species, 'NumNeighbors', 5);
 X = mean (meas);
 Y = {'versicolor'};
 m = margin (mdl, X, Y);
 assert (m, 1)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 3];
 mdl = fitcknn (X, Y);
 m = margin (mdl, X, Y);
 assert (m, [1; 1; 1])
***** test
 X = [7, 8; 9, 10];
 Y = ['1'; '2'];
 mdl = fitcknn (X, Y);
 m = margin (mdl, X, Y);
 assert (m, [1; 1])
***** test
 X = [11, 12];
 Y = {'1'};
 mdl = fitcknn (X, Y);
 m = margin (mdl, X, Y);
 assert (isnan (m))
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 3];
 mdl = fitcknn (X, Y);
 X1 = [15, 16];
 Y1 = [1];
 m = margin (mdl, X1, Y1);
 assert (m, -1)
***** error<ClassificationKNN.margin: too few input arguments.> ...
 margin (ClassificationKNN (ones (4,2), ones (4,1)))
***** error<ClassificationKNN.margin: too few input arguments.> ...
 margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2))
***** error<ClassificationKNN.margin: X is empty.> ...
 margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), [], zeros (2))
***** error<ClassificationKNN.margin: X must have the same number of predictors as the trained model.> ...
 margin (ClassificationKNN (ones (40,2), randi ([1, 2], 40, 1)), 1, zeros (2))
***** error<ClassificationKNN.margin: Y must have the same number of rows as X.> ...
 margin (ClassificationKNN (ones (4,2), ones (4,1)), ones (4,2), ones (3,1))
***** shared X, Y, mdl
 X = [1, 2; 4, 5; 7, 8; 3, 2];
 Y = [2; 1; 3; 2];
 mdl = fitcknn (X, Y);
***** test
 Vars = 1;
 Labels = 2;
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
 pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000];
 assert (pd, pdm)
***** test
 Vars = 1;
 Labels = 2;
 [pd, x, y] = partialDependence (mdl, Vars, Labels, ...
 'NumObservationsToSample', 5);
 pdm = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
 assert (abs (pdm - pd) < 1)
***** test
 Vars = 1;
 Labels = 2;
 [pd, x, y] = partialDependence (mdl, Vars, Labels, 'UseParallel', true);
 pdm = [0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000];
 assert (pd, pdm)
***** test
 Vars = [1, 2];
 Labels = 1;
 queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
 [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', ...
                            queryPoints, 'UseParallel', true);
 pdm = [0, 0, 0; 0, 0, 0; 0, 0, 0];
 assert (pd, pdm)
***** test
 Vars = 1;
 Labels = [1; 2];
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
 pdm = [0.2500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, 0.2500, ...
 0.2500, 0.2500; 0.7500, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, ...
 0.5000, 0.5000, 0.5000];
 assert (pd, pdm)
***** test
 Vars = [1, 2];
 Labels = [1; 2];
 queryPoints = {linspace(0, 1, 3)', linspace(0, 1, 3)'};
 [pd, x, y] = partialDependence (mdl, Vars, Labels, 'QueryPoints', queryPoints);
 pdm(:,:,1) = [0, 0, 0; 1, 1, 1];
 pdm(:,:,2) = [0, 0, 0; 1, 1, 1];
 pdm(:,:,3) = [0, 0, 0; 1, 1, 1];
 assert (pd, pdm)
***** test
 X1 = [1; 2; 4; 5; 7; 8; 3; 2];
 X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
 X = [X1, double(X2)];
 Y = [1; 2; 3; 3; 2; 1; 2; 1];
 mdl = fitcknn (X, Y, 'ClassNames', {'1', '2', '3'});
 Vars = 1;
 Labels = 1;
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
 pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
 0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
 0.7500, 0.7500, 0.7500];
 assert (pd, pdm)
***** test
 X1 = [1; 2; 4; 5; 7; 8; 3; 2];
 X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
 X = [X1, double(X2)];
 Y = [1; 2; 3; 3; 2; 1; 2; 1];
 predictorNames = {'Feature1', 'Feature2'};
 mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
 Vars = 'Feature1';
 Labels = 1;
 [pd, x, y] = partialDependence (mdl, Vars, Labels);
 pdm = [1.0000, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, ...
 0.6250, 0.6250, 0.6250, 0.6250, 0.6250, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3750, ...
 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, 0.3750, ...
 0.3750, 0.3750, 0.3750, 0.3750, 0.7500, 0.7500, 0.7500, 0.7500, 0.7500, ...
 0.7500, 0.7500, 0.7500];
 assert (pd, pdm)
***** test
 X1 = [1; 2; 4; 5; 7; 8; 3; 2];
 X2 = ['2'; '3'; '1'; '3'; '1'; '3'; '2'; '2'];
 X = [X1, double(X2)];
 Y = [1; 2; 3; 3; 2; 1; 2; 1];
 predictorNames = {'Feature1', 'Feature2'};
 mdl = fitcknn (X, Y, 'PredictorNames', predictorNames);
 new_X1 = [10; 5; 6; 8; 9; 20; 35; 6];
 new_X2 = ['2'; '2'; '1'; '2'; '1'; '3'; '3'; '2'];
 new_X = [new_X1, double(new_X2)];
 Vars = 'Feature1';
 Labels = 1;
 [pd, x, y] = partialDependence (mdl, Vars, Labels, new_X);
 pdm = [0, 0, 0, 0, 0, 0.2500, 0.2500, 0.2500, 0.2500, 0.7500, 0.7500, ...
 0.7500, 0.7500, 0.7500, 0.7500, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, ...
 1.0000, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1];
 assert (pd, pdm)
***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
 partialDependence (ClassificationKNN (ones (4,2), ones (4,1)))
***** error<ClassificationKNN.partialDependence: too few input arguments.> ...
 partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1)
***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
 partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
 ones (4,1), 'NumObservationsToSample')
***** error<ClassificationKNN.partialDependence: name-value arguments must be in pairs.> ...
 partialDependence (ClassificationKNN (ones (4,2), ones (4,1)), 1, ...
 ones (4,1), 2)
***** shared x, y, obj
 load fisheriris
 x = meas;
 y = species;
 covMatrix = cov (x);
 obj = fitcknn (x, y, 'NumNeighbors', 5, 'Distance', ...
      'mahalanobis', 'Cov', covMatrix);
***** test
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (obj);
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 10)
 assert (CVMdl.ModelParameters.NumNeighbors == 5)
 assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (!CVMdl.ModelParameters.Standardize)
***** test
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (obj, "KFold", 5);
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.KFold == 5)
 assert (CVMdl.ModelParameters.NumNeighbors == 5)
 assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
***** test
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (obj, "HoldOut", 0.2);
 warning (status);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.ModelParameters.NumNeighbors == 5)
 assert (strcmp (CVMdl.ModelParameters.Distance, "mahalanobis"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
***** test
 obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
 status = warning;
 warning ('off');
 rand ("seed", 23);
 CVMdl = crossval (obj, "HoldOut", 0.2);
 warning (status);
 CVMdl = crossval (obj, "LeaveOut", 'on');
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert ({CVMdl.X, CVMdl.Y}, {x, y})
 assert (CVMdl.ModelParameters.NumNeighbors == 10)
 assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
***** test
 obj = fitcknn (x, y, "NumNeighbors", 10, "Distance", "cityblock");
 status = warning;
 warning ('off');
 rand ("seed", 23);
 partition = cvpartition (y, 'KFold', 3);
 warning (status);
 CVMdl = crossval (obj, 'cvPartition', partition);
 assert (class (CVMdl), "ClassificationPartitionedModel")
 assert (CVMdl.KFold == 3)
 assert (CVMdl.ModelParameters.NumNeighbors == 10)
 assert (strcmp (CVMdl.ModelParameters.Distance, "cityblock"))
 assert (class (CVMdl.Trained{1}), "ClassificationKNN")
 assert (CVMdl.ModelParameters.Standardize == obj.Standardize)
***** error<ClassificationKNN.crossval: Name-Value arguments must be in pairs.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold")
***** error<ClassificationKNN.crossval: specify only one of the optional Name-Value paired arguments.>...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 12, "holdout", 0.2)
***** error<ClassificationKNN.crossval: 'KFold' must be an integer value greater than 1.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "kfold", 'a')
***** error<ClassificationKNN.crossval: 'Holdout' must be a numeric value between 0 and 1.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "holdout", 2)
***** error<ClassificationKNN.crossval: 'Leaveout' must be either 'on' or 'off'.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "leaveout", 1)
***** error<ClassificationKNN.crossval: 'CVPartition' must be a 'cvpartition' object.> ...
 crossval (ClassificationKNN (ones (4,2), ones (4,1)), "cvpartition", 1)
165 tests, 165 passed, 0 known failure, 0 skipped
[inst/Classification/CompactClassificationDiscriminant.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/Classification/CompactClassificationDiscriminant.m
***** demo
 ## Create a discriminant analysis classifier and its compact version
 # and compare their size

 load fisheriris
 X = meas;
 Y = species;

 Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
 CMdl = crossval (Mdl)
***** test
 load fisheriris
 x = meas;
 y = species;
 PredictorNames = {'Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width'};
 Mdl = fitcdiscr (x, y, "PredictorNames", PredictorNames);
 CMdl = compact (Mdl);
 sigma = [0.265008, 0.092721, 0.167514, 0.038401; ...
          0.092721, 0.115388, 0.055244, 0.032710; ...
          0.167514, 0.055244, 0.185188, 0.042665; ...
          0.038401, 0.032710, 0.042665, 0.041882];
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
              -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
              -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
 assert (class (CMdl), "CompactClassificationDiscriminant");
 assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
 assert ({CMdl.Gamma, CMdl.MinGamma}, {0, 0}, 1e-15)
 assert (CMdl.ClassNames, unique (species))
 assert (CMdl.Sigma, sigma, 1e-6)
 assert (CMdl.Mu, mu, 1e-14)
 assert (CMdl.LogDetSigma, -9.9585, 1e-4)
 assert (CMdl.PredictorNames, PredictorNames)
***** test
 load fisheriris
 x = meas;
 y = species;
 Mdl = fitcdiscr (x, y, "Gamma", 0.5);
 CMdl = compact (Mdl);
 sigma = [0.265008, 0.046361, 0.083757, 0.019201; ...
          0.046361, 0.115388, 0.027622, 0.016355; ...
          0.083757, 0.027622, 0.185188, 0.021333; ...
          0.019201, 0.016355, 0.021333, 0.041882];
 mu = [5.0060, 3.4280, 1.4620, 0.2460; ...
       5.9360, 2.7700, 4.2600, 1.3260; ...
       6.5880, 2.9740, 5.5520, 2.0260];
 xCentered = [ 9.4000e-02,  7.2000e-02, -6.2000e-02, -4.6000e-02; ...
              -1.0600e-01, -4.2800e-01, -6.2000e-02, -4.6000e-02; ...
              -3.0600e-01, -2.2800e-01, -1.6200e-01, -4.6000e-02];
 assert (class (CMdl), "CompactClassificationDiscriminant");
 assert ({CMdl.DiscrimType, CMdl.ResponseName}, {"linear", "Y"})
 assert ({CMdl.Gamma, CMdl.MinGamma}, {0.5, 0})
 assert (CMdl.ClassNames, unique (species))
 assert (CMdl.Sigma, sigma, 1e-6)
 assert (CMdl.Mu, mu, 1e-14)
 assert (CMdl.LogDetSigma, -8.6884, 1e-4)
***** error<CompactClassificationDiscriminant: invalid classification object.> ...
 CompactClassificationDiscriminant (1)
***** test
 load fisheriris
 x = meas;
 y = species;
 Mdl = fitcdiscr (meas, species, "Gamma", 0.5);
 CMdl = compact (Mdl);
 [label, score, cost] = predict (CMdl, [2, 2, 2, 2]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.9999, 0.0001], 1e-4)
 assert (cost, [1, 0.0001, 0.9999], 1e-4)
 [label, score, cost] = predict (CMdl, [2.5, 2.5, 2.5, 2.5]);
 assert (label, {'versicolor'})
 assert (score, [0, 0.6368, 0.3632], 1e-4)
 assert (cost, [1, 0.3632, 0.6368], 1e-4)
***** test
 load fisheriris
 x = meas;
 y = species;
 xc = [min(x); mean(x); max(x)];
 Mdl = fitcdiscr (x, y);
 CMdl = compact (Mdl);
 [label, score, cost] = predict (CMdl, xc);
 l = {'setosa'; 'versicolor'; 'virginica'};
 s = [1, 0, 0; 0, 1, 0; 0, 0, 1];
 c = [0, 1, 1; 1, 0, 1; 1, 1, 0];
 assert (label, l)
 assert (score, s, 1e-4)
 assert (cost, c, 1e-4)
***** shared MODEL
 X = rand (10,2);
 Y = [ones(5,1);2*ones(5,1)];
 MODEL = compact (ClassificationDiscriminant (X, Y));
***** error<CompactClassificationDiscriminant.predict: too few input arguments.> ...
 predict (MODEL)
***** error<CompactClassificationDiscriminant.predict: XC is empty.> ...
 predict (MODEL, [])
***** error<CompactClassificationDiscriminant.predict: XC must have the same number of features as the trained model.> ...
 predict (MODEL, 1)
***** test
 load fisheriris
 model = fitcdiscr (meas, species);
 x = mean (meas);
 y = {'versicolor'};
 L = loss (model, x, y);
 assert (L, 0)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y, "Gamma", 0.4);
 x_test = [1, 6; 3, 3];
 y_test = {'A'; 'B'};
 L = loss (model, x_test, y_test);
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3];
 y_test = ['1'];
 L = loss (model, x_test, y_test, 'LossFun', 'quadratic');
 assert (L, 0.2423, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 L = loss (model, x_test, y_test, 'LossFun', 'classifcost');
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 L = loss (model, x_test, y_test, 'LossFun', 'hinge');
 assert (L, 0.5886, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6; 7, 8];
 y = ['1'; '2'; '3'; '1'];
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_test = [3, 3; 5, 7];
 y_test = ['1'; '2'];
 W = [1; 2];
 L = loss (model, x_test, y_test, 'LossFun', 'logit', 'Weights', W);
 assert (L, 0.5107, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y, "gamma" , 0.5);
 x_with_nan = [1, 2; NaN, 4];
 y_test = {'A'; 'B'};
 L = loss (model, x_with_nan, y_test);
 assert (L, 0.3333, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y);
 x_with_nan = [1, 2; NaN, 4];
 y_test = {'A'; 'B'};
 L = loss (model, x_with_nan, y_test, 'LossFun', 'logit');
 assert (isnan (L))
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = {'A'; 'B'; 'A'};
 model = fitcdiscr (x, y);
 customLossFun = @(C, S, W, Cost) sum (W .* sum (abs (C - S), 2));
 L = loss (model, x, y, 'LossFun', customLossFun);
 assert (L, 0.8889, 1e-4)
***** test
 x = [1, 2; 3, 4; 5, 6];
 y = [1; 2; 1];
 model = fitcdiscr (x, y);
 L = loss (model, x, y, 'LossFun', 'classiferror');
 assert (L, 0.3333, 1e-4)
***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
 loss (MODEL)
***** error<CompactClassificationDiscriminant.loss: too few input arguments.> ...
 loss (MODEL, ones (4,2))
***** error<CompactClassificationDiscriminant.loss: name-value arguments must be in pairs.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'LossFun')
***** error<CompactClassificationDiscriminant.loss: Y must have the same number of rows as X.> ...
 loss (MODEL, ones (4,2), ones (3,1))
***** error<CompactClassificationDiscriminant.loss: invalid loss function.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'LossFun', 'a')
***** error<CompactClassificationDiscriminant.loss: invalid 'Weights'.> ...
 loss (MODEL, ones (4,2), ones (4,1), 'Weights', 'w')
 load fisheriris
 mdl = fitcdiscr (meas, species);
 X = mean (meas);
 Y = {'versicolor'};
 m = margin (mdl, X, Y);
 assert (m, 1, 1e-6)
***** test
 X = [1, 2; 3, 4; 5, 6];
 Y = [1; 2; 1];
 mdl = fitcdiscr (X, Y, "gamma", 0.5);
 m = margin (mdl, X, Y);
 assert (m, [0.3333; -0.3333; 0.3333], 1e-4)
***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
 margin (MODEL)
***** error<CompactClassificationDiscriminant.margin: too few input arguments.> ...
 margin (MODEL, ones (4,2))
***** error<CompactClassificationDiscriminant.margin: Y must have the same number of rows as X.> ...
 margin (MODEL, ones (4,2), ones (3,1))
28 tests, 28 passed, 0 known failure, 0 skipped
[inst/friedman.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/friedman.m
***** demo
 load popcorn;
 friedman (popcorn, 3);
***** demo
 load popcorn;
 [p, atab] = friedman (popcorn, 3);
 disp (p);
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [p, atab] = friedman (popcorn, 3);
 assert (p, 0.001028853354594794, 1e-14);
 assert (atab.SS(1), 99.75, 1e-14);
 assert (atab.df(1), 2, 0);
 assert (atab.MS(1), 49.875, 1e-14);
 assert (atab.Chi_sq(1), 13.75862068965517, 1e-14);
 assert (atab.Prob_Chi_sq(1), 0.001028853354594794, 1e-14);
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [p, atab, stats] = friedman (popcorn, 3);
 assert (atab.SS(end), 116, 0);
 assert (atab.df(end), 17, 0);
 assert (stats.source, 'friedman');
 assert (stats.n, 2);
 assert (stats.meanranks, [8, 4.75, 2.25], 0);
 assert (stats.sigma, 2.692582403567252, 1e-14);
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 s = evalc ('[p, atab] = friedman (popcorn, 3);');
 assert (isempty (strtrim (s)));
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 s = evalc ('[p, atab] = friedman (popcorn, 3, "on");');
 assert (! isempty (strtrim (s)));
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 [p, atab] = friedman (popcorn, 3);
 assert (size (atab, 1), 4, 0);
 assert (numel (atab.SS), size (atab, 1), 0);
***** test
 x = [1, 2, 3; 2, 1, 3; 3, 2, 1];
 [p, atab] = friedman (x);
 assert (size (atab, 1), 3, 0);
 assert (numel (atab.SS), size (atab, 1), 0);
***** error<friedman: displayopt must be either 'on' or 'off'.> ...
 friedman ([5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; 6.5, 5.0, 4.0; ...
            7.0, 5.5, 5.0; 7.0, 5.0, 4.5], 3, 'invalid_displayopt');
***** error<friedman: NaN values in input are not allowed.> ...
 friedman ([1, 2; NaN, 4]);
***** error<friedman: repetitions and observations do not match.> ...
 friedman ([1,2; 3,4; 5,6], 2);
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/manovacluster.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/manovacluster.m
***** demo
 load carbig
 X = [MPG Acceleration Weight Displacement];
 [d, p, stats] = manova1 (X, Origin);
 manovacluster (stats)
***** test
 hf = figure ("visible", "off");
 unwind_protect
   load carbig
   X = [MPG Acceleration Weight Displacement];
   [d, p, stats] = manova1 (X, Origin);
   manovacluster (stats);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error manovacluster (stats, "some");
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/cholcov.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/cholcov.m
***** demo
 C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3]
 T = cholcov (C1)
 C2 = T'*T
***** test
 C1 = [2, 1, 1, 2; 1, 2, 1, 2; 1, 1, 2, 2; 2, 2, 2, 3];
 T = cholcov (C1);
 assert (C1, T'*T, 1e-15 * ones (size (C1)));
1 test, 1 passed, 0 known failure, 0 skipped
[inst/combnk.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/combnk.m
***** demo
 c = combnk (1:5, 2);
 disp ("All pairs of integers between 1 and 5:");
 disp (c);
***** test
 c = combnk (1:3, 2);
 assert (c, [1, 2; 1, 3; 2, 3]);
***** test
 c = combnk (1:3, 6);
 assert (isempty (c));
***** test
 c = combnk ({1, 2, 3}, 2);
 assert (c, {1, 2; 1, 3; 2, 3});
***** test
 c = combnk ("hello", 2);
 assert (c, ["lo"; "lo"; "ll"; "eo"; "el"; "el"; "ho"; "hl"; "hl"; "he"]);
4 tests, 4 passed, 0 known failure, 0 skipped
[inst/levene_test.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/levene_test.m
***** error<levene_test: invalid number of input arguments.> levene_test ()
***** error<levene_test: invalid number of input arguments.> ...
 levene_test (1, 2, 3, 4, 5);
***** error<levene_test: wrong value for alpha.> levene_test (randn (50, 2), 0);
***** error<levene_test: GROUP and X mismatch.> ...
 levene_test (randn (50, 2), [1, 2, 3]);
***** error<levene_test: GROUP and X mismatch.> ...
 levene_test (randn (50, 1), ones (55, 1));
***** error<levene_test: invalid second input argument.> ...
 levene_test (randn (50, 1), ones (50, 2));
***** error<levene_test: wrong value for alpha.> ...
 levene_test (randn (50, 2), [], 1.2);
***** error<levene_test: GROUP and X mismatch.> ...
 levene_test (randn (50, 2), "some_string");
***** error<levene_test: invalid third input argument.> ...
 levene_test (randn (50, 2), [], "alpha");
***** error<levene_test: wrong value for alpha.> ...
 levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 1.2);
***** error<levene_test: invalid third input argument.> ...
 levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], "err");
***** error<levene_test: invalid option for TESTTYPE as 4th argument.> ...
 levene_test (randn (50, 1), [ones(25, 1); 2*ones(25, 1)], 0.05, "type");
***** warning<levene_test: GROUP> ...
 levene_test (randn (50, 1), [ones(24, 1); 2*ones(25, 1); 3]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades);
 assert (h, 1);
 assert (pval, 9.523239714592791e-07, 1e-14);
 assert (W, 8.59529, 1e-5);
 assert (df, [4, 595]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades, [], "quadratic");
 assert (h, 1);
 assert (pval, 9.523239714592791e-07, 1e-14);
 assert (W, 8.59529, 1e-5);
 assert (df, [4, 595]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades, [], "median");
 assert (h, 1);
 assert (pval, 1.312093241723211e-06, 1e-14);
 assert (W, 8.415969, 1e-6);
 assert (df, [4, 595]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades(:,[1:3]));
 assert (h, 1);
 assert (pval, 0.004349390980463497, 1e-14);
 assert (W, 5.52139, 1e-5);
 assert (df, [2, 357]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades(:,[1:3]), "median");
 assert (h, 1);
 assert (pval, 0.004355216763951453, 1e-14);
 assert (W, 5.52001, 1e-5);
 assert (df, [2, 357]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades(:,[3,4]), "quadratic");
 assert (h, 0);
 assert (pval, 0.1807494957440653, 2e-14);
 assert (W, 1.80200, 1e-5);
 assert (df, [1, 238]);
***** test
 load examgrades
 [h, pval, W, df] = levene_test (grades(:,[3,4]), "median");
 assert (h, 0);
 assert (pval, 0.1978225622063785, 2e-14);
 assert (W, 1.66768, 1e-5);
 assert (df, [1, 238]);
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fit/unidfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/unidfit.m
***** demo
 ## Sample 2 populations from different discrete uniform distributions
 rand ("seed", 1);    # for reproducibility
 r1 = unidrnd (5, 1000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = unidrnd (9, 1000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 0:0.5:20.5, 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 NhatA = unidfit (r(:,1));
 NhatB = unidfit (r(:,2));

 ## Plot their estimated PDFs
 x = [0:10];
 y = unidpdf (x, NhatA);
 plot (x, y, "-pg");
 y = unidpdf (x, NhatB);
 plot (x, y, "-sc");
 xlim ([0, 10])
 ylim ([0, 0.4])
 legend ({"Normalized HIST of sample 1 with N=5", ...
          "Normalized HIST of sample 2 with N=9", ...
          sprintf("PDF for sample 1 with estimated N=%0.2f", NhatA), ...
          sprintf("PDF for sample 2 with estimated N=%0.2f", NhatB)})
 title ("Two population samples from different discrete uniform distributions")
 hold off
***** test
 x = 0:5;
 [Nhat, Nci] = unidfit (x);
 assert (Nhat, 5);
 assert (Nci, [5; 9]);
***** test
 x = 0:5;
 [Nhat, Nci] = unidfit (x, [], [1 1 1 1 1 1]);
 assert (Nhat, 5);
 assert (Nci, [5; 9]);
***** assert (unidfit ([1 1 2 3]), unidfit ([1 2 3], [] ,[2 1 1]))
***** error<unidfit: function called with too few input arguments.> unidfit ()
***** error<unidfit: X cannot have negative values.> unidfit (-1, [1 2 3 3])
***** error<unidfit: wrong value for ALPHA.> unidfit (1, 0)
***** error<unidfit: wrong value for ALPHA.> unidfit (1, 1.2)
***** error<unidfit: wrong value for ALPHA.> unidfit (1, [0.02 0.05])
***** error<unidfit: X and FREQ vector mismatch.> ...
 unidfit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
***** error<unidfit: X and FREQ vector mismatch.> ...
 unidfit ([1.5, 0.2], [], [1, 1, 1])
***** error<unidfit: FREQ cannot have negative values.> ...
 unidfit ([1.5, 0.2], [], [1, -1])
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/bisafit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/bisafit.m
***** demo
 ## Sample 3 populations from different Birnbaum-Saunders distributions
 rand ("seed", 5);    # for reproducibility
 r1 = bisarnd (1, 0.5, 2000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = bisarnd (2, 0.3, 2000, 1);
 rand ("seed", 7);    # for reproducibility
 r3 = bisarnd (4, 0.5, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 80, 4.2);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 1.1]);
 xlim ([0, 8]);
 hold on

 ## Estimate their α and β parameters
 beta_gammaA = bisafit (r(:,1));
 beta_gammaB = bisafit (r(:,2));
 beta_gammaC = bisafit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:8];
 y = bisapdf (x, beta_gammaA(1), beta_gammaA(2));
 plot (x, y, "-pr");
 y = bisapdf (x, beta_gammaB(1), beta_gammaB(2));
 plot (x, y, "-sg");
 y = bisapdf (x, beta_gammaC(1), beta_gammaC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with β=1 and γ=0.5", ...
          "Normalized HIST of sample 2 with β=2 and γ=0.3", ...
          "Normalized HIST of sample 3 with β=4 and γ=0.5", ...
          sprintf("PDF for sample 1 with estimated β=%0.2f and γ=%0.2f", ...
                  beta_gammaA(1), beta_gammaA(2)), ...
          sprintf("PDF for sample 2 with estimated β=%0.2f and γ=%0.2f", ...
                  beta_gammaB(1), beta_gammaB(2)), ...
          sprintf("PDF for sample 3 with estimated β=%0.2f and γ=%0.2f", ...
                  beta_gammaC(1), beta_gammaC(2))})
 title ("Three population samples from different Birnbaum-Saunders distributions")
 hold off
***** test
 paramhat = bisafit ([1:50]);
 paramhat_out = [16.2649, 1.0156];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = bisafit ([1:5]);
 paramhat_out = [2.5585, 0.5839];
 assert (paramhat, paramhat_out, 1e-4);
***** error<bisafit: X must be a vector.> bisafit (ones (2,5));
***** error<bisafit: X must contain only positive values.> bisafit ([-1 2 3 4]);
***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 1.2);
***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], 0);
***** error<bisafit: wrong value for ALPHA.> bisafit ([1, 2, 3, 4, 5], "alpha");
***** error<bisafit: X and CENSOR vectors mismatch.> ...
 bisafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<bisafit: X and CENSOR vectors mismatch.> ...
 bisafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<bisafit: X and FREQ vectors mismatch.> ...
 bisafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<bisafit: X and FREQ vectors mismatch.> ...
 bisafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<bisafit: 'options' 5th argument must be a structure> ...
 bisafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fit/hnlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/hnlike.m
***** test
 x = 1:20;
 paramhat = hnfit (x, 0);
 [nlogL, acov] = hnlike (paramhat, x);
 assert (nlogL, 64.179177404891300, 1e-14);
***** test
 x = 1:20;
 paramhat = hnfit (x, 0);
 [nlogL, acov] = hnlike (paramhat, x, ones (1, 20));
 assert (nlogL, 64.179177404891300, 1e-14);
***** error<hnlike: function called with too few input arguments.> ...
 hnlike ([12, 15]);
***** error<hnlike: wrong parameters length.> hnlike ([12, 15, 3], [1:50]);
***** error<hnlike: wrong parameters length.> hnlike ([3], [1:50]);
***** error<hnlike: X must be a vector of real values.> ...
 hnlike ([0, 3], ones (2));
***** error<hnlike: X must be a vector of real values.> ...
 hnlike ([0, 3], [1, 2, 3, 4, 5+i]);
***** error<hnlike: X and FREQ vectors mismatch.> ...
 hnlike ([1, 2], ones (10, 1), ones (8,1))
***** error<hnlike: FREQ must not contain negative values.> ...
 hnlike ([1, 2], ones (1, 8), [1 1 1 1 1 1 1 -1])
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/gevlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/gevlike.m
***** test
 x = 1;
 k = 0.2;
 sigma = 0.3;
 mu = 0.5;
 [L, C] = gevlike ([k sigma mu], x);
 expected_L = 0.75942;
 expected_C = [-0.12547 1.77884 1.06731; 1.77884 16.40761 8.48877; 1.06731 8.48877 0.27979];
 assert (L, expected_L, 0.001);
 assert (C, inv (expected_C), 0.001);
***** test
 x = 1;
 k = 0;
 sigma = 0.3;
 mu = 0.5;
 [L, C] = gevlike ([k sigma mu], x);
 expected_L = 0.65157;
 expected_C = [0.090036 3.41229 2.047337; 3.412229 24.760027 12.510190; 2.047337 12.510190 2.098618];
 assert (L, expected_L, 0.001);
 assert (C, inv (expected_C), 0.001);
***** test
 x = -5:-1;
 k = -0.2;
 sigma = 0.3;
 mu = 0.5;
 [L, C] = gevlike ([k sigma mu], x);
 expected_L = 3786.4;
 expected_C = [1.6802e-07, 4.6110e-06, 8.7297e-05; ...
               4.6110e-06, 7.5693e-06, 1.2034e-05; ...
               8.7297e-05, 1.2034e-05, -0.0019125];
 assert (L, expected_L, -0.001);
 assert (C, expected_C, -0.001);
***** test
 x = -5:0;
 k = -0.2;
 sigma = 0.3;
 mu = 0.5;
 [L, C] = gevlike ([k sigma mu], x, [1, 1, 1, 1, 1, 0]);
 expected_L = 3786.4;
 expected_C = [1.6802e-07, 4.6110e-06, 8.7297e-05; ...
               4.6110e-06, 7.5693e-06, 1.2034e-05; ...
               8.7297e-05, 1.2034e-05, -0.0019125];
 assert (L, expected_L, -0.001);
 assert (C, expected_C, -0.001);
***** error<gevlike: function called with too few input arguments.> gevlike (3.25)
***** error<gevlike: X must be a vector.> gevlike ([1, 2, 3], ones (2))
***** error<gevlike: PARAMS must be a three-element vector.> ...
 gevlike ([1, 2], [1, 3, 5, 7])
***** error<gevlike: PARAMS must be a three-element vector.> ...
 gevlike ([1, 2, 3, 4], [1, 3, 5, 7])
***** error<gevlike: X and FREQ vectors mismatch.> ...
 gevlike ([5, 0.2, 1], ones (10, 1), ones (8,1))
***** error<gevlike: FREQ must not contain negative values.> ...
 gevlike ([5, 0.2, 1], ones (1, 8), [1 1 1 1 1 1 1 -1])
***** error<gevlike: FREQ must contain integer values.> ...
 gevlike ([5, 0.2, 1], ones (1, 8), [1 1 1 1 1 1 1 1.5])
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/lognfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/lognfit.m
***** demo
 ## Sample 3 populations from 3 different log-normal distributions
 randn ("seed", 1);    # for reproducibility
 r1 = lognrnd (0, 0.25, 1000, 1);
 randn ("seed", 2);    # for reproducibility
 r2 = lognrnd (0, 0.5, 1000, 1);
 randn ("seed", 3);    # for reproducibility
 r3 = lognrnd (0, 1, 1000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 30, 2);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their mu and sigma parameters
 mu_sigmaA = lognfit (r(:,1));
 mu_sigmaB = lognfit (r(:,2));
 mu_sigmaC = lognfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:6];
 y = lognpdf (x, mu_sigmaA(1), mu_sigmaA(2));
 plot (x, y, "-pr");
 y = lognpdf (x, mu_sigmaB(1), mu_sigmaB(2));
 plot (x, y, "-sg");
 y = lognpdf (x, mu_sigmaC(1), mu_sigmaC(2));
 plot (x, y, "-^c");
 ylim ([0, 2])
 xlim ([0, 6])
 hold off
 legend ({"Normalized HIST of sample 1 with mu=0, σ=0.25", ...
          "Normalized HIST of sample 2 with mu=0, σ=0.5", ...
          "Normalized HIST of sample 3 with mu=0, σ=1", ...
          sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
                  mu_sigmaA(1), mu_sigmaA(2)), ...
          sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
                  mu_sigmaB(1), mu_sigmaB(2)), ...
          sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
                  mu_sigmaC(1), mu_sigmaC(2))}, "location", "northeast")
 title ("Three population samples from different log-normal distributions")
 hold off
***** test
 randn ("seed", 1);
 x = lognrnd (3, 5, [1000, 1]);
 [paramhat, paramci] = lognfit (x, 0.01);
 assert (paramci(1,1) < 3);
 assert (paramci(1,2) > 3);
 assert (paramci(2,1) < 5);
 assert (paramci(2,2) > 5);
***** error<lognfit: X must be a numeric vector of positive values.> ...
 lognfit (ones (20,3))
***** error<lognfit: X must be a numeric vector of positive values.> ...
 lognfit ({1, 2, 3, 4, 5})
***** error<lognfit: X must be a numeric vector of positive values.> ...
 lognfit ([-1, 2, 3, 4, 5])
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0)
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), -0.3)
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 1.2)
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), [0.05,  0.1])
***** error<lognfit: wrong value for ALPHA.> lognfit (ones (20,1), 0.02+i)
***** error<lognfit: X and CENSOR vectors mismatch.> ...
 lognfit (ones (20,1), [], zeros(15,1))
***** error<lognfit: X and FREQ vectors mismatch.> ...
 lognfit (ones (20,1), [], zeros(20,1), ones(25,1))
***** error<lognfit: > lognfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fit/invgfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/invgfit.m
***** demo
 ## Sample 3 populations from different inverse Gaussian distributions
 rand ("seed", 5); randn ("seed", 5);   # for reproducibility
 r1 = invgrnd (1, 0.2, 2000, 1);
 rand ("seed", 2); randn ("seed", 2);   # for reproducibility
 r2 = invgrnd (1, 3, 2000, 1);
 rand ("seed", 7); randn ("seed", 7);   # for reproducibility
 r3 = invgrnd (3, 1, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.1:0.1:3.2], 9);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 3]);
 xlim ([0, 3]);
 hold on

 ## Estimate their MU and LAMBDA parameters
 mu_lambdaA = invgfit (r(:,1));
 mu_lambdaB = invgfit (r(:,2));
 mu_lambdaC = invgfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:3];
 y = invgpdf (x, mu_lambdaA(1), mu_lambdaA(2));
 plot (x, y, "-pr");
 y = invgpdf (x, mu_lambdaB(1), mu_lambdaB(2));
 plot (x, y, "-sg");
 y = invgpdf (x, mu_lambdaC(1), mu_lambdaC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with μ=1 and λ=0.5", ...
          "Normalized HIST of sample 2 with μ=2 and λ=0.3", ...
          "Normalized HIST of sample 3 with μ=4 and λ=0.5", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and λ=%0.2f", ...
                  mu_lambdaA(1), mu_lambdaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and λ=%0.2f", ...
                  mu_lambdaB(1), mu_lambdaB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and λ=%0.2f", ...
                  mu_lambdaC(1), mu_lambdaC(2))})
 title ("Three population samples from different inverse Gaussian distributions")
 hold off
***** test
 paramhat = invgfit ([1:50]);
 paramhat_out = [25.5, 19.6973];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = invgfit ([1:5]);
 paramhat_out = [3, 8.1081];
 assert (paramhat, paramhat_out, 1e-4);
***** error<invgfit: X must be a vector.> invgfit (ones (2,5));
***** error<invgfit: X must contain only positive values.> invgfit ([-1 2 3 4]);
***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 1.2);
***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], 0);
***** error<invgfit: wrong value for ALPHA.> invgfit ([1, 2, 3, 4, 5], "alpha");
***** error<invgfit: X and CENSOR vectors mismatch.> ...
 invgfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<invgfit: X and CENSOR vectors mismatch.> ...
 invgfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<invgfit: X and FREQ vectors mismatch.> ...
 invgfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<invgfit: X and FREQ vectors mismatch.> ...
 invgfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<invgfit: 'options' 5th argument must be a structure> ...
 invgfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fit/nakafit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/nakafit.m
***** demo
 ## Sample 3 populations from different Nakagami distributions
 randg ("seed", 5)  # for reproducibility
 r1 = nakarnd (0.5, 1, 2000, 1);
 randg ("seed", 2)   # for reproducibility
 r2 = nakarnd (5, 1, 2000, 1);
 randg ("seed", 7)   # for reproducibility
 r3 = nakarnd (2, 2, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.05:0.1:3.5], 10);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 2.5]);
 xlim ([0, 3.0]);
 hold on

 ## Estimate their MU and LAMBDA parameters
 mu_omegaA = nakafit (r(:,1));
 mu_omegaB = nakafit (r(:,2));
 mu_omegaC = nakafit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01:0.1:3.01];
 y = nakapdf (x, mu_omegaA(1), mu_omegaA(2));
 plot (x, y, "-pr");
 y = nakapdf (x, mu_omegaB(1), mu_omegaB(2));
 plot (x, y, "-sg");
 y = nakapdf (x, mu_omegaC(1), mu_omegaC(2));
 plot (x, y, "-^c");
 legend ({"Normalized HIST of sample 1 with μ=0.5 and ω=1", ...
          "Normalized HIST of sample 2 with μ=5 and ω=1", ...
          "Normalized HIST of sample 3 with μ=2 and ω=2", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and ω=%0.2f", ...
                  mu_omegaA(1), mu_omegaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and ω=%0.2f", ...
                  mu_omegaB(1), mu_omegaB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and ω=%0.2f", ...
                  mu_omegaC(1), mu_omegaC(2))})
 title ("Three population samples from different Nakagami distributions")
 hold off
***** test
 paramhat = nakafit ([1:50]);
 paramhat_out = [0.7355, 858.5];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = nakafit ([1:5]);
 paramhat_out = [1.1740, 11];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = nakafit ([1:6], [], [], [1 1 1 1 1 0]);
 paramhat_out = [1.1740, 11];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = nakafit ([1:5], [], [], [1 1 1 1 2]);
 paramhat_out = nakafit ([1:5, 5]);
 assert (paramhat, paramhat_out, 1e-4);
***** error<nakafit: X must be a vector.> nakafit (ones (2,5));
***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 1.2);
***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], 0);
***** error<nakafit: wrong value for ALPHA.> nakafit ([1, 2, 3, 4, 5], "alpha");
***** error<nakafit: X and CENSOR vectors mismatch.> ...
 nakafit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<nakafit: X and CENSOR vectors mismatch.> ...
 nakafit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<nakafit: X and FREQ vectors mismatch.> ...
 nakafit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<nakafit: X and FREQ vectors mismatch.> ...
 nakafit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<nakafit: FREQ must not contain negative values.> ...
 nakafit ([1, 2, 3, 4, 5], [], [], [1 1 -1 1 1]);
***** error<nakafit: FREQ must contain integer values.> ...
 nakafit ([1, 2, 3, 4, 5], [], [], [1 1 1.5 1 1]);
***** error<nakafit: 'options' 5th argument must be a structure> ...
 nakafit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fit/unifit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/unifit.m
***** demo
 ## Sample 2 populations from different continuous uniform distributions
 rand ("seed", 5);    # for reproducibility
 r1 = unifrnd (2, 5, 2000, 1);
 rand ("seed", 6);    # for reproducibility
 r2 = unifrnd (3, 9, 2000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 0:0.5:10, 2);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 a_bA = unifit (r(:,1));
 a_bB = unifit (r(:,2));

 ## Plot their estimated PDFs
 x = [0:10];
 y = unifpdf (x, a_bA(1), a_bA(2));
 plot (x, y, "-pg");
 y = unifpdf (x, a_bB(1), a_bB(2));
 plot (x, y, "-sc");
 xlim ([1, 10])
 ylim ([0, 0.5])
 legend ({"Normalized HIST of sample 1 with a=2 and b=5", ...
          "Normalized HIST of sample 2 with a=3 and b=9", ...
          sprintf("PDF for sample 1 with estimated a=%0.2f and b=%0.2f", ...
                  a_bA(1), a_bA(2)), ...
          sprintf("PDF for sample 2 with estimated a=%0.2f and b=%0.2f", ...
                  a_bB(1), a_bB(2))})
 title ("Two population samples from different continuous uniform distributions")
 hold off
***** test
 x = 0:5;
 [paramhat, paramci] = unifit (x);
 assert (paramhat, [0, 5]);
 assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
***** test
 x = 0:5;
 [paramhat, paramci] = unifit (x, [], [1 1 1 1 1 1]);
 assert (paramhat, [0, 5]);
 assert (paramci, [-3.2377, 8.2377; 0, 5], 1e-4);
***** assert (unifit ([1 1 2 3]), unifit ([1 2 3], [] ,[2 1 1]))
***** error<unifit: function called with too few input arguments.> unifit ()
***** error<unifit: X cannot have negative values.> unifit (-1, [1 2 3 3])
***** error<unifit: wrong value for ALPHA.> unifit (1, 0)
***** error<unifit: wrong value for ALPHA.> unifit (1, 1.2)
***** error<unifit: wrong value for ALPHA.> unifit (1, [0.02 0.05])
***** error<unifit: X and FREQ vector mismatch.> ...
 unifit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
***** error<unifit: FREQ cannot have negative values.> ...
 unifit ([1.5, 0.2], [], [1, -1])
***** error<unifit: X and FREQ vector mismatch.> ...
 unifit ([1.5, 0.2], [], [1, 1, 1])
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/tlslike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/tlslike.m
***** test
 x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
 [nlogL, acov] = tlslike ([0.035893, 0.862711, 0.649261], x);
 acov_out = [0.2525, 0.0670, 0.0288; ...
             0.0670, 0.5724, 0.1786; ...
             0.0288, 0.1786, 0.1789];
 assert (nlogL, 17.9979636579, 1e-10);
 assert (acov, acov_out, 1e-4);
***** error<tlslike: too few input arguments.> tlslike ([12, 15, 1]);
***** error<tlslike: wrong parameters length.> tlslike ([12, 15], [1:50]);
***** error<tlslike: X must be a vector.> tlslike ([12, 3, 1], ones (10, 2));
***** error<tlslike: X and CENSOR> tlslike ([12, 15, 1], [1:50], [1, 2, 3]);
***** error<tlslike: X and FREQ> tlslike ([12, 15, 1], [1:50], [], [1, 2, 3]);
***** error<tlslike: FREQ cannot> tlslike ([12, 15, 1], [1:3], [], [1, 2, -3]);
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/dist_fit/logllike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/logllike.m
***** test
 [nlogL, acov] = logllike ([3.09717, 0.468525], [1:50]);
 assert (nlogL, 211.2965, 1e-4);
 assert (acov, [0.0131, -0.0007; -0.0007, 0.0031], 1e-4);
***** test
 [nlogL, acov] = logllike ([1.01124, 0.336449], [1:5]);
 assert (nlogL, 9.2206, 1e-4);
 assert (acov, [0.0712, -0.0032; -0.0032, 0.0153], 1e-4);
***** error<logllike: function called with too few input arguments.> logllike (3.25)
***** error<logllike: X must be a vector.> logllike ([5, 0.2], ones (2))
***** error<logllike: PARAMS must be a two-element vector.> ...
 logllike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<logllike: X and CENSOR vector mismatch.> ...
 logllike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<logllike: X and FREQ vector mismatch.> ...
 logllike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<logllike: X and FREQ vector mismatch.> ...
 logllike ([1.5, 0.2], [1:5], [], [1, 1, 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/gamfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/gamfit.m
***** demo
 ## Sample 3 populations from different Gamma distributions
 randg ("seed", 5);    # for reproducibility
 r1 = gamrnd (1, 2, 2000, 1);
 randg ("seed", 2);    # for reproducibility
 r2 = gamrnd (2, 2, 2000, 1);
 randg ("seed", 7);    # for reproducibility
 r3 = gamrnd (7.5, 1, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 75, 4);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.62]);
 xlim ([0, 12]);
 hold on

 ## Estimate their α and β parameters
 a_bA = gamfit (r(:,1));
 a_bB = gamfit (r(:,2));
 a_bC = gamfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01,0.1:0.2:18];
 y = gampdf (x, a_bA(1), a_bA(2));
 plot (x, y, "-pr");
 y = gampdf (x, a_bB(1), a_bB(2));
 plot (x, y, "-sg");
 y = gampdf (x, a_bC(1), a_bC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with α=1 and β=2", ...
          "Normalized HIST of sample 2 with α=2 and β=2", ...
          "Normalized HIST of sample 3 with α=7.5 and β=1", ...
          sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
                  a_bA(1), a_bA(2)), ...
          sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
                  a_bB(1), a_bB(2)), ...
          sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
                  a_bC(1), a_bC(2))})
 title ("Three population samples from different Gamma distributions")
 hold off
***** shared x
 x = [1.2 1.6 1.7 1.8 1.9 2.0 2.2 2.6 3.0 3.5 4.0 4.8 5.6 6.6 7.6];
***** test
 [paramhat, paramci] = gamfit (x);
 assert (paramhat, [3.4248, 0.9752], 1e-4);
 assert (paramci, [1.7287, 0.4670; 6.7852, 2.0366], 1e-4);
***** test
 [paramhat, paramci] = gamfit (x, 0.01);
 assert (paramhat, [3.4248, 0.9752], 1e-4);
 assert (paramci, [1.3945, 0.3705; 8.4113, 2.5668], 1e-4);
***** test
 freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
 [paramhat, paramci] = gamfit (x, [], [], freq);
 assert (paramhat, [3.3025, 1.0615], 1e-4);
 assert (paramci, [1.7710, 0.5415; 6.1584, 2.0806], 1e-4);
***** test
 [paramhat, paramci] = gamfit (x, [], [], [1:15]);
 assert (paramhat, [4.4484, 0.9689], 1e-4);
 assert (paramci, [3.4848, 0.7482; 5.6785, 1.2546], 1e-4);
***** test
 [paramhat, paramci] = gamfit (x, 0.01, [], [1:15]);
 assert (paramhat, [4.4484, 0.9689], 1e-4);
 assert (paramci, [3.2275, 0.6899; 6.1312, 1.3608], 1e-4);
***** test
 cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
 [paramhat, paramci] = gamfit (x, [], cens, [1:15]);
 assert (paramhat, [4.7537, 0.9308], 1e-4);
 assert (paramci, [3.7123, 0.7162; 6.0872, 1.2097], 1e-4);
***** test
 cens = [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0];
 freq = [1 1 1 1 2 1 1 1 1 2 1 1 1 1 2];
 [paramhat, paramci] = gamfit (x, [], cens, freq);
 assert (paramhat, [3.4736, 1.0847], 1e-4);
 assert (paramci, [1.8286, 0.5359; 6.5982, 2.1956], 1e-4);
***** test
 [paramhat, paramci] = gamfit ([1 1 1 1 1 1]);
 assert (paramhat, [Inf, 0]);
 assert (paramci, [Inf, 0; Inf, 0]);
***** test
 [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [1 1 1 1 1 1]);
 assert (paramhat, [NaN, NaN]);
 assert (paramci, [NaN, NaN; NaN, NaN]);
***** test
 [paramhat, paramci] = gamfit ([1 1 1 1 1 1], [], [], [1 1 1 1 1 1]);
 assert (paramhat, [Inf, 0]);
 assert (paramci, [Inf, 0; Inf, 0]);
***** assert (class (gamfit (single (x))), "single")
***** error<gamfit: X must be a vector.> gamfit (ones (2))
***** error<gamfit: wrong value for ALPHA.> gamfit (x, 1)
***** error<gamfit: wrong value for ALPHA.> gamfit (x, -1)
***** error<gamfit: wrong value for ALPHA.> gamfit (x, {0.05})
***** error<gamfit: wrong value for ALPHA.> gamfit (x, "a")
***** error<gamfit: wrong value for ALPHA.> gamfit (x, i)
***** error<gamfit: wrong value for ALPHA.> gamfit (x, [0.01 0.02])
***** error<gamfit: X and FREQ vectors mismatch.>
 gamfit ([1 2 3], 0.05, [], [1 5])
***** error<gamfit: FREQ must not contain negative values.>
 gamfit ([1 2 3], 0.05, [], [1 5 -1])
***** error<gamfit: 'options' 5th argument must be a structure> ...
 gamfit ([1:10], 0.05, [], [], 5)
***** error<gamfit: X cannot contain negative values.> gamfit ([1 2 3 -4])
***** error<gamfit: X must contain positive values when censored.> ...
 gamfit ([1 2 0], [], [1 0 0])
23 tests, 23 passed, 0 known failure, 0 skipped
[inst/dist_fit/logifit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/logifit.m
***** demo
 ## Sample 3 populations from different logistic distributions
 rand ("seed", 5)  # for reproducibility
 r1 = logirnd (2, 1, 2000, 1);
 rand ("seed", 2)   # for reproducibility
 r2 = logirnd (5, 2, 2000, 1);
 rand ("seed", 7)   # for reproducibility
 r3 = logirnd (9, 4, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [-6:20], 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.3]);
 xlim ([-5, 20]);
 hold on

 ## Estimate their MU and LAMBDA parameters
 mu_sA = logifit (r(:,1));
 mu_sB = logifit (r(:,2));
 mu_sC = logifit (r(:,3));

 ## Plot their estimated PDFs
 x = [-5:0.5:20];
 y = logipdf (x, mu_sA(1), mu_sA(2));
 plot (x, y, "-pr");
 y = logipdf (x, mu_sB(1), mu_sB(2));
 plot (x, y, "-sg");
 y = logipdf (x, mu_sC(1), mu_sC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with μ=1 and s=0.5", ...
          "Normalized HIST of sample 2 with μ=2 and s=0.3", ...
          "Normalized HIST of sample 3 with μ=4 and s=0.5", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and s=%0.2f", ...
                  mu_sA(1), mu_sA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and s=%0.2f", ...
                  mu_sB(1), mu_sB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and s=%0.2f", ...
                  mu_sC(1), mu_sC(2))})
 title ("Three population samples from different logistic distributions")
 hold off
***** test
 paramhat = logifit ([1:50]);
 paramhat_out = [25.5, 8.7724];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = logifit ([1:5]);
 paramhat_out = [3, 0.8645];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = logifit ([1:6], [], [], [1 1 1 1 1 0]);
 paramhat_out = [3, 0.8645];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = logifit ([1:5], [], [], [1 1 1 1 2]);
 paramhat_out = logifit ([1:5, 5]);
 assert (paramhat, paramhat_out, 1e-4);
***** error<logifit: X must be a vector.> logifit (ones (2,5));
***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 1.2);
***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], 0);
***** error<logifit: wrong value for ALPHA.> logifit ([1, 2, 3, 4, 5], "alpha");
***** error<logifit: X and CENSOR vectors mismatch.> ...
 logifit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<logifit: X and CENSOR vectors mismatch.> ...
 logifit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<logifit: X and FREQ vectors mismatch.> ...
 logifit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<logifit: X and FREQ vectors mismatch.> ...
 logifit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<logifit: 'options' 5th argument must be a structure> ...
 logifit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/gevfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/gevfit.m
***** demo
 ## Sample 2 populations from 2 different exponential distributions
 rand ("seed", 1);   # for reproducibility
 r1 = gevrnd (-0.5, 1, 2, 5000, 1);
 rand ("seed", 2);   # for reproducibility
 r2 = gevrnd (0, 1, -4, 5000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 50, 5);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their k, sigma, and mu parameters
 k_sigma_muA = gevfit (r(:,1));
 k_sigma_muB = gevfit (r(:,2));

 ## Plot their estimated PDFs
 x = [-10:0.5:20];
 y = gevpdf (x, k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3));
 plot (x, y, "-pr");
 y = gevpdf (x, k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3));
 plot (x, y, "-sg");
 ylim ([0, 0.7])
 xlim ([-7, 5])
 legend ({"Normalized HIST of sample 1 with k=-0.5, σ=1, μ=2", ...
          "Normalized HIST of sample 2 with k=0, σ=1, μ=-4",
     sprintf("PDF for sample 1 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
                 k_sigma_muA(1), k_sigma_muA(2), k_sigma_muA(3)), ...
     sprintf("PDF for sample 3 with estimated k=%0.2f, σ=%0.2f, μ=%0.2f", ...
                 k_sigma_muB(1), k_sigma_muB(2), k_sigma_muB(3))})
 title ("Two population samples from different exponential distributions")
 hold off
***** test
 x = 1:50;
 [pfit, pci] = gevfit (x);
 pfit_out = [-0.4407, 15.1923, 21.5309];
 pci_out = [-0.7532, 11.5878, 16.5686; -0.1282, 19.9183, 26.4926];
 assert (pfit, pfit_out, 1e-3);
 assert (pci, pci_out, 1e-3);
***** test
 x = 1:2:50;
 [pfit, pci] = gevfit (x);
 pfit_out = [-0.4434, 15.2024, 21.0532];
 pci_out = [-0.8904, 10.3439, 14.0168; 0.0035, 22.3429, 28.0896];
 assert (pfit, pfit_out, 1e-3);
 assert (pci, pci_out, 1e-3);
***** error<gevfit: X must be a vector.> gevfit (ones (2,5));
***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 1.2);
***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], 0);
***** error<gevfit: wrong value for ALPHA.> gevfit ([1, 2, 3, 4, 5], "alpha");
***** error<gevfit: X and FREQ vectors mismatch.> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
***** error<gevfit: FREQ must not contain negative values.> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
***** error<gevfit: FREQ must contain integer values.> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
***** error<gevfit: 'options' argument must be a structure> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
***** error<gevfit: 'options' argument must be a structure> ...
 gevfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/gumbellike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/gumbellike.m
***** test
 x = 1:50;
 [nlogL, avar] = gumbellike ([2.3, 1.2], x);
 avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
 assert (nlogL, 3.242264755689906e+17, 1e-14);
 assert (avar, avar_out, 1e-3);
***** test
 x = 1:50;
 [nlogL, avar] = gumbellike ([2.3, 1.2], x * 0.5);
 avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
 assert (nlogL, 481898704.0472211, 1e-6);
 assert (avar, avar_out, 1e-3);
***** test
 x = 1:50;
 [nlogL, avar] = gumbellike ([21, 15], x);
 avar_out = [11.73913876598908, -5.9546128523121216; ...
             -5.954612852312121, 3.708060045170236];
 assert (nlogL, 223.7612479380652, 1e-13);
 assert (avar, avar_out, 1e-14);
***** error<gumbellike: too few input arguments.> gumbellike ([12, 15]);
***** error<gumbellike: wrong parameters length.> gumbellike ([12, 15, 3], [1:50]);
***** error<gumbellike: X must be a vector.> gumbellike ([12, 3], ones (10, 2));
***** error<gumbellike: X and CENSOR> gumbellike ([12, 15], [1:50], [1, 2, 3]);
***** error<gumbellike: X and FREQ> gumbellike ([12, 15], [1:50], [], [1, 2, 3]);
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/normfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/normfit.m
***** demo
 ## Sample 3 populations from 3 different normal distributions
 randn ("seed", 1);    # for reproducibility
 r1 = normrnd (2, 5, 5000, 1);
 randn ("seed", 2);    # for reproducibility
 r2 = normrnd (5, 2, 5000, 1);
 randn ("seed", 3);    # for reproducibility
 r3 = normrnd (9, 4, 5000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 15, 0.4);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their mu and sigma parameters
 [muhat, sigmahat] = normfit (r);

 ## Plot their estimated PDFs
 x = [min(r(:)):max(r(:))];
 y = normpdf (x, muhat(1), sigmahat(1));
 plot (x, y, "-pr");
 y = normpdf (x, muhat(2), sigmahat(2));
 plot (x, y, "-sg");
 y = normpdf (x, muhat(3), sigmahat(3));
 plot (x, y, "-^c");
 ylim ([0, 0.5])
 xlim ([-20, 20])
 hold off
 legend ({"Normalized HIST of sample 1 with mu=2, σ=5", ...
          "Normalized HIST of sample 2 with mu=5, σ=2", ...
          "Normalized HIST of sample 3 with mu=9, σ=4", ...
          sprintf("PDF for sample 1 with estimated mu=%0.2f and σ=%0.2f", ...
                  muhat(1), sigmahat(1)), ...
          sprintf("PDF for sample 2 with estimated mu=%0.2f and σ=%0.2f", ...
                  muhat(2), sigmahat(2)), ...
          sprintf("PDF for sample 3 with estimated mu=%0.2f and σ=%0.2f", ...
                  muhat(3), sigmahat(3))}, "location", "northwest")
 title ("Three population samples from different normal distributions")
 hold off
***** test
 load lightbulb
 idx = find (lightbulb(:,2) == 0);
 censoring = lightbulb(idx,3) == 1;
 [muHat, sigmaHat] = normfit (lightbulb(idx,1), [], censoring);
 assert (muHat, 9496.59586737857, 1e-11);
 assert (sigmaHat, 3064.021012796456, 2e-12);
***** test
 randn ("seed", 234);
 x = normrnd (3, 5, [1000, 1]);
 [muHat, sigmaHat, muCI, sigmaCI] = normfit (x, 0.01);
 assert (muCI(1) < 3);
 assert (muCI(2) > 3);
 assert (sigmaCI(1) < 5);
 assert (sigmaCI(2) > 5);
***** error<normfit: X must not be a multi-dimensional array.> ...
 normfit (ones (3,3,3))
***** error<normfit: matrix data acceptable only under 2-arg syntax.> ...
 normfit (ones (20,3), [], zeros (20,1))
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0)
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), -0.3)
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 1.2)
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), [0.05 0.1])
***** error<normfit: wrong value for ALPHA.> normfit (ones (20,1), 0.02+i)
***** error<normfit: X and CENSOR vectors mismatch.> ...
 normfit (ones (20,1), [], zeros(15,1))
***** error<normfit: X and FREQ vectors mismatch.> ...
 normfit (ones (20,1), [], zeros(20,1), ones(25,1))
***** error<normfit: FREQ must not contain negative values.> ...
 normfit (ones (5,1), [], zeros(5,1), [1, 2, 1, 2, -1]')
***** error<normfit: > normfit (ones (20,1), [], zeros(20,1), ones(20,1), "options")
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/loglfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/loglfit.m
***** demo
 ## Sample 3 populations from different log-logistic distributions
 rand ("seed", 5)  # for reproducibility
 r1 = loglrnd (0, 1, 2000, 1);
 rand ("seed", 2)   # for reproducibility
 r2 = loglrnd (0, 0.5, 2000, 1);
 rand ("seed", 7)   # for reproducibility
 r3 = loglrnd (0, 0.125, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.05:0.1:2.5], 10);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 3.5]);
 xlim ([0, 2.0]);
 hold on

 ## Estimate their MU and LAMBDA parameters
 a_bA = loglfit (r(:,1));
 a_bB = loglfit (r(:,2));
 a_bC = loglfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01:0.1:2.01];
 y = loglpdf (x, a_bA(1), a_bA(2));
 plot (x, y, "-pr");
 y = loglpdf (x, a_bB(1), a_bB(2));
 plot (x, y, "-sg");
 y = loglpdf (x, a_bC(1), a_bC(2));
 plot (x, y, "-^c");
 legend ({"Normalized HIST of sample 1 with α=1 and β=1", ...
          "Normalized HIST of sample 2 with α=1 and β=2", ...
          "Normalized HIST of sample 3 with α=1 and β=8", ...
          sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
                  a_bA(1), a_bA(2)), ...
          sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
                  a_bB(1), a_bB(2)), ...
          sprintf("PDF for sample 3 with estimated α=%0.2f and β=%0.2f", ...
                  a_bC(1), a_bC(2))})
 title ("Three population samples from different log-logistic distributions")
 hold off
***** test
 [paramhat, paramci] = loglfit ([1:50]);
 paramhat_out = [3.09717, 0.468525];
 paramci_out = [2.87261, 0.370616; 3.32174, 0.5923];
 assert (paramhat, paramhat_out, 1e-5);
 assert (paramci, paramci_out, 1e-5);
***** test
 paramhat = loglfit ([1:5]);
 paramhat_out = [1.01124, 0.336449];
 assert (paramhat, paramhat_out, 1e-5);
***** test
 paramhat = loglfit ([1:6], [], [], [1 1 1 1 1 0]);
 paramhat_out = [1.01124, 0.336449];
 assert (paramhat, paramhat_out, 1e-4);
***** test
 paramhat = loglfit ([1:5], [], [], [1 1 1 1 2]);
 paramhat_out = loglfit ([1:5, 5]);
 assert (paramhat, paramhat_out, 1e-4);
***** error<loglfit: X must be a vector.> loglfit (ones (2,5));
***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 1.2);
***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], 0);
***** error<loglfit: wrong value for ALPHA.> loglfit ([1, 2, 3, 4, 5], "alpha");
***** error<loglfit: X and CENSOR vectors mismatch.> ...
 loglfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<loglfit: X and CENSOR vectors mismatch.> ...
 loglfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<loglfit: X and FREQ vectors mismatch.> ...
 loglfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<loglfit: X and FREQ vectors mismatch.> ...
 loglfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<loglfit: 'options' 5th argument must be a structure> ...
 loglfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/nakalike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/nakalike.m
***** test
 nlogL = nakalike ([0.735504, 858.5], [1:50]);
 assert (nlogL, 202.8689, 1e-4);
***** test
 nlogL = nakalike ([1.17404, 11], [1:5]);
 assert (nlogL, 8.6976, 1e-4);
***** test
 nlogL = nakalike ([1.17404, 11], [1:5], [], [1, 1, 1, 1, 1]);
 assert (nlogL, 8.6976, 1e-4);
***** test
 nlogL = nakalike ([1.17404, 11], [1:6], [], [1, 1, 1, 1, 1, 0]);
 assert (nlogL, 8.6976, 1e-4);
***** error<nakalike: function called with too few input arguments.> nakalike (3.25)
***** error<nakalike: X must be a vector.> nakalike ([5, 0.2], ones (2))
***** error<nakalike: PARAMS must be a two-element vector.> ...
 nakalike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<nakalike: X and CENSOR vector mismatch.> ...
 nakalike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<nakalike: X and FREQ vector mismatch.> ...
 nakalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<nakalike: X and FREQ vector mismatch.> ...
 nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
***** error<nakalike: FREQ must not contain negative values.> ...
 nakalike ([1.5, 0.2], [1:5], [], [1, 1, 1, 1, -1])
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/dist_fit/hnfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/hnfit.m
***** demo
 ## Sample 2 populations from different half-normal distributions
 rand ("seed", 1);   # for reproducibility
 r1 = hnrnd (0, 5, 5000, 1);
 rand ("seed", 2);   # for reproducibility
 r2 = hnrnd (0, 2, 5000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, [0.5:20], 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their shape parameters
 mu_sigmaA = hnfit (r(:,1), 0);
 mu_sigmaB = hnfit (r(:,2), 0);

 ## Plot their estimated PDFs
 x = [0:0.2:10];
 y = hnpdf (x, mu_sigmaA(1), mu_sigmaA(2));
 plot (x, y, "-pr");
 y = hnpdf (x, mu_sigmaB(1), mu_sigmaB(2));
 plot (x, y, "-sg");
 xlim ([0, 10])
 ylim ([0, 0.5])
 legend ({"Normalized HIST of sample 1 with μ=0 and σ=5", ...
          "Normalized HIST of sample 2 with μ=0 and σ=2", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaA(1), mu_sigmaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaB(1), mu_sigmaB(2))})
 title ("Two population samples from different half-normal distributions")
 hold off
***** test
 x = 1:20;
 [paramhat, paramci] = hnfit (x, 0);
 assert (paramhat, [0, 11.9791], 1e-4);
 assert (paramci, [0, 9.1648; 0, 17.2987], 1e-4);
***** test
 x = 1:20;
 [paramhat, paramci] = hnfit (x, 0, 0.01);
 assert (paramci, [0, 8.4709; 0, 19.6487], 1e-4);
***** error<hnfit: function called with too few input arguments.> hnfit ()
***** error<hnfit: function called with too few input arguments.> hnfit (1)
***** error<hnfit: X must be a vector of real values.> hnfit ([0.2, 0.5+i], 0);
***** error<hnfit: X must be a vector of real values.> hnfit (ones (2,2) * 0.5, 0);
***** error<hnfit: MU must be a real scalar value.> ...
 hnfit ([0.5, 1.2], [0, 1]);
***** error<hnfit: MU must be a real scalar value.> ...
 hnfit ([0.5, 1.2], 5+i);
***** error<hnfit: X cannot contain values less than MU.> ...
 hnfit ([1:5], 2);
***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, 1.2);
***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, i);
***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, -1);
***** error<hnfit: wrong value for ALPHA.> hnfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
***** error<hnfit: X and FREQ vectors mismatch.>
 hnfit ([1 2 3], 0, [], [1 5])
***** error<hnfit: FREQ must not contain negative values.>
 hnfit ([1 2 3], 0, [], [1 5 -1])
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fit/binolike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/binolike.m
***** assert (binolike ([3, 0.333], [0:3]), 6.8302, 1e-4)
***** assert (binolike ([3, 0.333], 0), 1.2149, 1e-4)
***** assert (binolike ([3, 0.333], 1), 0.8109, 1e-4)
***** assert (binolike ([3, 0.333], 2), 1.5056, 1e-4)
***** assert (binolike ([3, 0.333], 3), 3.2988, 1e-4)
***** test
 [nlogL, acov] = binolike ([3, 0.333], 3);
 assert (acov(4), 0.0740, 1e-4)
***** error<binolike: function called with too few input arguments.> binolike (3.25)
***** error<binolike: X must be a vector.> binolike ([5, 0.2], ones (2))
***** error<binolike: PARAMS must be a two-element vector.> ...
 binolike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<binolike: number of trials,> binolike ([1.5, 0.2], 1)
***** error<binolike: number of trials,> binolike ([-1, 0.2], 1)
***** error<binolike: number of trials,> binolike ([Inf, 0.2], 1)
***** error<binolike: probability of success,> binolike ([5, 1.2], [3, 5])
***** error<binolike: probability of success,> binolike ([5, -0.2], [3, 5])
***** error<binolike: X and FREQ vectors mismatch.> ...
 binolike ([5, 0.5], ones (10, 1), ones (8,1))
***** error<binolike: FREQ must not contain negative values.> ...
 binolike ([5, 0.5], ones (1, 8), [1 1 1 1 1 1 1 -1])
***** error<binolike: X cannot have negative values.> binolike ([5, 0.2], [-1, 3])
***** error<binolike: number of successes,> binolike ([5, 0.2], [3, 5, 7])
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/dist_fit/bisalike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/bisalike.m
***** test
 nlogL = bisalike ([16.2649, 1.0156], [1:50]);
 assert (nlogL, 215.5905, 1e-4);
***** test
 nlogL = bisalike ([2.5585, 0.5839], [1:5]);
 assert (nlogL, 8.9950, 1e-4);
***** error<bisalike: function called with too few input arguments.> bisalike (3.25)
***** error<bisalike: X must be a vector.> bisalike ([5, 0.2], ones (2))
***** error<bisalike: X cannot have negative values.> bisalike ([5, 0.2], [-1, 3])
***** error<bisalike: PARAMS must be a two-element vector.> ...
 bisalike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<bisalike: X and CENSOR vector mismatch.> ...
 bisalike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<bisalike: X and FREQ vector mismatch.> ...
 bisalike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<bisalike: X and FREQ vector mismatch.> ...
 bisalike ([1.5, 0.2], [1:5], [], [1, 1, 1])
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/gevfit_lmom.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/gevfit_lmom.m
***** xtest <31070>
 data = 1:50;
 [pfit, pci] = gevfit_lmom (data);
 expected_p = [-0.28 15.01 20.22]';
 assert (pfit, expected_p, 0.1);
1 test, 1 passed, 0 known failure, 0 skipped
[inst/dist_fit/explike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/explike.m
***** test
 x = 12;
 beta = 5;
 [L, V] = explike (beta, x);
 expected_L = 4.0094;
 expected_V = 6.5789;
 assert (L, expected_L, 0.001);
 assert (V, expected_V, 0.001);
***** test
 x = 1:5;
 beta = 2;
 [L, V] = explike (beta, x);
 expected_L = 10.9657;
 expected_V = 0.4;
 assert (L, expected_L, 0.001);
 assert (V, expected_V, 0.001);
***** error<explike: function called with too few input arguments.> explike ()
***** error<explike: function called with too few input arguments.> explike (2)
***** error<explike: MU must be a scalar.> explike ([12, 3], [1:50])
***** error<explike: X must be a vector.> explike (3, ones (10, 2))
***** error<explike: X and CENSOR vectors mismatch.> ...
 explike (3, [1:50], [1, 2, 3])
***** error<explike: X and FREQ vectors mismatch.> ...
 explike (3, [1:50], [], [1, 2, 3])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/raylfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/raylfit.m
***** demo
 ## Sample 3 populations from 3 different Rayleigh distributions
 rand ("seed", 2);    # for reproducibility
 r1 = raylrnd (1, 1000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = raylrnd (2, 1000, 1);
 rand ("seed", 3);    # for reproducibility
 r3 = raylrnd (4, 1000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.5:0.5:10.5], 2);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their lambda parameter
 sigmaA = raylfit (r(:,1));
 sigmaB = raylfit (r(:,2));
 sigmaC = raylfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:10];
 y = raylpdf (x, sigmaA);
 plot (x, y, "-pr");
 y = raylpdf (x, sigmaB);
 plot (x, y, "-sg");
 y = raylpdf (x, sigmaC);
 plot (x, y, "-^c");
 xlim ([0, 10])
 ylim ([0, 0.7])
 legend ({"Normalized HIST of sample 1 with σ=1", ...
          "Normalized HIST of sample 2 with σ=2", ...
          "Normalized HIST of sample 3 with σ=4", ...
          sprintf("PDF for sample 1 with estimated σ=%0.2f", ...
                  sigmaA), ...
          sprintf("PDF for sample 2 with estimated σ=%0.2f", ...
                  sigmaB), ...
          sprintf("PDF for sample 3 with estimated σ=%0.2f", ...
                  sigmaC)})
 title ("Three population samples from different Rayleigh distributions")
 hold off
***** test
 x = [1 3 2 4 5 4 3 4];
 [shat, sci] = raylfit (x);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.8243; 3.7279], 1e-4)
***** test
 x = [1 3 2 4 5 4 3 4];
 [shat, sci] = raylfit (x, 0.01);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.6738; 4.3208], 1e-4)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [shat, sci] = raylfit (x, [], [], f);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.8243; 3.7279], 1e-4)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [shat, sci] = raylfit (x, 0.01, [], f);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.6738; 4.3208], 1e-4)
***** test
 x = [1 2 3 4 5 6];
 c = [0 0 0 0 0 1];
 f = [1 1 2 3 1 1];
 [shat, sci] = raylfit (x, 0.01, c, f);
 assert (shat, 2.4495, 1e-4)
 assert (sci, [1.6738; 4.3208], 1e-4)
***** error<raylfit: X must be a vector.> raylfit (ones (2,5));
***** error<raylfit: X cannot have negative values.> raylfit ([1 2 -1 3])
***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 0)
***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], 1.2)
***** error<raylfit: wrong value for ALPHA.> raylfit ([1 2 3], [0.02 0.05])
***** error<raylfit: X and CENSOR vectors mismatch.> ...
 raylfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<raylfit: X and CENSOR vectors mismatch.> ...
 raylfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<raylfit: X and FREQ vectors mismatch.> ...
 raylfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<raylfit: X and FREQ vectors mismatch.> ...
 raylfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<raylfit: X and FREQ vectors mismatch.>
 raylfit ([1 2 3], [], [], [1 5])
***** error<raylfit: FREQ must not contain negative values.>
 raylfit ([1 2 3], [], [], [1 5 -1])
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fit/nbinlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/nbinlike.m
***** assert (nbinlike ([2.42086, 0.0867043], [1:50]), 205.5942, 1e-4)
***** assert (nbinlike ([3.58823, 0.254697], [1:20]), 63.6435, 1e-4)
***** assert (nbinlike ([8.80671, 0.615565], [1:10]), 24.7410, 1e-4)
***** assert (nbinlike ([22.1756, 0.831306], [1:8]), 17.9528, 1e-4)
***** assert (nbinlike ([22.1756, 0.831306], [1:9], [ones(1,8), 0]), 17.9528, 1e-4)
***** error<nbinlike: function called with too few input arguments.> nbinlike (3.25)
***** error<nbinlike: X must be a vector.> nbinlike ([5, 0.2], ones (2))
***** error<nbinlike: X cannot have negative values.> nbinlike ([5, 0.2], [-1, 3])
***** error<nbinlike: PARAMS must be a two-element vector.> ...
 nbinlike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<nbinlike: number of successes,> nbinlike ([-5, 0.2], [1:15])
***** error<nbinlike: number of successes,> nbinlike ([0, 0.2], [1:15])
***** error<nbinlike: probability of success,> nbinlike ([5, 1.2], [3, 5])
***** error<nbinlike: probability of success,> nbinlike ([5, -0.2], [3, 5])
***** error<nbinlike: X and FREQ vectors mismatch.> ...
 nbinlike ([5, 0.2], ones (10, 1), ones (8,1))
***** error<nbinlike: FREQ must not contain negative values.> ...
 nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 -1])
***** error<nbinlike: FREQ must contain integer values.> ...
 nbinlike ([5, 0.2], ones (1, 8), [1 1 1 1 1 1 1 1.5])
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fit/geofit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/geofit.m
***** demo
 ## Sample 2 populations from different geometric distributions
 rande ("seed", 1);    # for reproducibility
 r1 = geornd (0.15, 1000, 1);
 rande ("seed", 2);    # for reproducibility
 r2 = geornd (0.5, 1000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 0:0.5:20.5, 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 pshatA = geofit (r(:,1));
 pshatB = geofit (r(:,2));

 ## Plot their estimated PDFs
 x = [0:15];
 y = geopdf (x, pshatA);
 plot (x, y, "-pg");
 y = geopdf (x, pshatB);
 plot (x, y, "-sc");
 xlim ([0, 15])
 ylim ([0, 0.6])
 legend ({"Normalized HIST of sample 1 with ps=0.15", ...
          "Normalized HIST of sample 2 with ps=0.50", ...
          sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
                  mean (pshatA)), ...
          sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
                  mean (pshatB))})
 title ("Two population samples from different geometric distributions")
 hold off
***** test
 x = 0:5;
 [pshat, psci] = geofit (x);
 assert (pshat, 0.2857, 1e-4);
 assert (psci, [0.092499; 0.478929], 1e-5);
***** test
 x = 0:5;
 [pshat, psci] = geofit (x, [], [1 1 1 1 1 1]);
 assert (pshat, 0.2857, 1e-4);
 assert (psci, [0.092499; 0.478929], 1e-5);
***** assert (geofit ([1 1 2 3]), geofit ([1 2 3], [] ,[2 1 1]))
***** error<geofit: function called with too few input arguments.> geofit ()
***** error<geofit: X cannot have negative values.> geofit (-1, [1 2 3 3])
***** error<geofit: wrong value for ALPHA.> geofit (1, 0)
***** error<geofit: wrong value for ALPHA.> geofit (1, 1.2)
***** error<geofit: wrong value for ALPHA.> geofit (1, [0.02 0.05])
***** error<geofit: X and FREQ vector mismatch.> ...
 geofit ([1.5, 0.2], [], [0, 0, 0, 0, 0])
***** error<geofit: X and FREQ vector mismatch.> ...
 geofit ([1.5, 0.2], [], [1, 1, 1])
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/betafit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/betafit.m
***** demo
 ## Sample 2 populations from different Beta distributions
 randg ("seed", 1);   # for reproducibility
 r1 = betarnd (2, 5, 500, 1);
 randg ("seed", 2);   # for reproducibility
 r2 = betarnd (2, 2, 500, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 12, 15);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their shape parameters
 a_b_A = betafit (r(:,1));
 a_b_B = betafit (r(:,2));

 ## Plot their estimated PDFs
 x = [min(r(:)):0.01:max(r(:))];
 y = betapdf (x, a_b_A(1), a_b_A(2));
 plot (x, y, "-pr");
 y = betapdf (x, a_b_B(1), a_b_B(2));
 plot (x, y, "-sg");
 ylim ([0, 4])
 legend ({"Normalized HIST of sample 1 with α=2 and β=5", ...
          "Normalized HIST of sample 2 with α=2 and β=2", ...
          sprintf("PDF for sample 1 with estimated α=%0.2f and β=%0.2f", ...
                  a_b_A(1), a_b_A(2)), ...
          sprintf("PDF for sample 2 with estimated α=%0.2f and β=%0.2f", ...
                  a_b_B(1), a_b_B(2))})
 title ("Two population samples from different Beta distributions")
 hold off
***** test
 x = 0.01:0.02:0.99;
 [paramhat, paramci] = betafit (x);
 paramhat_out = [1.0199, 1.0199];
 paramci_out = [0.6947, 0.6947; 1.4974, 1.4974];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 0.01:0.02:0.99;
 [paramhat, paramci] = betafit (x, 0.01);
 paramci_out = [0.6157, 0.6157; 1.6895, 1.6895];
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 0.00:0.02:1;
 [paramhat, paramci] = betafit (x);
 paramhat_out = [0.0875, 0.1913];
 paramci_out = [0.0822, 0.1490; 0.0931, 0.2455];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** error<betafit: X must be a vector of real values.> betafit ([0.2, 0.5+i]);
***** error<betafit: X must be a vector of real values.> betafit (ones (2,2) * 0.5);
***** error<betafit: X must be in the range> betafit ([0.5, 1.2]);
***** error<betafit: X must contain distinct values.> betafit ([0.1, 0.1]);
***** error<betafit: wrong value for ALPHA.> betafit ([0.01:0.1:0.99], 1.2);
***** error<betafit: X and FREQ vectors mismatch.> ...
 betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2]);
***** error<betafit: FREQ must not contain negative values.> ...
 betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, -1]);
***** error<betafit: FREQ must contain integer values.> ...
 betafit ([0.01:0.01:0.05], 0.05, [1, 2, 3, 2, 1.5]);
***** error<betafit: 'options' argument must be a structure> ...
 betafit ([0.01:0.01:0.05], 0.05, struct ("option", 234));
***** error<betafit: 'options' argument must be a structure> ...
 betafit ([0.01:0.01:0.05], 0.05, ones (1,5), struct ("option", 234));
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/ricefit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/ricefit.m
***** demo
 ## Sample 3 populations from different Gamma distributions
 randg ("seed", 5);    # for reproducibility
 randp ("seed", 6);
 r1 = ricernd (1, 2, 3000, 1);
 randg ("seed", 2);    # for reproducibility
 randp ("seed", 8);
 r2 = ricernd (2, 4, 3000, 1);
 randg ("seed", 7);    # for reproducibility
 randp ("seed", 9);
 r3 = ricernd (7.5, 1, 3000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 75, 4);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.7]);
 xlim ([0, 12]);
 hold on

 ## Estimate their α and β parameters
 s_sigmaA = ricefit (r(:,1));
 s_sigmaB = ricefit (r(:,2));
 s_sigmaC = ricefit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01,0.1:0.2:18];
 y = ricepdf (x, s_sigmaA(1), s_sigmaA(2));
 plot (x, y, "-pr");
 y = ricepdf (x, s_sigmaB(1), s_sigmaB(2));
 plot (x, y, "-sg");
 y = ricepdf (x, s_sigmaC(1), s_sigmaC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with s=1 and σ=2", ...
          "Normalized HIST of sample 2 with s=2 and σ=4", ...
          "Normalized HIST of sample 3 with s=7.5 and σ=1", ...
          sprintf("PDF for sample 1 with estimated s=%0.2f and σ=%0.2f", ...
                  s_sigmaA(1), s_sigmaA(2)), ...
          sprintf("PDF for sample 2 with estimated s=%0.2f and σ=%0.2f", ...
                  s_sigmaB(1), s_sigmaB(2)), ...
          sprintf("PDF for sample 3 with estimated s=%0.2f and σ=%0.2f", ...
                  s_sigmaC(1), s_sigmaC(2))})
 title ("Three population samples from different Rician distributions")
 hold off
***** test
 [paramhat, paramci] = ricefit ([1:50]);
 assert (paramhat, [15.3057, 17.6668], 1e-4);
 assert (paramci, [9.5468, 11.7802; 24.5383, 26.4952], 1e-4);
***** test
 [paramhat, paramci] = ricefit ([1:50], 0.01);
 assert (paramhat, [15.3057, 17.6668], 1e-4);
 assert (paramci, [8.2309, 10.3717; 28.4615, 30.0934], 1e-4);
***** test
 [paramhat, paramci] = ricefit ([1:5]);
 assert (paramhat, [2.3123, 1.6812], 1e-4);
 assert (paramci, [1.0819, 0.6376; 4.9424, 4.4331], 1e-4);
***** test
 [paramhat, paramci] = ricefit ([1:5], 0.01);
 assert (paramhat, [2.3123, 1.6812], 1e-4);
 assert (paramci, [0.8521, 0.4702; 6.2747, 6.0120], 1e-4);
***** test
 freq = [1 1 1 1 5];
 [paramhat, paramci] = ricefit ([1:5], [], [], freq);
 assert (paramhat, [3.5181, 1.5565], 1e-4);
 assert (paramci, [2.5893, 0.9049; 4.7801, 2.6772], 1e-4);
***** test
 censor = [1 0 0 0 0];
 [paramhat, paramci] = ricefit ([1:5], [], censor);
 assert (paramhat, [3.2978, 1.1527], 1e-4);
 assert (paramci, [2.3192, 0.5476; 4.6895, 2.4261], 1e-4);
***** assert (class (ricefit (single ([1:50]))), "single")
***** error<ricefit: X must be a vector.> ricefit (ones (2))
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], 1)
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], -1)
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], {0.05})
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], "k")
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], i)
***** error<ricefit: wrong value for ALPHA.> ricefit ([1:50], [0.01 0.02])
***** error<ricefit: X and CENSOR vectors mismatch.> ricefit ([1:50], [], [1 1])
***** error<ricefit: X and FREQ vectors mismatch.> ricefit ([1:50], [], [], [1 1])
***** error<ricefit: FREQ cannot have negative values.> ...
 ricefit ([1:5], [], [], [1, 1, 2, 1, -1])
***** error<ricefit: X must contain positive values.> ricefit ([1 2 3 -4])
***** error<ricefit: X must contain positive values.> ricefit ([1 2 0], [], [1 0 0])
19 tests, 19 passed, 0 known failure, 0 skipped
[inst/dist_fit/tlsfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/tlsfit.m
***** demo
 ## Sample 3 populations from 3 different location-scale T distributions
 randn ("seed", 1);    # for reproducibility
 randg ("seed", 2);    # for reproducibility
 r1 = tlsrnd (-4, 3, 1, 2000, 1);
 randn ("seed", 3);    # for reproducibility
 randg ("seed", 4);    # for reproducibility
 r2 = tlsrnd (0, 3, 1, 2000, 1);
 randn ("seed", 5);    # for reproducibility
 randg ("seed", 6);    # for reproducibility
 r3 = tlsrnd (5, 5, 4, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [-21:21], [1, 1, 1]);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.25]);
 xlim ([-20, 20]);
 hold on

 ## Estimate their lambda parameter
 mu_sigma_nuA = tlsfit (r(:,1));
 mu_sigma_nuB = tlsfit (r(:,2));
 mu_sigma_nuC = tlsfit (r(:,3));

 ## Plot their estimated PDFs
 x = [-20:0.1:20];
 y = tlspdf (x, mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3));
 plot (x, y, "-pr");
 y = tlspdf (x, mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3));
 plot (x, y, "-sg");
 y = tlspdf (x, mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with μ=0, σ=2 and nu=1", ...
          "Normalized HIST of sample 2 with μ=5, σ=2 and nu=1", ...
          "Normalized HIST of sample 3 with μ=3, σ=4 and nu=3", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
                  mu_sigma_nuA(1), mu_sigma_nuA(2), mu_sigma_nuA(3)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
                  mu_sigma_nuB(1), mu_sigma_nuB(2), mu_sigma_nuB(3)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f, σ=%0.2f, and ν=%0.2f", ...
                  mu_sigma_nuC(1), mu_sigma_nuC(2), mu_sigma_nuC(3))})
 title ("Three population samples from different location-scale T distributions")
 hold off
***** test
 x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
 [paramhat, paramci] = tlsfit (x);
 paramhat_out = [0.035893, 0.862711, 0.649261];
 paramci_out = [-0.949034, 0.154655, 0.181080; 1.02082, 4.812444, 2.327914];
 assert (paramhat, paramhat_out, 1e-6);
 assert (paramci, paramci_out, 1e-5);
***** test
 x = [-1.2352, -0.2741, 0.1726, 7.4356, 1.0392, 16.4165];
 [paramhat, paramci] = tlsfit (x, 0.01);
 paramci_out = [-1.2585, 0.0901, 0.1212; 1.3303, 8.2591, 3.4771];
 assert (paramci, paramci_out, 1e-4);
***** error<tlsfit: X must be a vector.> tlsfit (ones (2,5));
***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 1.2);
***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], 0);
***** error<tlsfit: wrong value for ALPHA.> tlsfit ([1, 2, 3, 4, 5], "alpha");
***** error<tlsfit: X and CENSOR vectors mismatch.> ...
 tlsfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<tlsfit: X and CENSOR vectors mismatch.> ...
 tlsfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<tlsfit: X and FREQ vectors mismatch.> ...
 tlsfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<tlsfit: X and FREQ vectors mismatch.> ...
 tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<tlsfit: FREQ cannot have negative values.> ...
 tlsfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 -1]);
***** error<tlsfit: 'options' 5th argument must be a structure> ...
 tlsfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
12 tests, 12 passed, 0 known failure, 0 skipped
[inst/dist_fit/ricelike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/ricelike.m
***** test
 nlogL = ricelike ([15.3057344, 17.6668458], [1:50]);
 assert (nlogL, 204.5230311010569, 1e-12);
***** test
 nlogL = ricelike ([2.312346885, 1.681228265], [1:5]);
 assert (nlogL, 8.65562164930058, 1e-12);
***** error<ricelike: function called with too few input arguments.> ricelike (3.25)
***** error<ricelike: X must be a vector.> ricelike ([5, 0.2], ones (2))
***** error<ricelike: PARAMS must be a two-element vector.> ...
 ricelike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<ricelike: X and CENSOR vector mismatch.> ...
 ricelike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<ricelike: X and FREQ vector mismatch.> ...
 ricelike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<ricelike: X and FREQ vector mismatch.> ...
 ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1])
***** error<ricelike: FREQ must not contain negative values.> ...
 ricelike ([1.5, 0.2], [1:5], [], [1, 1, 1, 0, -1])
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/nbinfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/nbinfit.m
***** demo
 ## Sample 2 populations from different negative binomial distributions
 randp ("seed", 5); randg ("seed", 5);    # for reproducibility
 r1 = nbinrnd (2, 0.15, 5000, 1);
 randp ("seed", 8); randg ("seed", 8);    # for reproducibility
 r2 = nbinrnd (5, 0.2, 5000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, [0:51], 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 r_psA = nbinfit (r(:,1));
 r_psB = nbinfit (r(:,2));

 ## Plot their estimated PDFs
 x = [0:40];
 y = nbinpdf (x, r_psA(1), r_psA(2));
 plot (x, y, "-pg");
 x = [min(r(:,2)):max(r(:,2))];
 y = nbinpdf (x, r_psB(1), r_psB(2));
 plot (x, y, "-sc");
 ylim ([0, 0.1])
 xlim ([0, 50])
 legend ({"Normalized HIST of sample 1 with r=2 and ps=0.15", ...
          "Normalized HIST of sample 2 with r=5 and ps=0.2", ...
          sprintf("PDF for sample 1 with estimated r=%0.2f and ps=%0.2f", ...
                  r_psA(1), r_psA(2)), ...
          sprintf("PDF for sample 2 with estimated r=%0.2f and ps=%0.2f", ...
                  r_psB(1), r_psB(2))})
 title ("Two population samples from negative different binomial distributions")
 hold off
***** test
 [paramhat, paramci] = nbinfit ([1:50]);
 assert (paramhat, [2.420857, 0.086704], 1e-6);
 assert (paramci(:,1), [1.382702; 3.459012], 1e-6);
 assert (paramci(:,2), [0.049676; 0.123732], 1e-6);
***** test
 [paramhat, paramci] = nbinfit ([1:20]);
 assert (paramhat, [3.588233, 0.254697], 1e-6);
 assert (paramci(:,1), [0.451693; 6.724774], 1e-6);
 assert (paramci(:,2), [0.081143; 0.428251], 1e-6);
***** test
 [paramhat, paramci] = nbinfit ([1:10]);
 assert (paramhat, [8.8067, 0.6156], 1e-4);
 assert (paramci(:,1), [0; 30.7068], 1e-4);
 assert (paramci(:,2), [0.0217; 1], 1e-4);
***** test
 [paramhat, paramci] = nbinfit ([1:10], 0.05, ones (1, 10));
 assert (paramhat, [8.8067, 0.6156], 1e-4);
 assert (paramci(:,1), [0; 30.7068], 1e-4);
 assert (paramci(:,2), [0.0217; 1], 1e-4);
***** test
 [paramhat, paramci] = nbinfit ([1:11], 0.05, [ones(1, 10), 0]);
 assert (paramhat, [8.8067, 0.6156], 1e-4);
 assert (paramci(:,1), [0; 30.7068], 1e-4);
 assert (paramci(:,2), [0.0217; 1], 1e-4);
***** error<nbinfit: X cannot have negative values.> nbinfit ([-1 2 3 3])
***** error<nbinfit: X must be a vector.> nbinfit (ones (2))
***** error<nbinfit: X must be a non-negative integer.> nbinfit ([1 2 1.2 3])
***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 0)
***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], 1.2)
***** error<nbinfit: wrong value for ALPHA.> nbinfit ([1 2 3], [0.02 0.05])
***** error<nbinfit: X and FREQ vectors mismatch.> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2]);
***** error<nbinfit: FREQ must not contain negative values.> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, -1]);
***** error<nbinfit: FREQ must contain integer values.> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, [1, 2, 3, 2, 1.5]);
***** error<nbinfit: 'options' argument must be a structure> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, struct ("option", 234));
***** error<nbinfit: 'options' argument must be a structure> ...
 nbinfit ([1, 2, 3, 4, 5], 0.05, ones (1,5), struct ("option", 234));
16 tests, 16 passed, 0 known failure, 0 skipped
[inst/dist_fit/gumbelfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/gumbelfit.m
***** demo
 ## Sample 3 populations from different Gumbel distributions
 rand ("seed", 1);    # for reproducibility
 r1 = gumbelrnd (2, 5, 400, 1);
 rand ("seed", 11);    # for reproducibility
 r2 = gumbelrnd (-5, 3, 400, 1);
 rand ("seed", 16);    # for reproducibility
 r3 = gumbelrnd (14, 8, 400, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 25, 0.32);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.28])
 xlim ([-11, 50]);
 hold on

 ## Estimate their MU and BETA parameters
 mu_betaA = gumbelfit (r(:,1));
 mu_betaB = gumbelfit (r(:,2));
 mu_betaC = gumbelfit (r(:,3));

 ## Plot their estimated PDFs
 x = [min(r(:)):max(r(:))];
 y = gumbelpdf (x, mu_betaA(1), mu_betaA(2));
 plot (x, y, "-pr");
 y = gumbelpdf (x, mu_betaB(1), mu_betaB(2));
 plot (x, y, "-sg");
 y = gumbelpdf (x, mu_betaC(1), mu_betaC(2));
 plot (x, y, "-^c");
 legend ({"Normalized HIST of sample 1 with μ=2 and β=5", ...
          "Normalized HIST of sample 2 with μ=-5 and β=3", ...
          "Normalized HIST of sample 3 with μ=14 and β=8", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and β=%0.2f", ...
                  mu_betaA(1), mu_betaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and β=%0.2f", ...
                  mu_betaB(1), mu_betaB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and β=%0.2f", ...
                  mu_betaC(1), mu_betaC(2))})
 title ("Three population samples from different Gumbel distributions")
 hold off
***** test
 x = 1:50;
 [paramhat, paramci] = gumbelfit (x);
 paramhat_out = [18.3188, 13.0509];
 paramci_out = [14.4882, 10.5294; 22.1495, 16.1763];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 1:50;
 [paramhat, paramci] = gumbelfit (x, 0.01);
 paramci_out = [13.2845, 9.8426; 23.3532, 17.3051];
 assert (paramci, paramci_out, 1e-4);
***** error<gumbelfit: X must be a double-precision vector.> gumbelfit (ones (2,5));
***** error<gumbelfit: X must be a double-precision vector.> ...
 gumbelfit (single (ones (1,5)));
***** error<gumbelfit: X must NOT contain missing values> ...
 gumbelfit ([1, 2, 3, 4, NaN]);
***** error<gumbelfit: wrong value for ALPHA.> gumbelfit ([1, 2, 3, 4, 5], 1.2);
***** error<gumbelfit: X and CENSOR vectors mismatch.> ...
 gumbelfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<gumbelfit: X and FREQ vectors mismatch.> ...
 gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [1 1 0]);
***** error<gamfit: FREQ must not contain negative values.>
 gamfit ([1, 2, 3], 0.05, [], [1 5 -1])
***** error<gumbelfit: 'options' 5th argument> ...
 gumbelfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/burrfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/burrfit.m
***** demo
 ## Sample 3 populations from different Burr type XII distributions
 rand ("seed", 4);    # for reproducibility
 r1 = burrrnd (3.5, 2, 2.5, 10000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = burrrnd (1, 3, 1, 10000, 1);
 rand ("seed", 9);    # for reproducibility
 r3 = burrrnd (0.5, 2, 3, 10000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0.1:0.2:20], [18, 5, 3]);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 3]);
 xlim ([0, 5]);
 hold on

 ## Estimate their α and β parameters
 lambda_c_kA = burrfit (r(:,1));
 lambda_c_kB = burrfit (r(:,2));
 lambda_c_kC = burrfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0.01:0.15:15];
 y = burrpdf (x, lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3));
 plot (x, y, "-pr");
 y = burrpdf (x, lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3));
 plot (x, y, "-sg");
 y = burrpdf (x, lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with λ=3.5, c=2, and k=2.5", ...
          "Normalized HIST of sample 2 with λ=1, c=3, and k=1", ...
          "Normalized HIST of sample 3 with λ=0.5, c=2, and k=3", ...
  sprintf("PDF for sample 1 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
          lambda_c_kA(1), lambda_c_kA(2), lambda_c_kA(3)), ...
  sprintf("PDF for sample 2 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
          lambda_c_kB(1), lambda_c_kB(2), lambda_c_kB(3)), ...
  sprintf("PDF for sample 3 with estimated λ=%0.2f, c=%0.2f, and k=%0.2f", ...
          lambda_c_kC(1), lambda_c_kC(2), lambda_c_kC(3))})
 title ("Three population samples from different Burr type XII distributions")
 hold off
***** test
 l = 1; c = 2; k = 3;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** test
 l = 0.5; c = 1; k = 3;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** test
 l = 1; c = 3; k = 1;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** test
 l = 3; c = 2; k = 1;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** test
 l = 4; c = 2; k = 4;
 r = burrrnd (l, c, k, 100000, 1);
 lambda_c_kA = burrfit (r);
 assert (lambda_c_kA(1), l, 0.2);
 assert (lambda_c_kA(2), c, 0.2);
 assert (lambda_c_kA(3), k, 0.3);
***** error<burrfit: X must be a vector.> burrfit (ones (2,5));
***** error<burrfit: X must contain only positive values.> burrfit ([-1 2 3 4]);
***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 1.2);
***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], 0);
***** error<burrfit: wrong value for ALPHA.> burrfit ([1, 2, 3, 4, 5], "alpha");
***** error<burrfit: X and CENSOR vectors mismatch.> ...
 burrfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<burrfit: X and CENSOR vectors mismatch.> ...
 burrfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<burrfit: X and FREQ vectors mismatch.>
 burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 1, 5])
***** error<burrfit: FREQ must not contain negative values.>
 burrfit ([1, 2, 3, 4, 5], 0.05, [], [1, 5, 1, 1, -1])
***** error<burrfit: 'options' 5th argument must be a structure> ...
 burrfit ([1:10], 0.05, [], [], 5)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fit/gplike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/gplike.m
***** test
 k = 0.8937; sigma = 1.3230; theta = 1;
 x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
      1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
 [nlogL, acov] = gplike ([k, sigma, theta], x);
 assert (nlogL, 21.736, 1e-3);
 assert (acov, [0.7249, -0.7351, 0; -0.7351, 1.3040, 0; 0, 0, 0], 1e-4);
***** assert (gplike ([2, 3, 0], 4), 3.047536764863501, 1e-14)
***** assert (gplike ([2, 3, 4], 8), 3.047536764863501, 1e-14)
***** assert (gplike ([1, 2, 0], 4), 2.890371757896165, 1e-14)
***** assert (gplike ([1, 2, 4], 8), 2.890371757896165, 1e-14)
***** assert (gplike ([2, 3, 0], [1:10]), 32.57864322725392, 1e-14)
***** assert (gplike ([2, 3, 2], [1:10] + 2), 32.57864322725392, 1e-14)
***** assert (gplike ([2, 3, 0], [1:10], ones (1,10)), 32.57864322725392, 1e-14)
***** assert (gplike ([1, 2, 0], [1:10]), 31.65666282460443, 1e-14)
***** assert (gplike ([1, 2, 3], [1:10] + 3), 31.65666282460443, 1e-14)
***** assert (gplike ([1, 2, 0], [1:10], ones (1,10)), 31.65666282460443, 1e-14)
***** assert (gplike ([1, NaN, 0], [1:10]), NaN)
***** error<gplike: function called with too few input arguments.> gplike ()
***** error<gplike: function called with too few input arguments.> gplike (1)
***** error<gplike: X must be a vector.> gplike ([1, 2, 0], [])
***** error<gplike: X must be a vector.> gplike ([1, 2, 0], ones (2))
***** error<gplike: PARAMS must be a three-element vector.> gplike (2, [1:10])
***** error<gplike: PARAMS must be a three-element vector.> gplike ([2, 3], [1:10])
***** error<gplike: X and FREQ vectors mismatch.> ...
 gplike ([1, 2, 0], ones (10, 1), ones (8,1))
***** error<gplike: FREQ must not contain negative values.> ...
 gplike ([1, 2, 0], ones (1, 8), [1 1 1 1 1 1 1 -1])
20 tests, 20 passed, 0 known failure, 0 skipped
[inst/dist_fit/betalike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/betalike.m
***** test
 x = 0.01:0.02:0.99;
 [nlogL, avar] = betalike ([2.3, 1.2], x);
 avar_out = [0.03691678, 0.02803056; 0.02803056, 0.03965629];
 assert (nlogL, 17.873477715879040, 3e-14);
 assert (avar, avar_out, 1e-7);
***** test
 x = 0.01:0.02:0.99;
 [nlogL, avar] = betalike ([1, 4], x);
 avar_out = [0.02793282, 0.02717274; 0.02717274, 0.03993361];
 assert (nlogL, 79.648061114839550, 1e-13);
 assert (avar, avar_out, 1e-7);
***** test
 x = 0.00:0.02:1;
 [nlogL, avar] = betalike ([1, 4], x);
 avar_out = [0.00000801564765, 0.00000131397245; ...
             0.00000131397245, 0.00070827639442];
 assert (nlogL, 573.2008434477486, 1e-10);
 assert (avar, avar_out, 1e-14);
***** error<betalike: function called with too few input arguments.> ...
 betalike ([12, 15]);
***** error<betalike: wrong parameters length.> betalike ([12, 15, 3], [1:50]);
***** error<betalike: X and FREQ vectors mismatch.> ...
 betalike ([12, 15], ones (10, 1), ones (8,1))
***** error<betalike: FREQ must not contain negative values.> ...
 betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 -1])
***** error<betalike: FREQ must contain integer values.> ...
 betalike ([12, 15], ones (1, 8), [1 1 1 1 1 1 1 1.5])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/invglike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/invglike.m
***** test
 nlogL = invglike ([25.5, 19.6973], [1:50]);
 assert (nlogL, 219.1516, 1e-4);
***** test
 nlogL = invglike ([3, 8.1081], [1:5]);
 assert (nlogL, 9.0438, 1e-4);
***** error<invglike: function called with too few input arguments.> invglike (3.25)
***** error<invglike: X must be a vector.> invglike ([5, 0.2], ones (2))
***** error<invglike: X must have positive values.> invglike ([5, 0.2], [-1, 3])
***** error<invglike: PARAMS must be a two-element vector.> ...
 invglike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<invglike: X and CENSOR vector mismatch.> ...
 invglike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<invglike: X and FREQ vector mismatch.> ...
 invglike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<invglike: X and FREQ vector mismatch.> ...
 invglike ([1.5, 0.2], [1:5], [], [1, 1, 1])
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/rayllike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/rayllike.m
***** test
 x = [1 3 2 4 5 4 3 4];
 [nlogL, acov] = rayllike (3.25, x);
 assert (nlogL, 14.7442, 1e-4)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [nlogL, acov] = rayllike (3.25, x, [], f);
 assert (nlogL, 14.7442, 1e-4)
***** test
 x = [1 2 3 4 5 6];
 f = [1 1 2 3 1 0];
 [nlogL, acov] = rayllike (3.25, x, [], f);
 assert (nlogL, 14.7442, 1e-4)
***** test
 x = [1 2 3 4 5 6];
 c = [0 0 0 0 0 1];
 f = [1 1 2 3 1 0];
 [nlogL, acov] = rayllike (3.25, x, c, f);
 assert (nlogL, 14.7442, 1e-4)
***** error<rayllike: function called with too few input arguments.> rayllike (1)
***** error<rayllike: SIGMA must be a positive scalar.> rayllike ([1 2 3], [1 2])
***** error<rayllike: X must be a vector of non-negative values.> ...
 rayllike (3.25, ones (10, 2))
***** error<rayllike: X must be a vector of non-negative values.> ...
 rayllike (3.25, [1 2 3 -4 5])
***** error<rayllike: X and CENSOR vectors mismatch.> ...
 rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0]);
***** error<rayllike: X and CENSOR vectors mismatch.> ...
 rayllike (3.25, [1, 2, 3, 4, 5], [1 1 0 1 1]');
***** error<rayllike: X and FREQ vectors mismatch.> ...
 rayllike (3.25, [1, 2, 3, 4, 5], zeros (1,5), [1 1 0]);
***** error<rayllike: X and FREQ vectors mismatch.> ...
 rayllike (3.25, [1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<rayllike: FREQ must not contain negative values.> ...
 rayllike (3.25, ones (1, 8), [], [1 1 1 1 1 1 1 -1])
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/evlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/evlike.m
***** test
 x = 1:50;
 [nlogL, acov] = evlike ([2.3, 1.2], x);
 avar_out = [-1.2778e-13, 3.1859e-15; 3.1859e-15, -7.9430e-17];
 assert (nlogL, 3.242264755689906e+17, 1e-14);
 assert (acov, avar_out, 1e-3);
***** test
 x = 1:50;
 [nlogL, acov] = evlike ([2.3, 1.2], x * 0.5);
 avar_out = [-7.6094e-05, 3.9819e-06; 3.9819e-06, -2.0836e-07];
 assert (nlogL, 481898704.0472211, 1e-6);
 assert (acov, avar_out, 1e-3);
***** test
 x = 1:50;
 [nlogL, acov] = evlike ([21, 15], x);
 avar_out = [11.73913876598908, -5.9546128523121216; ...
             -5.954612852312121, 3.708060045170236];
 assert (nlogL, 223.7612479380652, 1e-13);
 assert (acov, avar_out, 1e-14);
***** error<evlike: function called with too few input arguments.> evlike ([12, 15])
***** error<evlike: wrong parameters length.> evlike ([12, 15, 3], [1:50])
***** error<evlike: X must be a vector.> evlike ([12, 3], ones (10, 2))
***** error<evlike: X and CENSOR vectors mismatch.> ...
 evlike ([12, 15], [1:50], [1, 2, 3])
***** error<evlike: X and FREQ vectors mismatch.> ...
 evlike ([12, 15], [1:50], [], [1, 2, 3])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/burrlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/burrlike.m
***** error<burrlike: function called with too few input arguments.> burrlike (3.25)
***** error<burrlike: X must be a vector.> burrlike ([1, 2, 3], ones (2))
***** error<burrlike: X cannot have negative values.> burrlike ([1, 2, 3], [-1, 3])
***** error<burrlike: PARAMS must be a three-element vector.> ...
 burrlike ([1, 2], [1, 3, 5, 7])
***** error<burrlike: PARAMS must be a three-element vector.> ...
 burrlike ([1, 2, 3, 4], [1, 3, 5, 7])
***** error<burrlike: X and CENSOR vector mismatch.> ...
 burrlike ([1, 2, 3], [1:5], [0, 0, 0])
***** error<burrlike: X and FREQ vector mismatch.> ...
 burrlike ([1, 2, 3], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<burrlike: X and FREQ vector mismatch.> ...
 burrlike ([1, 2, 3], [1:5], [], [1, 1, 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/lognlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/lognlike.m
***** test
 x = 1:50;
 [nlogL, avar] = lognlike ([0, 0.25], x);
 avar_out = [-5.4749e-03, 2.8308e-04; 2.8308e-04, -1.1916e-05];
 assert (nlogL, 3962.330333301793, 1e-10);
 assert (avar, avar_out, 1e-7);
***** test
 x = 1:50;
 [nlogL, avar] = lognlike ([0, 0.25], x * 0.5);
 avar_out = [-7.6229e-03, 4.8722e-04; 4.8722e-04, -2.6754e-05];
 assert (nlogL, 2473.183051225747, 1e-10);
 assert (avar, avar_out, 1e-7);
***** test
 x = 1:50;
 [nlogL, avar] = lognlike ([0, 0.5], x);
 avar_out = [-2.1152e-02, 2.2017e-03; 2.2017e-03, -1.8535e-04];
 assert (nlogL, 1119.072424020455, 1e-12);
 assert (avar, avar_out, 1e-6);
***** test
 x = 1:50;
 censor = ones (1, 50);
 censor([2, 4, 6, 8, 12, 14]) = 0;
 [nlogL, avar] = lognlike ([0, 0.5], x, censor);
 avar_out = [-1.9823e-02, 2.0370e-03; 2.0370e-03, -1.6618e-04];
 assert (nlogL, 1091.746371145497, 1e-12);
 assert (avar, avar_out, 1e-6);
***** test
 x = 1:50;
 censor = ones (1, 50);
 censor([2, 4, 6, 8, 12, 14]) = 0;
 [nlogL, avar] = lognlike ([0, 1], x, censor);
 avar_out = [-6.8634e-02, 1.3968e-02; 1.3968e-02, -2.1664e-03];
 assert (nlogL, 349.3969104144271, 1e-12);
 assert (avar, avar_out, 1e-6);
***** error<lognlike: function called with too few input arguments.> ...
 lognlike ([12, 15]);
***** error<lognlike: X must be a vector.> lognlike ([12, 15], ones (2));
***** error<lognlike: PARAMS must be a two-element vector.> ...
 lognlike ([12, 15, 3], [1:50]);
***** error<lognlike: X and CENSOR vectors mismatch.> ...
 lognlike ([12, 15], [1:50], [1, 2, 3]);
***** error<lognlike: X and FREQ vectors mismatch.> ...
 lognlike ([12, 15], [1:50], [], [1, 2, 3]);
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/expfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/expfit.m
***** demo
 ## Sample 3 populations from 3 different exponential distributions
 rande ("seed", 1);   # for reproducibility
 r1 = exprnd (2, 4000, 1);
 rande ("seed", 2);   # for reproducibility
 r2 = exprnd (5, 4000, 1);
 rande ("seed", 3);   # for reproducibility
 r3 = exprnd (12, 4000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 48, 0.52);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their mu parameter
 muhat = expfit (r);

 ## Plot their estimated PDFs
 x = [0:max(r(:))];
 y = exppdf (x, muhat(1));
 plot (x, y, "-pr");
 y = exppdf (x, muhat(2));
 plot (x, y, "-sg");
 y = exppdf (x, muhat(3));
 plot (x, y, "-^c");
 ylim ([0, 0.6])
 xlim ([0, 40])
 legend ({"Normalized HIST of sample 1 with μ=2", ...
          "Normalized HIST of sample 2 with μ=5", ...
          "Normalized HIST of sample 3 with μ=12", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f", muhat(1)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f", muhat(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f", muhat(3))})
 title ("Three population samples from different exponential distributions")
 hold off
***** assert (expfit (1), 1)
***** assert (expfit (1:3), 2)
***** assert (expfit ([1:3]'), 2)
***** assert (expfit (1:3, []), 2)
***** assert (expfit (1:3, [], [], []), 2)
***** assert (expfit (magic (3)), [5 5 5])
***** assert (expfit (cat (3, magic (3), 2*magic (3))), cat (3,[5 5 5], [10 10 10]))
***** assert (expfit (1:3, 0.1, [0 0 0], [1 1 1]), 2)
***** assert (expfit ([1:3]', 0.1, [0 0 0]', [1 1 1]'), 2)
***** assert (expfit (1:3, 0.1, [0 0 0]', [1 1 1]'), 2)
***** assert (expfit (1:3, 0.1, [1 0 0], [1 1 1]), 3)
***** assert (expfit (1:3, 0.1, [0 0 0], [4 1 1]), 1.5)
***** assert (expfit (1:3, 0.1, [1 0 0], [4 1 1]), 4.5)
***** assert (expfit (1:3, 0.1, [1 0 1], [4 1 1]), 9)
***** assert (expfit (1:3, 0.1, [], [-1 1 1]), 4)
***** assert (expfit (1:3, 0.1, [], [0.5 1 1]), 2.2)
***** assert (expfit (1:3, 0.1, [1 1 1]), NaN)
***** assert (expfit (1:3, 0.1, [], [0 0 0]), NaN)
***** assert (expfit (reshape (1:9, [3 3])), [2 5 8])
***** assert (expfit (reshape (1:9, [3 3]), [], eye(3)), [3 7.5 12])
***** assert (expfit (reshape (1:9, [3 3]), [], 2*eye(3)), [3 7.5 12])
***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
 [1.75 4.75 7.75])
***** assert (expfit (reshape (1:9, [3 3]), [], [], [2 2 2; 1 1 1; 1 1 1]), ...
 [1.75 4.75 7.75])
***** assert (expfit (reshape (1:9, [3 3]), [], eye(3), [2 2 2; 1 1 1; 1 1 1]), ...
 [3.5 19/3 31/3])
***** assert ([~,muci] = expfit (1:3, 0), [0; Inf])
***** assert ([~,muci] = expfit (1:3, 2), [Inf; 0])
***** assert ([~,muci] = expfit (1:3, 0.1, [1 1 1]), [NaN; NaN])
***** assert ([~,muci] = expfit (1:3, 0.1, [], [0 0 0]), [NaN; NaN])
***** assert ([~,muci] = expfit (1:3, -1), [NaN; NaN])
***** assert ([~,muci] = expfit (1:3, 5), [NaN; NaN])
***** assert ([~,muci] = expfit (1:3), [0.830485728373393; 9.698190330474096], ...
             1000*eps)
***** assert ([~,muci] = expfit (1:3, 0.1), ...
                          [0.953017262058213; 7.337731146400207], 1000*eps)
***** assert ([~,muci] = expfit ([1:3;2:4]), ...
             [0.538440777613095, 0.897401296021825, 1.256361814430554; ...
             12.385982973214016, 20.643304955356694, 28.900626937499371], ...
             1000*eps)
***** assert ([~,muci] = expfit ([1:3;2:4], [], [1 1 1; 0 0 0]), ...
             100*[0.008132550920455, 0.013554251534091, 0.018975952147727; ...
             1.184936706156216, 1.974894510260360, 2.764852314364504], ...
             1000*eps)
***** assert ([~,muci] = expfit ([1:3;2:4], [], [], [3 3 3; 1 1 1]), ...
             [0.570302756652583, 1.026544961974649, 1.482787167296715; ...
             4.587722594914109, 8.257900670845396, 11.928078746776684], ...
             1000*eps)
***** assert ([~,muci] = expfit ([1:3;2:4], [], [0 0 0; 1 1 1], [3 3 3; 1 1 1]), ...
             [0.692071440311161, 1.245728592560089, 1.799385744809018; ...
             8.081825275395081, 14.547285495711145, 21.012745716027212], ...
             1000*eps)
***** test
 x = reshape (1:8, [4 2]);
 x(4) = NaN;
 [muhat,muci] = expfit (x);
 assert ({muhat, muci}, {[NaN, 6.5], ...
         [NaN, 2.965574334593430;NaN, 23.856157493553368]}, 1000*eps);
***** test
 x = magic (3);
 censor = [0 1 0; 0 1 0; 0 1 0];
 freq = [1 1 0; 1 1 0; 1 1 0];
 [muhat,muci] = expfit (x, [], censor, freq);
 assert ({muhat, muci}, {[5 NaN NaN], ...
                 [[2.076214320933482; 24.245475826185242],NaN(2)]}, 1000*eps);
***** error expfit ()
***** error expfit (1,2,3,4,5)
***** error [a b censor] = expfit (1)
***** error <ALPHA must be a scalar quantity> expfit (1, [1 2])
***** error <X cannot be negative> expfit ([-1 2 3 4 5])
***** error <CENSOR must be a numeric or logical array> expfit ([1:5], [], "test")
***** error <FREQ must be a numeric or logical array> expfit ([1:5], [], [], "test")
***** error <X and CENSOR vectors mismatch.> expfit ([1:5], [], [0 0 0 0])
***** error <X and FREQ vectors mismatch.> expfit ([1:5], [], [], [1 1 1 1])
47 tests, 47 passed, 0 known failure, 0 skipped
[inst/dist_fit/gamlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/gamlike.m
***** test
 [nlogL, acov] = gamlike([2, 3], [2, 3, 4, 5, 6, 7, 8, 9]);
 assert (nlogL, 19.4426, 1e-4);
 assert (acov, [2.7819, -5.0073; -5.0073, 9.6882], 1e-4);
***** test
 [nlogL, acov] = gamlike([2, 3], [5:45]);
 assert (nlogL, 305.8070, 1e-4);
 assert (acov, [0.0423, -0.0087; -0.0087, 0.0167], 1e-4);
***** test
 [nlogL, acov] = gamlike([2, 13], [5:45]);
 assert (nlogL, 163.2261, 1e-4);
 assert (acov, [0.2362, -1.6631; -1.6631, 13.9440], 1e-4);
***** error<gamlike: function called with too few input arguments.> ...
 gamlike ([12, 15])
***** error<gamlike: wrong parameters length.> gamlike ([12, 15, 3], [1:50])
***** error<gamlike: X must be a vector.> gamlike ([12, 3], ones (10, 2))
***** error<gamlike: X and CENSOR vectors mismatch.> ...
 gamlike ([12, 15], [1:50], [1, 2, 3])
***** error<gamlike: X and FREQ vectors mismatch.> ...
 gamlike ([12, 15], [1:50], [], [1, 2, 3])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/logilike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/logilike.m
***** test
 nlogL = logilike ([25.5, 8.7725], [1:50]);
 assert (nlogL, 206.6769, 1e-4);
***** test
 nlogL = logilike ([3, 0.8645], [1:5]);
 assert (nlogL, 9.0699, 1e-4);
***** error<logilike: function called with too few input arguments.> logilike (3.25)
***** error<logilike: X must be a vector.> logilike ([5, 0.2], ones (2))
***** error<logilike: PARAMS must be a two-element vector.> ...
 logilike ([1, 0.2, 3], [1, 3, 5, 7])
***** error<logilike: X and CENSOR vector mismatch.> ...
 logilike ([1.5, 0.2], [1:5], [0, 0, 0])
***** error<logilike: X and FREQ vector mismatch.> ...
 logilike ([1.5, 0.2], [1:5], [0, 0, 0, 0, 0], [1, 1, 1])
***** error<logilike: X and FREQ vector mismatch.> ...
 logilike ([1.5, 0.2], [1:5], [], [1, 1, 1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/evfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/evfit.m
***** demo
 ## Sample 3 populations from different extreme value distributions
 rand ("seed", 1);    # for reproducibility
 r1 = evrnd (2, 5, 400, 1);
 rand ("seed", 12);    # for reproducibility
 r2 = evrnd (-5, 3, 400, 1);
 rand ("seed", 13);    # for reproducibility
 r3 = evrnd (14, 8, 400, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 25, 0.4);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 0.28])
 xlim ([-30, 30]);
 hold on

 ## Estimate their MU and SIGMA parameters
 mu_sigmaA = evfit (r(:,1));
 mu_sigmaB = evfit (r(:,2));
 mu_sigmaC = evfit (r(:,3));

 ## Plot their estimated PDFs
 x = [min(r(:)):max(r(:))];
 y = evpdf (x, mu_sigmaA(1), mu_sigmaA(2));
 plot (x, y, "-pr");
 y = evpdf (x, mu_sigmaB(1), mu_sigmaB(2));
 plot (x, y, "-sg");
 y = evpdf (x, mu_sigmaC(1), mu_sigmaC(2));
 plot (x, y, "-^c");
 legend ({"Normalized HIST of sample 1 with μ=2 and σ=5", ...
          "Normalized HIST of sample 2 with μ=-5 and σ=3", ...
          "Normalized HIST of sample 3 with μ=14 and σ=8", ...
          sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaA(1), mu_sigmaA(2)), ...
          sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaB(1), mu_sigmaB(2)), ...
          sprintf("PDF for sample 3 with estimated μ=%0.2f and σ=%0.2f", ...
                  mu_sigmaC(1), mu_sigmaC(2))})
 title ("Three population samples from different extreme value distributions")
 hold off
***** test
 x = 1:50;
 [paramhat, paramci] = evfit (x);
 paramhat_out = [32.6811, 13.0509];
 paramci_out = [28.8504, 10.5294; 36.5118, 16.1763];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 1:50;
 [paramhat, paramci] = evfit (x, 0.01);
 paramci_out = [27.6468, 9.8426; 37.7155, 17.3051];
 assert (paramci, paramci_out, 1e-4);
***** error<evfit: X must be a double-precision vector.> evfit (ones (2,5));
***** error<evfit: X must be a double-precision vector.> evfit (single (ones (1,5)));
***** error<evfit: X must NOT contain missing values> evfit ([1, 2, 3, 4, NaN]);
***** error<evfit: wrong value for ALPHA.> evfit ([1, 2, 3, 4, 5], 1.2);
***** error<evfit: X and FREQ vectors mismatch.>
 evfit ([1 2 3], 0.05, [], [1 5])
***** error<evfit: FREQ must not contain negative values.>
 evfit ([1 2 3], 0.05, [], [1 5 -1])
***** error<evfit: 'options' 5th argument must be a structure> ...
 evfit ([1:10], 0.05, [], [], 5)
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/gpfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/gpfit.m
***** demo
 ## Sample 2 populations from different generalized Pareto distributions
 ## Assume location parameter θ is known
 theta = 0;
 rand ("seed", 5);    # for reproducibility
 r1 = gprnd (1, 2, theta, 20000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = gprnd (3, 1, theta, 20000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, [0.1:0.2:100], 5);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "r");
 set (h(2), "facecolor", "c");
 ylim ([0, 1]);
 xlim ([0, 5]);
 hold on

 ## Estimate their α and β parameters
 k_sigmaA = gpfit (r(:,1), theta);
 k_sigmaB = gpfit (r(:,2), theta);

 ## Plot their estimated PDFs
 x = [0.01, 0.1:0.2:18];
 y = gppdf (x, k_sigmaA(1), k_sigmaA(2), theta);
 plot (x, y, "-pc");
 y = gppdf (x, k_sigmaB(1), k_sigmaB(2), theta);
 plot (x, y, "-sr");
 hold off
 legend ({"Normalized HIST of sample 1 with k=1 and σ=2", ...
          "Normalized HIST of sample 2 with k=2 and σ=2", ...
          sprintf("PDF for sample 1 with estimated k=%0.2f and σ=%0.2f", ...
                  k_sigmaA(1), k_sigmaA(2)), ...
          sprintf("PDF for sample 3 with estimated k=%0.2f and σ=%0.2f", ...
                  k_sigmaB(1), k_sigmaB(2))})
 title ("Two population samples from different generalized Pareto distributions")
 text (2, 0.7, "Known location parameter θ = 0")
 hold off
***** test
 k = 0.8937; sigma = 1.3230; theta = 1;
 x = [2.2196, 11.9301, 4.3673, 1.0949, 6.5626, ...
      1.2109, 1.8576, 1.0039, 12.7917, 2.2590];
 [hat, ci] = gpfit (x, theta);
 assert (hat, [k, sigma, theta], 1e-4);
 assert (ci, [-0.7750, 0.2437, 1; 2.5624, 7.1820, 1], 1e-4);
***** error<gpfit: function called with too few input arguments.> gpfit ()
***** error<gpfit: function called with too few input arguments.> gpfit (1)
***** error<gpfit: X must be a vector of real values.> gpfit ([0.2, 0.5+i], 0);
***** error<gpfit: X must be a vector of real values.> gpfit (ones (2,2) * 0.5, 0);
***** error<gpfit: THETA must be a real scalar value.> ...
 gpfit ([0.5, 1.2], [0, 1]);
***** error<gpfit: THETA must be a real scalar value.> ...
 gpfit ([0.5, 1.2], 5+i);
***** error<gpfit: X cannot contain values less than THETA.> ...
 gpfit ([1:5], 2);
***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, 1.2);
***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, i);
***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, -1);
***** error<gpfit: wrong value for ALPHA.> gpfit ([0.01:0.1:0.99], 0, [0.05, 0.01]);
***** error<gpfit: X and FREQ vectors mismatch.>
 gpfit ([1 2 3], 0, [], [1 5])
***** error<gpfit: FREQ must not contain negative values.>
 gpfit ([1 2 3], 0, [], [1 5 -1])
***** error<gpfit: 'options' 5th argument must be a structure> ...
 gpfit ([1:10], 1, 0.05, [], 5)
15 tests, 15 passed, 0 known failure, 0 skipped
[inst/dist_fit/poisslike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/poisslike.m
***** test
 x = [1 3 2 4 5 4 3 4];
 [nlogL, avar] = poisslike (3.25, x);
 assert (nlogL, 13.9533, 1e-4)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [nlogL, avar] = poisslike (3.25, x, f);
 assert (nlogL, 13.9533, 1e-4)
***** error<poisslike: function called with too few input arguments.> poisslike (1)
***** error<poisslike: LAMBDA must be a positive scalar.> poisslike ([1 2 3], [1 2])
***** error<poisslike: X must be a vector of non-negative values.> ...
 poisslike (3.25, ones (10, 2))
***** error<poisslike: X must be a vector of non-negative values.> ...
 poisslike (3.25, [1 2 3 -4 5])
***** error<poisslike: X and FREQ vectors mismatch.> ...
 poisslike (3.25, ones (10, 1), ones (8,1))
***** error<poisslike: FREQ must not contain negative values.> ...
 poisslike (3.25, ones (1, 8), [1 1 1 1 1 1 1 -1])
8 tests, 8 passed, 0 known failure, 0 skipped
[inst/dist_fit/wblfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/wblfit.m
***** demo
 ## Sample 3 populations from 3 different Weibull distributions
 rande ("seed", 1);    # for reproducibility
 r1 = wblrnd(2, 4, 2000, 1);
 rande ("seed", 2);    # for reproducibility
 r2 = wblrnd(5, 2, 2000, 1);
 rande ("seed", 5);    # for reproducibility
 r3 = wblrnd(1, 5, 2000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, 30, [2.5 2.1 3.2]);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 ylim ([0, 2]);
 xlim ([0, 10]);
 hold on

 ## Estimate their lambda parameter
 lambda_kA = wblfit (r(:,1));
 lambda_kB = wblfit (r(:,2));
 lambda_kC = wblfit (r(:,3));

 ## Plot their estimated PDFs
 x = [0:0.1:15];
 y = wblpdf (x, lambda_kA(1), lambda_kA(2));
 plot (x, y, "-pr");
 y = wblpdf (x, lambda_kB(1), lambda_kB(2));
 plot (x, y, "-sg");
 y = wblpdf (x, lambda_kC(1), lambda_kC(2));
 plot (x, y, "-^c");
 hold off
 legend ({"Normalized HIST of sample 1 with λ=2 and k=4", ...
          "Normalized HIST of sample 2 with λ=5 and k=2", ...
          "Normalized HIST of sample 3 with λ=1 and k=5", ...
          sprintf("PDF for sample 1 with estimated λ=%0.2f and k=%0.2f", ...
                  lambda_kA(1), lambda_kA(2)), ...
          sprintf("PDF for sample 2 with estimated λ=%0.2f and k=%0.2f", ...
                  lambda_kB(1), lambda_kB(2)), ...
          sprintf("PDF for sample 3 with estimated λ=%0.2f and k=%0.2f", ...
                  lambda_kC(1), lambda_kC(2))})
 title ("Three population samples from different Weibull distributions")
 hold off
***** test
 x = 1:50;
 [paramhat, paramci] = wblfit (x);
 paramhat_out = [28.3636, 1.7130];
 paramci_out = [23.9531, 1.3551; 33.5861, 2.1655];
 assert (paramhat, paramhat_out, 1e-4);
 assert (paramci, paramci_out, 1e-4);
***** test
 x = 1:50;
 [paramhat, paramci] = wblfit (x, 0.01);
 paramci_out = [22.7143, 1.2589; 35.4179, 2.3310];
 assert (paramci, paramci_out, 1e-4);
***** error<wblfit: X must be a vector.> wblfit (ones (2,5));
***** error<wblfit: X must contain only positive values.> wblfit ([-1 2 3 4]);
***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 1.2);
***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], 0);
***** error<wblfit: wrong value for ALPHA.> wblfit ([1, 2, 3, 4, 5], "alpha");
***** error<wblfit: X and CENSOR vectors mismatch.> ...
 wblfit ([1, 2, 3, 4, 5], 0.05, [1 1 0]);
***** error<wblfit: X and CENSOR vectors mismatch.> ...
 wblfit ([1, 2, 3, 4, 5], [], [1 1 0 1 1]');
***** error<wblfit: X and FREQ vectors mismatch.> ...
 wblfit ([1, 2, 3, 4, 5], 0.05, zeros (1,5), [1 1 0]);
***** error<wblfit: FREQ cannot have negative values.> ...
 wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 -1 1]);
***** error<wblfit: X and FREQ vectors mismatch.> ...
 wblfit ([1, 2, 3, 4, 5], [], [], [1 1 0 1 1]');
***** error<wblfit: 'options' 5th argument must be a structure> ...
 wblfit ([1, 2, 3, 4, 5], 0.05, [], [], 2);
13 tests, 13 passed, 0 known failure, 0 skipped
[inst/dist_fit/wbllike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/wbllike.m
***** test
 x = 1:50;
 [nlogL, acov] = wbllike ([2.3, 1.2], x);
 avar_out = [0.0250, 0.0062; 0.0062, 0.0017];
 assert (nlogL, 945.9589180651594, 1e-12);
 assert (acov, avar_out, 1e-4);
***** test
 x = 1:50;
 [nlogL, acov] = wbllike ([2.3, 1.2], x * 0.5);
 avar_out = [-0.3238, -0.1112; -0.1112, -0.0376];
 assert (nlogL, 424.9879809704742, 6e-14);
 assert (acov, avar_out, 1e-4);
***** test
 x = 1:50;
 [nlogL, acov] = wbllike ([21, 15], x);
 avar_out = [-0.00001236, -0.00001166; -0.00001166, -0.00001009];
 assert (nlogL, 1635190.328991511, 1e-8);
 assert (acov, avar_out, 1e-8);
***** error<wbllike: too few input arguments.> wbllike ([12, 15]);
***** error<wbllike: wrong parameters length.> wbllike ([12, 15, 3], [1:50]);
***** error<wbllike: X must be a vector.> wbllike ([12, 3], ones (10, 2));
***** error<wbllike: X and CENSOR> wbllike ([12, 15], [1:50], [1, 2, 3]);
***** error<wbllike: X and FREQ> wbllike ([12, 15], [1:50], [], [1, 2, 3]);
***** error<wbllike: FREQ cannot have negative values.> ...
 wbllike ([12, 15], [1:5], [], [1, 2, 3, -1, 0]);
9 tests, 9 passed, 0 known failure, 0 skipped
[inst/dist_fit/binofit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/binofit.m
***** demo
 ## Sample 2 populations from different binomial distributions
 rand ("seed", 1);    # for reproducibility
 r1 = binornd (50, 0.15, 1000, 1);
 rand ("seed", 2);    # for reproducibility
 r2 = binornd (100, 0.5, 1000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 23, 0.35);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 pshatA = binofit (r(:,1), 50);
 pshatB = binofit (r(:,2), 100);

 ## Plot their estimated PDFs
 x = [min(r(:,1)):max(r(:,1))];
 y = binopdf (x, 50, mean (pshatA));
 plot (x, y, "-pg");
 x = [min(r(:,2)):max(r(:,2))];
 y = binopdf (x, 100, mean (pshatB));
 plot (x, y, "-sc");
 ylim ([0, 0.2])
 legend ({"Normalized HIST of sample 1 with ps=0.15", ...
          "Normalized HIST of sample 2 with ps=0.50", ...
          sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
                  mean (pshatA)), ...
          sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
                  mean (pshatB))})
 title ("Two population samples from different binomial distributions")
 hold off
***** test
 x = 0:3;
 [pshat, psci] = binofit (x, 3);
 assert (pshat, [0, 0.3333, 0.6667, 1], 1e-4);
 assert (psci(1,:), [0, 0.7076], 1e-4);
 assert (psci(2,:), [0.0084, 0.9057], 1e-4);
 assert (psci(3,:), [0.0943, 0.9916], 1e-4);
 assert (psci(4,:), [0.2924, 1.0000], 1e-4);
***** error<binofit: function called with too few input arguments.> ...
 binofit ([1 2 3 4])
***** error<binofit: X cannot have negative values.> ...
 binofit ([-1, 4, 3, 2], [1, 2, 3, 3])
***** error<binofit: X must be a vector.> binofit (ones(2), [1, 2, 3, 3])
***** error<binofit: N must be a non-negative integer.> ...
 binofit ([1, 4, 3, 2], [1, 2, -1, 3])
***** error<binofit: N must be a scalar or the same size as X.> ...
 binofit ([1, 4, 3, 2], [5, 5, 5])
***** error<binofit: N must be at least as large as X.> ...
 binofit ([1, 4, 3, 2], [5, 3, 5, 5])
***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 1.2);
***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, 0);
***** error<binofit: wrong value for ALPHA.> binofit ([1, 2, 1], 3, "alpha");
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/poissfit.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/poissfit.m
***** demo
 ## Sample 3 populations from 3 different Poisson distributions
 randp ("seed", 2);    # for reproducibility
 r1 = poissrnd (1, 1000, 1);
 randp ("seed", 2);    # for reproducibility
 r2 = poissrnd (4, 1000, 1);
 randp ("seed", 3);    # for reproducibility
 r3 = poissrnd (10, 1000, 1);
 r = [r1, r2, r3];

 ## Plot them normalized and fix their colors
 hist (r, [0:20], 1);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 set (h(3), "facecolor", "r");
 hold on

 ## Estimate their lambda parameter
 lambdahat = poissfit (r);

 ## Plot their estimated PDFs
 x = [0:20];
 y = poisspdf (x, lambdahat(1));
 plot (x, y, "-pr");
 y = poisspdf (x, lambdahat(2));
 plot (x, y, "-sg");
 y = poisspdf (x, lambdahat(3));
 plot (x, y, "-^c");
 xlim ([0, 20])
 ylim ([0, 0.4])
 legend ({"Normalized HIST of sample 1 with λ=1", ...
          "Normalized HIST of sample 2 with λ=4", ...
          "Normalized HIST of sample 3 with λ=10", ...
          sprintf("PDF for sample 1 with estimated λ=%0.2f", ...
                  lambdahat(1)), ...
          sprintf("PDF for sample 2 with estimated λ=%0.2f", ...
                  lambdahat(2)), ...
          sprintf("PDF for sample 3 with estimated λ=%0.2f", ...
                  lambdahat(3))})
 title ("Three population samples from different Poisson distributions")
 hold off
***** test
 x = [1 3 2 4 5 4 3 4];
 [lhat, lci] = poissfit (x);
 assert (lhat, 3.25)
 assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
***** test
 x = [1 3 2 4 5 4 3 4];
 [lhat, lci] = poissfit (x, 0.01);
 assert (lhat, 3.25)
 assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [lhat, lci] = poissfit (x, [], f);
 assert (lhat, 3.25)
 assert (lci, [2.123007901949543; 4.762003010390628], 1e-14)
***** test
 x = [1 2 3 4 5];
 f = [1 1 2 3 1];
 [lhat, lci] = poissfit (x, 0.01, f);
 assert (lhat, 3.25)
 assert (lci, [1.842572740234582; 5.281369033298528], 1e-14)
***** error<poissfit: X cannot have negative values.> poissfit ([1 2 -1 3])
***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 0)
***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], 1.2)
***** error<poissfit: wrong value for ALPHA.> poissfit ([1 2 3], [0.02 0.05])
***** error<poissfit: X and FREQ vectors mismatch.>
 poissfit ([1 2 3], [], [1 5])
***** error<poissfit: FREQ must not contain negative values.>
 poissfit ([1 2 3], [], [1 5 -1])
10 tests, 10 passed, 0 known failure, 0 skipped
[inst/dist_fit/normlike.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/dist_fit/normlike.m
***** error<normlike: too few input arguments.> normlike ([12, 15]);
***** error<normlike: X must be a vector.> normlike ([12, 15], ones (2));
***** error<normlike: PARAMS must be a two-element vector.> ...
 normlike ([12, 15, 3], [1:50]);
***** error<normlike: X and CENSOR vectors mismatch.> ...
 normlike ([12, 15], [1:50], [1, 2, 3]);
***** error<normlike: X and FREQ vectors mismatch.> ...
 normlike ([12, 15], [1:50], [], [1, 2, 3]);
***** error<normlike: FREQ must not contain negative values.> ...
 normlike ([12, 15], [1:5], [], [1, 2, 3, 2, -1]);
***** test
 x = 1:50;
 [nlogL, avar] = normlike ([2.3, 1.2], x);
 avar_out = [7.5767e-01, -1.8850e-02; -1.8850e-02, 4.8750e-04];
 assert (nlogL, 13014.95883783327, 1e-10);
 assert (avar, avar_out, 1e-4);
***** test
 x = 1:50;
 [nlogL, avar] = normlike ([2.3, 1.2], x * 0.5);
 avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
 assert (nlogL, 2854.802587833265, 1e-10);
 assert (avar, avar_out, 1e-4);
***** test
 x = 1:50;
 [nlogL, avar] = normlike ([21, 15], x);
 avar_out = [5.460474308300396, -1.600790513833993; ...
             -1.600790513833993, 2.667984189723321];
 assert (nlogL, 206.738325604233, 1e-12);
 assert (avar, avar_out, 1e-14);
***** test
 x = 1:50;
 censor = ones (1, 50);
 censor([2, 4, 6, 8, 12, 14]) = 0;
 [nlogL, avar] = normlike ([2.3, 1.2], x, censor);
 avar_out = [3.0501e-01, -1.5859e-02; -1.5859e-02, 9.1057e-04];
 assert (nlogL, Inf);
 assert (avar, [NaN, NaN; NaN, NaN]);
***** test
 x = 1:50;
 censor = ones (1, 50);
 censor([2, 4, 6, 8, 12, 14]) = 0;
 [nlogL, avar] = normlike ([21, 15], x, censor);
 avar_out = [24.4824488866131, -10.6649544179636; ...
             -10.6649544179636, 6.22827849965737];
 assert (nlogL, 86.9254371829733, 1e-12);
 assert (avar, avar_out, 8e-14);
11 tests, 11 passed, 0 known failure, 0 skipped
[inst/hmmgenerate.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/hmmgenerate.m
***** test
 len = 25;
 transprob = [0.8, 0.2; 0.4, 0.6];
 outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
 [sequence, states] = hmmgenerate (len, transprob, outprob);
 assert (length (sequence), len);
 assert (length (states), len);
 assert (min (sequence) >= 1);
 assert (max (sequence) <= columns (outprob));
 assert (min (states) >= 1);
 assert (max (states) <= rows (transprob));
***** test
 len = 25;
 transprob = [0.8, 0.2; 0.4, 0.6];
 outprob = [0.2, 0.4, 0.4; 0.7, 0.2, 0.1];
 symbols = {"A", "B", "C"};
 statenames = {"One", "Two"};
 [sequence, states] = hmmgenerate (len, transprob, outprob, ...
                      "symbols", symbols, "statenames", statenames);
 assert (length (sequence), len);
 assert (length (states), len);
 assert (strcmp (sequence, "A") + strcmp (sequence, "B") + ...
                                  strcmp (sequence, "C") == ones (1, len));
 assert (strcmp (states, "One") + strcmp (states, "Two") == ones (1, len));
2 tests, 2 passed, 0 known failure, 0 skipped
[inst/fitlm.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/fitlm.m
***** demo
 y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';

 [TAB,STATS] = fitlm (X,y,"linear","CategoricalVars",1,"display","on");
***** demo
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 brands = {'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'};
 popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
           'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};

 [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
                          "CategoricalVars",[1,2],"display","on");
***** test
 y =  [ 8.706 10.362 11.552  6.941 10.983 10.092  6.421 14.943 15.931 ...
        22.968 18.590 16.567 15.944 21.637 14.492 17.965 18.851 22.891 ...
        22.028 16.884 17.252 18.325 25.435 19.141 21.238 22.196 18.038 ...
        22.628 31.163 26.053 24.419 32.145 28.966 30.207 29.142 33.212 ...
        25.694 ]';
 X = [1 1 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5]';
 [TAB,STATS] = fitlm (X,y,"continuous",[],"display","off");
 [TAB,STATS] = fitlm (X,y,"CategoricalVars",1,"display","off");
 [TAB,STATS] = fitlm (X,y,"constant","categorical",1,"display","off");
 [TAB,STATS] = fitlm (X,y,"linear","categorical",1,"display","off");
 [TAB,STATS] = fitlm (X,y,[0,0;1,0],"categorical",1,"display","off");
 assert (TAB{2,2}, 10, 1e-04);
 assert (TAB{3,2}, 7.99999999999999, 1e-09);
 assert (TAB{4,2}, 8.99999999999999, 1e-09);
 assert (TAB{5,2}, 11.0001428571429, 1e-09);
 assert (TAB{6,2}, 19.0001111111111, 1e-09);
 assert (TAB{2,3}, 1.01775379540949, 1e-09);
 assert (TAB{3,3}, 1.64107868458008, 1e-09);
 assert (TAB{4,3}, 1.43932122062479, 1e-09);
 assert (TAB{5,3}, 1.48983900477565, 1e-09);
 assert (TAB{6,3}, 1.3987687997822, 1e-09);
 assert (TAB{2,6}, 9.82555903510687, 1e-09);
 assert (TAB{3,6}, 4.87484242844031, 1e-09);
 assert (TAB{4,6}, 6.25294748040552, 1e-09);
 assert (TAB{5,6}, 7.38344399756088, 1e-09);
 assert (TAB{6,6}, 13.5834536158296, 1e-09);
 assert (TAB{3,7}, 2.85812420217862e-05, 1e-12);
 assert (TAB{4,7}, 5.22936741204002e-07, 1e-06);
 assert (TAB{5,7}, 2.12794763209106e-08, 1e-07);
 assert (TAB{6,7}, 7.82091664406755e-15, 1e-08);
***** test
 popcorn = [5.5, 4.5, 3.5; 5.5, 4.5, 4.0; 6.0, 4.0, 3.0; ...
            6.5, 5.0, 4.0; 7.0, 5.5, 5.0; 7.0, 5.0, 4.5];
 brands = bsxfun (@times, ones(6,1), [1,2,3]);
 popper = bsxfun (@times, [1;1;1;2;2;2], ones(1,3));

 [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
                          "categoricalvars",[1,2],"display","off");
 assert (TAB{2,2}, 5.66666666666667, 1e-09);
 assert (TAB{3,2}, -1.33333333333333, 1e-09);
 assert (TAB{4,2}, -2.16666666666667, 1e-09);
 assert (TAB{5,2}, 1.16666666666667, 1e-09);
 assert (TAB{6,2}, -0.333333333333334, 1e-09);
 assert (TAB{7,2}, -0.166666666666667, 1e-09);
 assert (TAB{2,3}, 0.215165741455965, 1e-09);
 assert (TAB{3,3}, 0.304290309725089, 1e-09);
 assert (TAB{4,3}, 0.304290309725089, 1e-09);
 assert (TAB{5,3}, 0.304290309725089, 1e-09);
 assert (TAB{6,3}, 0.43033148291193, 1e-09);
 assert (TAB{7,3}, 0.43033148291193, 1e-09);
 assert (TAB{2,6}, 26.3362867542108, 1e-09);
 assert (TAB{3,6}, -4.38178046004138, 1e-09);
 assert (TAB{4,6}, -7.12039324756724, 1e-09);
 assert (TAB{5,6}, 3.83405790253621, 1e-09);
 assert (TAB{6,6}, -0.774596669241495, 1e-09);
 assert (TAB{7,6}, -0.387298334620748, 1e-09);
 assert (TAB{2,7}, 5.49841502258254e-12, 1e-09);
 assert (TAB{3,7}, 0.000893505495903642, 1e-09);
 assert (TAB{4,7}, 1.21291454302428e-05, 1e-09);
 assert (TAB{5,7}, 0.00237798044119407, 1e-09);
 assert (TAB{6,7}, 0.453570536021938, 1e-09);
 assert (TAB{7,7}, 0.705316781644046, 1e-09);
 ## Test with string ids for categorical variables
 brands = {'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'; ...
           'Gourmet', 'National', 'Generic'};
 popper = {'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; 'oil', 'oil', 'oil'; ...
           'air', 'air', 'air'; 'air', 'air', 'air'; 'air', 'air', 'air'};
 [TAB, STATS] = fitlm ({brands(:),popper(:)},popcorn(:),"interactions",...
                          "categoricalvars",[1,2],"display","off");
***** test
 load carsmall
 X = [Weight,Horsepower,Acceleration];
 [TAB, STATS] = fitlm (X, MPG,"constant","display","off");
 [TAB, STATS] = fitlm (X, MPG,"linear","display","off");
 assert (TAB{2,2}, 47.9767628118615, 1e-09);
 assert (TAB{3,2}, -0.00654155878851796, 1e-09);
 assert (TAB{4,2}, -0.0429433065881864, 1e-09);
 assert (TAB{5,2}, -0.0115826516894871, 1e-09);
 assert (TAB{2,3}, 3.87851641748551, 1e-09);
 assert (TAB{3,3}, 0.00112741016370336, 1e-09);
 assert (TAB{4,3}, 0.0243130608813806, 1e-09);
 assert (TAB{5,3}, 0.193325043113178, 1e-09);
 assert (TAB{2,6}, 12.369874881944, 1e-09);
 assert (TAB{3,6}, -5.80228828790225, 1e-09);
 assert (TAB{4,6}, -1.76626492228599, 1e-09);
 assert (TAB{5,6}, -0.0599128364487485, 1e-09);
 assert (TAB{2,7}, 4.89570341688996e-21, 1e-09);
 assert (TAB{3,7}, 9.87424814144e-08, 1e-09);
 assert (TAB{4,7}, 0.0807803098213114, 1e-09);
 assert (TAB{5,7}, 0.952359384151778, 1e-09);
3 tests, 3 passed, 0 known failure, 0 skipped
[inst/trimmean.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/trimmean.m
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 assert (trimmean (x, 10, "all"), 19.4722, 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 out = trimmean (x, 10, [1, 2]);
 assert (out(1,1,1), 10.3889, 1e-4);
 assert (out(1,1,2), 29.6111, 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 assert (trimmean (x, 10, "all"), 19.3824, 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 out = trimmean (x, 10, 1);
 assert (out(:,:,1), [-17.6, 8, 13, 18]);
 assert (out(:,:,2), [23, 28, 33, 10.6]);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, 1);
 assert (out(:,:,1), [-23, 8, 13, 18]);
 assert (out(:,:,2), [23, 28, 33, 3.75]);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 out = trimmean (x, 10, 2);
 assert (out(:,:,1), [8.5; 9.5; -15.25; 11.5; 12.5]);
 assert (out(:,:,2), [28.5; -4.75; 30.5; 31.5; 32.5]);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, 2);
 assert (out(:,:,1), [8.5; 9.5; -15.25; 14; 12.5]);
 assert (out(:,:,2), [28.5; -4.75; 28; 31.5; 32.5]);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 out = trimmean (x, 10, [1, 2, 3]);
 assert (out, trimmean (x, 10, "all"));
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [1, 2]);
 assert (out(1,1,1), 10.7647, 1e-4);
 assert (out(1,1,2), 29.1176, 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [1, 3]);
 assert (out, [2.5556, 18, 23, 11.6667], 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [2, 3]);
 assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [1, 2, 3]);
 assert (out, trimmean (x, 10, "all"));
***** test
 x = reshape (1:40, [5, 4, 2]);
 x([3, 37]) = -100;
 x([4, 38]) = NaN;
 out = trimmean (x, 10, [2, 3, 5]);
 assert (out, [18.5; 2.3750; 3.2857; 24; 22.5], 1e-4);
***** assert (trimmean ([1, 2, 3, 4, 5], 40), 3)
***** assert (trimmean (reshape (1:40, [5, 4, 2]), 10, 4), reshape(1:40, [5, 4, 2]))
***** assert (trimmean ([], 10), NaN)
***** assert (trimmean ([1;2;3;4;5], 10, 2), [1;2;3;4;5])
***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1)
***** error<Invalid call to trimmean.  Correct usage is:> trimmean (1,2,3,4,5)
***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], -10)
***** error<trimmean: invalid percent.> trimmean ([1 2 3 4], 100)
***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag")
***** error<trimmean: invalid FLAG argument.> trimmean ([1 2 3 4], 10, "flag", 1)
***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
 trimmean ([1 2 3 4], 10, -1)
***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
 trimmean ([1 2 3 4], 10, "floor", -1)
***** error<trimmean: DIM must be a positive integer scalar or vector.> ...
 trimmean (reshape (1:40, [5, 4, 2]), 10, [-1, 2])
***** error<trimmean: VECDIM must contain non-repeating positive integers.> ...
 trimmean (reshape (1:40, [5, 4, 2]), 10, [1, 2, 2])
27 tests, 27 passed, 0 known failure, 0 skipped
[inst/regress.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/regress.m
***** test
 % Longley data from the NIST Statistical Reference Dataset
 Z = [  60323    83.0   234289   2356     1590    107608  1947
        61122    88.5   259426   2325     1456    108632  1948
        60171    88.2   258054   3682     1616    109773  1949
        61187    89.5   284599   3351     1650    110929  1950
        63221    96.2   328975   2099     3099    112075  1951
        63639    98.1   346999   1932     3594    113270  1952
        64989    99.0   365385   1870     3547    115094  1953
        63761   100.0   363112   3578     3350    116219  1954
        66019   101.2   397469   2904     3048    117388  1955
        67857   104.6   419180   2822     2857    118734  1956
        68169   108.4   442769   2936     2798    120445  1957
        66513   110.8   444546   4681     2637    121950  1958
        68655   112.6   482704   3813     2552    123366  1959
        69564   114.2   502601   3931     2514    125368  1960
        69331   115.7   518173   4806     2572    127852  1961
        70551   116.9   554894   4007     2827    130081  1962 ];
 % Results certified by NIST using 500 digit arithmetic
 % b and standard error in b
 V = [  -3482258.63459582         890420.383607373
         15.0618722713733         84.9149257747669
        -0.358191792925910E-01    0.334910077722432E-01
        -2.02022980381683         0.488399681651699
        -1.03322686717359         0.214274163161675
        -0.511041056535807E-01    0.226073200069370
         1829.15146461355         455.478499142212 ];
 Rsq = 0.995479004577296;
 F = 330.285339234588;
 y = Z(:,1); X = [ones(rows(Z),1), Z(:,2:end)];
 alpha = 0.05;
 [b, bint, r, rint, stats] = regress (y, X, alpha);
 assert(b,V(:,1),4e-6);
 assert(stats(1),Rsq,1e-12);
 assert(stats(2),F,3e-8);
 assert(((bint(:,1)-bint(:,2))/2)/tinv(alpha/2,9),V(:,2),-1.e-5);
warning: matrix singular to machine precision, rcond = 3.50566e-20
warning: called from
    regress at line 131 column 5
    __test__ at line 33 column 3
    test at line 685 column 11
    /tmp/tmp.2QPMk3f8PQ at line 3534 column 2

1 test, 1 passed, 0 known failure, 0 skipped
[inst/cophenet.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/cophenet.m
***** demo
 randn ("seed", 5)  # for reproducibility
 X = randn (10,2);
 y = pdist (X);
 Z = linkage (y, "average");
 cophenet (Z, y)
***** error<cophenet: function called with too few input arguments.> cophenet ()
***** error<cophenet: function called with too few input arguments.> cophenet (1)
***** error<cophenet: Z must be a matrix as generated by the linkage function.> ...
 cophenet (ones (2,2), 1)
***** error<cophenet: Y must be a vector of euclidean distances.> ...
 cophenet ([1 2 1], "a")
***** error<cophenet: Y must be a vector of euclidean distances.> ...
 cophenet ([1 2 1], [1 2])
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/signrank.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/signrank.m
***** test
 load gradespaired.mat
 [p, h, stats] = signrank (gradespaired(:,1), ...
                           gradespaired(:,2), 'tail', 'left');
 assert (p, 0.0047, 1e-4);
 assert (h, true);
 assert (stats.zval, -2.5982, 1e-4);
 assert (stats.signedrank, 2017.5);
***** test
 load ('gradespaired.mat');
 [p, h, stats] = signrank (gradespaired(:,1), gradespaired(:,2), ...
                           'tail', 'left', 'method', 'exact');
 assert (p, 0.0045, 1e-4);
 assert (h, true);
 assert (stats.zval, NaN);
 assert (stats.signedrank, 2017.5);
***** test
 load mileage
 [p, h, stats] = signrank (mileage(:,2), 33);
 assert (p, 0.0312, 1e-4);
 assert (h, true);
 assert (stats.zval, NaN);
 assert (stats.signedrank, 21);
***** test
 load mileage
 [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right');
 assert (p, 0.0156, 1e-4);
 assert (h, true);
 assert (stats.zval, NaN);
 assert (stats.signedrank, 21);
***** test
 load mileage
 [p, h, stats] = signrank (mileage(:,2), 33, 'tail', 'right', ...
                           'alpha', 0.01, 'method', 'approximate');
 assert (p, 0.0180, 1e-4);
 assert (h, false);
 assert (stats.zval, 2.0966, 1e-4);
 assert (stats.signedrank, 21);
***** error <signrank: X must be a vector.> signrank (ones (2))
***** error <signrank: Y must be either a scalar of a vector.> ...
 signrank ([1, 2, 3, 4], ones (2))
***** error <signrank: X and Y vectors have different lengths.> ...
 signrank ([1, 2, 3, 4], [1, 2, 3])
***** error <signrank: optional arguments must be in pairs.> ...
 signrank ([1, 2, 3, 4], [], 'tail')
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', 1.2)
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', 0)
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', -0.05)
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', "a")
***** error <signrank: 'alpha' must be a numeric scalar in the range 0 to 1.> ...
 signrank ([1, 2, 3, 4], [], 'alpha', [0.01, 0.05])
***** error <signrank: 'tail' argument must be a character vector.> ...
 signrank ([1, 2, 3, 4], [], 'tail', 0.01)
***** error <signrank: 'tail' argument must be a character vector.> ...
 signrank ([1, 2, 3, 4], [], 'tail', {"both"})
***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
 signrank ([1, 2, 3, 4], [], 'tail', "some")
***** error <signrank: 'tail' value must be either 'both', right' or 'left'.> ...
 signrank ([1, 2, 3, 4], [], 'method', 'exact', 'tail', "some")
***** error <signrank: 'method' argument must be a character vector.> ...
 signrank ([1, 2, 3, 4], [], 'method', 0.01)
***** error <signrank: 'method' argument must be a character vector.> ...
 signrank ([1, 2, 3, 4], [], 'method', {"exact"})
***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
 signrank ([1, 2, 3, 4], [], 'method', "some")
***** error <signrank: 'method' value must be either 'exact' or 'approximate'.> ...
 signrank ([1, 2, 3, 4], [], 'tail', "both", 'method', "some")
22 tests, 22 passed, 0 known failure, 0 skipped
[inst/cluster.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/cluster.m
***** error cluster ()
***** error <Z must be .* generated by the linkage .*> cluster ([1 1], "Cutoff", 1)
***** error <unknown option .*> cluster ([1 2 1], "Bogus", 1)
***** error <C must be a positive scalar .*> cluster ([1 2 1], "Cutoff", -1)
***** error <unknown property .*> cluster ([1 2 1], "Cutoff", 1, "Bogus", 1)
***** test
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/isoutlier.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/isoutlier.m
***** demo
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 TF = isoutlier (A, "mean")
***** demo
 ## Use a moving detection method to detect local outliers in a sine wave

 x = -2*pi:0.1:2*pi;
 A = sin(x);
 A(47) = 0;
 time = datenum (2023,1,1,0,0,0) + (1/24)*[0:length(x)-1] - 730485;
 TF = isoutlier (A, "movmedian", 5*(1/24), "SamplePoints", time);
 plot (time, A)
 hold on
 plot (time(TF), A(TF), "x")
 datetick ('x', 20, 'keepticks')
 legend ("Original Data", "Outlier Data")
***** demo
 ## Locate an outlier in a vector of data and visualize the outlier

 x = 1:10;
 A = [60 59 49 49 58 100 61 57 48 58];
 [TF, L, U, C] = isoutlier (A);
 plot (x, A);
 hold on
 plot (x(TF), A(TF), "x");
 xlim ([1,10]);
 line ([1,10], [L, L], "Linestyle", ":");
 text (1.1, L-2, "Lower Threshold");
 line ([1,10], [U, U], "Linestyle", ":");
 text (1.1, U-2, "Upper Threshold");
 line ([1,10], [C, C], "Linestyle", ":");
 text (1.1, C-3, "Center Value");
 legend ("Original Data", "Outlier Data");
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 assert (isoutlier (A, "mean"), logical([zeros(1,8) 1 zeros(1,6)]))
 assert (isoutlier (A, "median"), ...
 logical([zeros(1,3) 1 zeros(1,4) 1 zeros(1,6)]))
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "mean");
 assert (L, -109.2459044922864, 1e-12)
 assert (U, 264.9792378256198, 1e-12)
 assert (C, 77.8666666666666, 1e-12)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "median");
 assert (L, 50.104386688966386, 1e-12)
 assert (U, 67.895613311033610, 1e-12)
 assert (C, 59)
***** test
 A = magic(5) + diag(200*ones(1,5));
 T = logical (eye (5));
 assert (isoutlier (A, 2), T)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "movmedian", 5);
 l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
      53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
 u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
      62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
 c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
 assert (L, l, 1e-4)
 assert (U, u, 1e-4)
 assert (C, c)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "movmedian", 5, "SamplePoints", [1:15]);
 l = [54.5522, 52.8283, 54.5522, 54.5522, 54.5522, 53.5522, 53.5522, ...
      53.5522, 47.6566, 56.5522, 57.5522, 56.5522, 51.1044, 52.3283, 53.5522];
 u = [63.4478, 66.1717, 63.4478, 63.4478, 63.4478, 62.4478, 62.4478, ...
      62.4478, 74.3434, 65.4478, 66.4478, 65.4478, 68.8956, 65.6717, 62.4478];
 c = [59, 59.5, 59, 59, 59, 58, 58, 58, 61, 61, 62, 61, 60, 59, 58];
 assert (L, l, 1e-4)
 assert (U, u, 1e-4)
 assert (C, c)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "movmean", 5);
 l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
      -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
      52.5979, 51.0627];
 u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
      430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
      66.9373];
 c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
      60.6, 59.8, 59.25, 59];
 assert (L, l, 1e-4)
 assert (U, u, 1e-4)
 assert (C, c, 1e-4)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "movmean", 5, "SamplePoints", [1:15]);
 l = [54.0841,  6.8872, 11.5608, 12.1518, 11.0210, 10.0112, -218.2840, ...
      -217.2375, -215.1239, -213.4890, -211.3264, 55.5800, 52.9589, ...
      52.5979, 51.0627];
 u = [63.2492, 131.1128, 122.4392, 122.2482, 122.5790, 122.7888, 431.0840, ...
      430.8375, 430.3239, 429.8890, 429.3264, 65.6200, 66.6411, 65.9021, ...
      66.9373];
 c = [58.6667, 69, 67, 67.2, 66.8, 66.4, 106.4, 106.8, 107.6, 108.2, 109, ...
      60.6, 59.8, 59.25, 59];
 assert (L, l, 1e-4)
 assert (U, u, 1e-4)
 assert (C, c, 1e-4)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "gesd");
 assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 34.235977035439944, 1e-12)
 assert (U, 89.764022964560060, 1e-12)
 assert (C, 62)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 0.01);
 assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 31.489256770616173, 1e-12)
 assert (U, 92.510743229383820, 1e-12)
 assert (C, 62)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "gesd", "ThresholdFactor", 5e-10);
 assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 23.976664158788935, 1e-12)
 assert (U, 100.02333584121110, 1e-12)
 assert (C, 62)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "grubbs");
 assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 54.642809574646606, 1e-12)
 assert (U, 63.511036579199555, 1e-12)
 assert (C, 59.076923076923080, 1e-12)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A, "grubbs", "ThresholdFactor", 0.01);
 assert (TF, logical ([0 0 0 1 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 54.216083184201850, 1e-12)
 assert (U, 63.937762969644310, 1e-12)
 assert (C, 59.076923076923080, 1e-12)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A,  "percentiles", [10 90]);
 assert (TF, logical ([0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]))
 assert (L, 57)
 assert (U, 100)
 assert (C, 78.5)
***** test
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
 [TF, L, U, C] = isoutlier (A,  "percentiles", [20 80]);
 assert (TF, logical ([1 0 0 1 0 0 1 0 1 0 0 0 0 0 1]))
 assert (L, 57.5)
 assert (U, 62)
 assert (C, 59.75)
***** shared A
 A = [57 59 60 100 59 58 57 58 300 61 62 60 62 58 57];
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", 0);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", []);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", [2 3 4]);
***** error<isoutlier: WINDOW must be a positive integer> ...
 isoutlier (A, "movmedian", 1.4);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", [0 1]);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", [2 -1]);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", {2 3});
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmedian", "char");

***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", 0);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", []);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", [2 3 4]);
***** error<isoutlier: WINDOW must be a positive integer> ...
 isoutlier (A, "movmean", 1.4);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", [0 1]);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", [2 -1]);
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", {2 3});
***** error<isoutlier: WINDOW must be a positive scalar> ...
 isoutlier (A, "movmean", "char");

***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [-1 90]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [10 -90]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [90]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [90 20]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [90 20]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", [10 20 90]);
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", {10 90});
***** error<isoutlier: THRESHOLD must be a two-element> ...
 isoutlier (A, "percentiles", "char");

***** error<isoutlier: sample points must be a vector.> ...
 isoutlier (A, "movmean", 5, "SamplePoints", ones(3,15));
***** error<isoutlier: sample points must be a vector.> ...
 isoutlier (A, "movmean", 5, "SamplePoints", 15);
***** error<isoutlier: sample points must be unique.> ...
 isoutlier (A, "movmean", 5, "SamplePoints", [1,1:14]);
***** error<isoutlier: sample points must be sorted.> ...
 isoutlier (A, "movmean", 5, "SamplePoints", [2,1,3:15]);
***** error<isoutlier: sample points must have the same size> ...
 isoutlier (A, "movmean", 5, "SamplePoints", [1:14]);

***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
 isoutlier (A, "movmean", 5, "ThresholdFactor", [1:14]);
***** error<isoutlier: threshold factor must be a nonnegative scalar.> ...
 isoutlier (A, "movmean", 5, "ThresholdFactor", -1);
***** error<isoutlier: threshold factor must must be in> ...
 isoutlier (A, "gesd", "ThresholdFactor", 3);
***** error<isoutlier: threshold factor must must be in> ...
 isoutlier (A, "grubbs", "ThresholdFactor", 3);

***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
 isoutlier (A, "movmean", 5, "MaxNumOutliers", [1:14]);
***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
 isoutlier (A, "movmean", 5, "MaxNumOutliers", -1);
***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
 isoutlier (A, "movmean", 5, "MaxNumOutliers", 0);
***** error<isoutlier: maximum outlier count must be a positive integer scalar.> ...
 isoutlier (A, "movmean", 5, "MaxNumOutliers", 1.5);

***** error<isoutlier: invalid input argument.> ...
 isoutlier (A, {"movmean"}, 5, "SamplePoints", [1:15]);
***** error<isoutlier: invalid input argument.> isoutlier (A, {1});
***** error<isoutlier: invalid input argument.> isoutlier (A, true);
***** error<isoutlier: invalid input argument.> isoutlier (A, false);
***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, 0);
***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, [1 2]);
***** error<isoutlier: DIM must be a positive integer scalar.> isoutlier (A, -2);
59 tests, 59 passed, 0 known failure, 0 skipped
[inst/nansum.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/nansum.m
***** demo
 ## Find the column sums for a matrix with missing values.,

 x = magic (3);
 x([1, 4, 7:9]) = NaN
 s = nansum (x)
***** demo
 ## Find the row sums for a matrix with missing values.,

 x = magic (3);
 x([1, 4, 7:9]) = NaN
 s = nansum (x, 2)
***** demo
 ## Find the sum of all the values in a multidimensional array
 ## with missing values.

 x = reshape (1:30, [2, 5, 3]);
 x([10:12, 25]) = NaN
 s = nansum (x, "all")
***** demo
 ## Find the sum of a multidimensional array with missing values over
 ## multiple dimensions.

 x = reshape (1:30, [2, 5, 3]);
 x([10:12, 25]) = NaN
 s = nansum (x, [2, 3])
***** assert (nansum ([]), 0)
***** assert (nansum (NaN), 0)
***** assert (nansum (NaN(3)), [0, 0, 0])
***** assert (nansum ([2 4 NaN 7]), 13)
***** assert (nansum ([2 4 NaN Inf]), Inf)
***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN]), [8 13 9])
***** assert (nansum ([1 NaN 3; NaN 5 6; 7 8 NaN], 2), [4; 11; 15])
***** assert (nansum (uint8 ([2 4 1 7])), 14)
***** test
 x = magic(3);
 x([1 6:9]) = NaN;
 assert (nansum (x), [7, 6, 0])
 assert (nansum (x, 2), [1; 8; 4])
***** test
 x = reshape(1:24, [2, 4, 3]);
 x([5:6, 20]) = NaN;
 assert (nansum (x, "all"), 269)
***** test
 x = reshape(1:24,[2, 4, 3]);
 x([5:6, 20]) = NaN;
 assert (squeeze (nansum (x, [1, 2])), [25; 100; 144])
 assert (nansum (x, [2, 3]), [139; 130])
***** error <nansum: X must be numeric.> nansum ({3})
***** error <nansum: DIM must be a positive integer.> nansum (ones (3), 0)
***** error <nansum: DIM must be a positive integer.> nansum (ones (3), 1.5)
***** error <nansum: DIM must be a positive integer.> nansum (ones (3), 1.5)
***** error <nansum: VECDIM must be a vector of positive integer.> ...
 nansum (ones (3, 3, 3), [2, 2.5])
***** error <nansum: VECDIM must be a vector of positive integer.> ...
 nansum (ones (3, 3, 3), [-1, 2])
***** error <nansum: VECDIM must contain non-repeating positive integers.> ...
 nansum (ones (3, 3, 3), [2, 2, 3])
18 tests, 18 passed, 0 known failure, 0 skipped
[inst/normplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/normplot.m
***** demo
 h = normplot([1:20]);
***** demo
 h = normplot([1:20;5:2:44]');
***** demo
 ax = newplot();
 h = normplot(ax, [1:20]);
 ax = gca;
 h = normplot(ax, [-10:10]);
 set (ax, "xlim", [-11, 21]);
***** error normplot ();
***** error normplot (23);
***** error normplot (23, [1:20]);
***** error normplot (ones(3,4,5));
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ax = newplot (hf);
   h = normplot (ax, [1:20]);
   ax = gca;
   h = normplot(ax, [-10:10]);
   set (ax, "xlim", [-11, 21]);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** test
 hf = figure ("visible", "off");
 unwind_protect
   h = normplot([1:20;5:2:44]');
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
6 tests, 6 passed, 0 known failure, 0 skipped
[inst/pcacov.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/pcacov.m
***** demo
 x = [ 7    26     6    60;
       1    29    15    52;
      11    56     8    20;
      11    31     8    47;
       7    52     6    33;
      11    55     9    22;
       3    71    17     6;
       1    31    22    44;
       2    54    18    22;
      21    47     4    26;
       1    40    23    34;
      11    66     9    12;
      10    68     8    12
     ];
 Kxx = cov (x);
 [coeff, latent, explained] = pcacov (Kxx)
***** test
 load hald
 Kxx = cov (ingredients);
 [coeff,latent,explained] = pcacov(Kxx);
 c_out = [-0.0678, -0.6460,  0.5673, 0.5062; ...
          -0.6785, -0.0200, -0.5440, 0.4933; ...
           0.0290,  0.7553,  0.4036, 0.5156; ...
           0.7309, -0.1085, -0.4684, 0.4844];
 l_out = [517.7969; 67.4964; 12.4054; 0.2372];
 e_out = [ 86.5974; 11.2882;  2.0747; 0.0397];
 assert (coeff, c_out, 1e-4);
 assert (latent, l_out, 1e-4);
 assert (explained, e_out, 1e-4);
***** error <pcacov: K must be a square matrix.> pcacov (ones (2, 3))
***** error <pcacov: K must be a square matrix.> pcacov (ones (3, 3, 3))
***** error <pcacov: K must be a symmetric matrix.> pcacov ([1, 2; 0, 1])
***** error <pcacov: K must be a positive semi-definite matrix.> pcacov ([10, 0; 0, -1])
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/chi2test.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/chi2test.m
***** error chi2test ();
***** error chi2test ([1, 2, 3, 4, 5]);
***** error chi2test ([1, 2; 2, 1+3i]);
***** error chi2test ([NaN, 6; 34, 12]);
***** error<chi2test: optional arguments are not supported for 2-way> ...
 p = chi2test (ones (3, 3), "mutual", []);
***** error<chi2test: invalid model name for testing a 3-way table.> ...
 p = chi2test (ones (3, 3, 3), "testtype", 2);
***** error<chi2test: optional arguments must be in pairs.> ...
 p = chi2test (ones (3, 3, 3), "mutual");
***** error<chi2test: value must be numeric in optional argument> ...
 p = chi2test (ones (3, 3, 3), "joint", ["a"]);
***** error<chi2test: value must be empty or scalar in optional argument> ...
 p = chi2test (ones (3, 3, 3), "joint", [2, 3]);
***** error<chi2test: optional arguments are not supported for k> ...
 p = chi2test (ones (3, 3, 3, 4), "mutual", [])
***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (2));
***** warning<chi2test: Expected values less than 5.> p = chi2test (ones (3, 2));
***** warning<chi2test: Expected values less than 1.> p = chi2test (0.4 * ones (3));
***** test
 x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
 p = chi2test (x);
 assert (p, 0.017787, 1e-6);
***** test
 x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
 [p, chisq] = chi2test (x);
 assert (chisq, 11.9421, 1e-4);
***** test
 x = [11, 3, 8; 2, 9, 14; 12, 13, 28];
 [p, chisq, df] = chi2test (x);
 assert (df, 4);
***** test
***** shared x
 x(:,:,1) = [59, 32; 9,16];
 x(:,:,2) = [55, 24;12,33];
 x(:,:,3) = [107,80;17,56];%!
***** assert (chi2test (x), 2.282063427117009e-11, 1e-14);
***** assert (chi2test (x, "mutual", []), 2.282063427117009e-11, 1e-14);
***** assert (chi2test (x, "joint", 1), 1.164834895206468e-11, 1e-14);
***** assert (chi2test (x, "joint", 2), 7.771350230001417e-11, 1e-14);
***** assert (chi2test (x, "joint", 3), 0.07151361728026107, 1e-14);
***** assert (chi2test (x, "marginal", 1), 0, 1e-14);
***** assert (chi2test (x, "marginal", 2), 6.347555814301131e-11, 1e-14);
***** assert (chi2test (x, "marginal", 3), 0, 1e-14);
***** assert (chi2test (x, "conditional", 1), 0.2303114201312508, 1e-14);
***** assert (chi2test (x, "conditional", 2), 0.0958810684407079, 1e-14);
***** assert (chi2test (x, "conditional", 3), 2.648037344954446e-11, 1e-14);
***** assert (chi2test (x, "homogeneous", []), 0.4485579470993741, 1e-14);
***** test
 [pval, chisq, df, E] = chi2test (x);
 assert (chisq, 64.0982, 1e-4);
 assert (df, 7);
 assert (E(:,:,1), [42.903, 39.921; 17.185, 15.991], ones (2, 2) * 1e-3);
***** test
 [pval, chisq, df, E] = chi2test (x, "joint", 2);
 assert (chisq, 56.0943, 1e-4);
 assert (df, 5);
 assert (E(:,:,2), [40.922, 23.310; 38.078, 21.690], ones (2, 2) * 1e-3);
***** test
 [pval, chisq, df, E] = chi2test (x, "marginal", 3);
 assert (chisq, 146.6058, 1e-4);
 assert (df, 9);
 assert (E(:,1,1), [61.642; 57.358], ones (2, 1) * 1e-3);
***** test
 [pval, chisq, df, E] = chi2test (x, "conditional", 3);
 assert (chisq, 52.2509, 1e-4);
 assert (df, 3);
 assert (E(:,:,1), [53.345, 37.655; 14.655, 10.345], ones (2, 2) * 1e-3);
***** test
 [pval, chisq, df, E] = chi2test (x, "homogeneous", []);
 assert (chisq, 1.6034, 1e-4);
 assert (df, 2);
 assert (E(:,:,1), [60.827, 31.382; 7.173, 16.618], ones (2, 2) * 1e-3);
34 tests, 34 passed, 0 known failure, 0 skipped
[inst/ppplot.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/ppplot.m
***** test
 hf = figure ("visible", "off");
 unwind_protect
   ppplot ([2 3 3 4 4 5 6 5 6 7 8 9 8 7 8 9 0 8 7 6 5 4 6 13 8 15 9 9]);
 unwind_protect_cleanup
   close (hf);
 end_unwind_protect
***** error ppplot ()
***** error <ppplot: X must be a numeric vector> ppplot (ones (2,2))
***** error <ppplot: X must be a numeric vector> ppplot (1, 2)
***** error <ppplot: DIST must be a string> ppplot ([1 2 3 4], 2)
5 tests, 5 passed, 0 known failure, 0 skipped
[inst/sigma_pts.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/sigma_pts.m
***** demo
 K      = [1 0.5; 0.5 1]; # covariance matrix
 # calculate and build associated ellipse
 [R,S,~] = svd (K);
 theta   = atan2 (R(2,1), R(1,1));
 v       = sqrt (diag (S));
 v       = v .* [cos(theta) sin(theta); -sin(theta) cos(theta)];
 t       = linspace (0, 2*pi, 100).';
 xe      = v(1,1) * cos (t) + v(2,1) * sin (t);
 ye      = v(1,2) * cos (t) + v(2,2) * sin (t);

 figure(1); clf; hold on
 # Plot ellipse and axes
 line ([0 0; v(:,1).'],[0 0; v(:,2).'])
 plot (xe,ye,'-r');

 col = 'rgb';
 l     = [-1.8 -1 1.5];
 for li = 1:3
  p     = sigma_pts (2, [], K, l(li));
  tmp   = plot (p(2:end,1), p(2:end,2), ['x' col(li)], ...
               p(1,1), p(1,2), ['o' col(li)]);
  h(li) = tmp(1);
 endfor
 hold off
 axis image
 legend (h, arrayfun (@(x) sprintf ("l:%.2g", x), l, "unif", 0));
***** test
 p = sigma_pts (5);
 assert (mean (p), zeros(1,5), sqrt(eps));
 assert (cov (p), eye(5), sqrt(eps));
***** test
 m = randn(1, 5);
 p = sigma_pts (5, m);
 assert (mean (p), m, sqrt(eps));
 assert (cov (p), eye(5), sqrt(eps));
***** test
 x = linspace (0,1,5);
 K = exp (- (x.' - x).^2/ 0.5);
 p = sigma_pts (5, [], K);
 assert (mean (p), zeros(1,5), sqrt(eps));
 assert (cov (p), K, sqrt(eps));
***** error sigma_pts(2,1);
***** error sigma_pts(2,[],1);
***** error sigma_pts(2,1,1);
***** error sigma_pts(2,[0.5 0.5],[-1 0; 0 0]);
7 tests, 7 passed, 0 known failure, 0 skipped
[inst/monotone_smooth.m]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/inst/monotone_smooth.m
***** error<Invalid call to monotone_smooth.  Correct usage is> ...
 monotone_smooth (1)
***** error<monotone_smooth: X must be a numeric vector.> ...
 monotone_smooth ("char", 1)
***** error<monotone_smooth: X must be a numeric vector.> ...
 monotone_smooth ({1,2,3}, 1)
***** error<monotone_smooth: X must be a numeric vector.> ...
 monotone_smooth (ones(20,3), 1)
***** error<monotone_smooth: Y must be a numeric vector.> ...
 monotone_smooth (1, "char")
***** error<monotone_smooth: Y must be a numeric vector.> ...
 monotone_smooth (1, {1,2,3})
***** error<monotone_smooth: Y must be a numeric vector.> ...
 monotone_smooth (1, ones(20,3))
***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), [1, 2])
***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), {2})
***** error<monotone_smooth: H>  monotone_smooth (ones (10,1), ones(10,1), "char")
10 tests, 10 passed, 0 known failure, 0 skipped
Checking C++ files ...
[src/editDistance.cc]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/src/editDistance.cc
***** error <editDistance: too many input arguments.> d = editDistance (1, 2, 3, 4);
***** error <editDistance: too many output arguments.> ...
 [C, IA, IC, I] = editDistance ({"AS","SD","AD"}, 1);
***** error <editDistance: too many output arguments.> ...
 [C, IA] = editDistance ({"AS","SD","AD"});
***** error <editDistance: minDist must be a scalar value.> ...
 d = editDistance ({"AS","SD","AD"}, [1, 2]);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ({"AS","SD","AD"}, -2);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ({"AS","SD","AD"}, 1.25);
***** error <editDistance: minDist must be a scalar value.> ...
 d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, [1, 2]);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, -2);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ({"AS","SD","AD"}, {"AS","SD","AD"}, 1.25);
***** error <editDistance: minDist must be a scalar value.> ...
 d = editDistance ("string1", "string2", [1, 2]);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ("string1", "string2", -2);
***** error <editDistance: minDist must be a nonnegative integer.> ...
 d = editDistance ("string1", "string2", 1.25);
***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
 d = editDistance ({{"string1", "string2"}, 2});
***** error <editDistance: tokenizedDocument must contain cellstr arrays.> ...
 d = editDistance ({{"string1", "string2"}, 2}, 2);
***** error <editDistance: STR1 must be a cellstr.> ...
 d = editDistance ([1, 2, 3]);
***** error <editDistance: STR1 must be a cellstr.> ...
 d = editDistance (["AS","SD","AD","AS"]);
***** error <editDistance: STR1 must be a cellstr.> ...
 d = editDistance (["AS","SD","AD"], 2);
***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
 d = editDistance (logical ([1,2,3]), {"AS","AS","AD"});
***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
 d = editDistance ({"AS","SD","AD"}, logical ([1,2,3]));
***** error <editDistance: STR1 and STR2 must be either strings or cellstr.> ...
 d = editDistance ([1,2,3], {"AS","AS","AD"});
***** error <editDistance: first tokenizedDocument does not contain cellstr arrays.> ...
 d = editDistance ({1,2,3}, {"AS","SD","AD"});
***** error <editDistance: second tokenizedDocument does not contain cellstr arrays.> ...
 d = editDistance ({"AS","SD","AD"}, {1,2,3});
***** error <editDistance: cellstr input arguments size mismatch.> ...
 d = editDistance ({"AS","SD","AD"}, {"AS", "AS"});
***** test
 d = editDistance ({"AS","SD","AD"});
 assert (d, [2; 1; 1]);
 assert (class (d), "double");
***** test
 C = editDistance ({"AS","SD","AD"}, 1);
 assert (iscellstr (C), true);
 assert (C, {"AS";"SD"});
***** test
 [C, IA] = editDistance ({"AS","SD","AD"}, 1);
 assert (class (IA), "double");
 assert (IA, [1;2]);
***** test
 A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
 [C, IA] = editDistance (A, 2, "OutputAllIndices", false);
 assert (class (IA), "double");
 assert (A(IA), C);
***** test
 A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
 [C, IA] = editDistance (A, 2, "OutputAllIndices", true);
 assert (class (IA), "cell");
 assert (C, {"ASS"; "FDE"; "OPA"});
 assert (A(IA{1}), {"ASS"; "SDS"; "EDS"});
 assert (A(IA{2}), {"FDE"; "EDS"});
 assert (A(IA{3}), {"OPA"});
***** test
 A = {"ASS"; "SDS"; "FDE"; "EDS"; "OPA"};
 [C, IA, IC] = editDistance (A, 2);
 assert (class (IA), "double");
 assert (A(IA), C);
 assert (IC, [1; 1; 3; 1; 5]);
***** test
 d = editDistance ({"AS","SD","AD"}, {"AS", "AD", "SE"});
 assert (d, [0; 1; 2]);
 assert (class (d), "double");
***** test
 d = editDistance ({"AS","SD","AD"}, {"AS"});
 assert (d, [0; 2; 1]);
 assert (class (d), "double");
***** test
 d = editDistance ({"AS"}, {"AS","SD","AD"});
 assert (d, [0; 2; 1]);
 assert (class (d), "double");
***** test
 b = editDistance ("Octave", "octave");
 assert (b, 1);
 assert (class (b), "double");
33 tests, 33 passed, 0 known failure, 0 skipped
[src/svmtrain.cc]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/src/svmtrain.cc
***** test
 # Test 1: Basic C-SVC Classification and Model Structure
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 model = svmtrain(L, D, '-c 1 -g 0.07');
 [predict_label, accuracy, dec_values] = svmpredict(L, D, model);

 assert (isstruct (model), true);
 assert (isfield (model, "Parameters"), true);
 assert (model.totalSV, 130);
 assert (model.nr_class, 2);
 assert (size (model.Label), [2, 1]);

 # Check prediction output sizes
 assert (size (predict_label), [length(L), 1]);
 assert (size (dec_values), [length(L), 1]);


 # Test 2: One-Class SVM Model Structure Check
 # Ensures training with -s 2 is functional and the model structure is valid (accommodating 3.36 changes).
 model_oc = svmtrain(L, D, '-s 2 -n 0.5 -g 0.07');
 assert (isstruct (model_oc), true);
 assert (model_oc.Parameters(1), 2); # Check svm_type is ONE_CLASS
 assert (model_oc.nr_class, 2);
 assert (model_oc.totalSV > 0, true);
 clear model_oc


 # Test 3: Regression SVR Test
 # Check training of Epsilon SVR (-s 3)
 model_svr = svmtrain (L, D, '-s 3 -p 0.1 -c 10');
 assert (isstruct (model_svr), true);
 assert (model_svr.Parameters(1), 3); # Check svm_type is EPSILON_SVR
 assert (model_svr.nr_class, 2);
 clear model_svr


 # Test 4: Input Argument Error Checking
***** shared L, D
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));

 # Check argument count errors
***** error <svmtrain: wrong number of output arguments.> [L, D] = svmtrain (L, D);
***** error <svmtrain: wrong number of input arguments.> model = svmtrain (L, D, "", "");

 # Check argument type errors
***** error <svmtrain: label vector and instance matrix must be double.> ...
 model = svmtrain (single (L), D);

 # Check dimension mismatch error
***** error <svmtrain: label vector must have same number of elements as rows in instance matrix.> ...
 model = svmtrain (L(1:end-1), D);

 # Test 5: One-Class Probability Training (New LIBSVM 3.36 Feature)
 # This ensures svmtrain DOES NOT reject -s 2 combined with -b 1
 # and correctly populates the new ProbDensityMarks field.
***** test
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 model = svmtrain (L, D, '-s 2 -n 0.1 -g 0.07 -b 1');

 assert (isstruct (model), true);
 assert (model.Parameters(1), 2); # Check svm_type is ONE_CLASS
 # CRITICAL CHECK: Verify the new field exists (Specific to upgrade)
 assert (isfield (model, "ProbDensityMarks"), true);
 clear model
6 tests, 6 passed, 0 known failure, 0 skipped
[src/libsvmwrite.cc]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/src/libsvmwrite.cc
***** shared L, D
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
***** error <libsvmwrite: error opening file for write.> libsvmwrite ("", L, D);
***** error <libsvmwrite: length of label vector does not match instances.> ...
 libsvmwrite (tempname (), [L;L], D);
***** error <libsvmwrite: wrong number of output arguments.> ...
 OUT = libsvmwrite (tempname (), L, D);
***** error <libsvmwrite: label vector and instance matrix must be double.> ...
 libsvmwrite (tempname (), single (L), D);
***** error <libsvmwrite: filename must be a string.> libsvmwrite (13412, L, D);
***** error <libsvmwrite: instance_matrix must be sparse.> ...
 libsvmwrite (tempname (), L, full (D));
***** error <libsvmwrite: wrong number of input arguments.> ...
 libsvmwrite (tempname (), L, D, D);
7 tests, 7 passed, 0 known failure, 0 skipped
[src/svmpredict.cc]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/src/svmpredict.cc
***** test
 # Test 1: Standard C-SVC Prediction (Original Regression Test)
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 model = svmtrain (L, D, '-c 1 -g 0.07');
 [predict_label, accuracy, dec_values] = svmpredict (L, D, model);
 assert (size (predict_label), size (dec_values));
 assert (accuracy, [86.666, 0.533, 0.533]', [1e-3, 1e-3, 1e-3]');
 assert (dec_values(1), 1.225836001973273, 1e-14);
 assert (dec_values(2), -0.3212992933043805, 1e-14);
 assert (predict_label(1), 1);

***** test
 # Test 2: One-Class Probability (NEW LIBSVM 3.36 FEATURE)
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 # Train One-Class (-s 2) with Probability (-b 1)
 model_oc = svmtrain (L, D, '-s 2 -n 0.1 -g 0.07 -b 1');
 # FIX: Changed // to # below to fix syntax error
 assert (isstruct(model_oc), true, "svmtrain failed to return a valid struct model."); # <-- FIXED COMMENT HERE
 # Predict with Probability (-b 1)
 [pred, acc, probs] = svmpredict (L, D, model_oc, '-b 1');

 # Detail Check A: Output must be N x 2 (Column 1: Normal, Column 2: Outlier)
 assert (size (probs), [length(L), 2]);

 # Detail Check B: Probabilities must sum to 1.0 for every instance
 assert (sum (probs, 2), ones (length(L), 1), 1e-5);

 # Detail Check C: Values must be valid probabilities [0, 1]
 assert (all (all (probs >= 0 & probs <= 1)));
 clear model_oc

***** test
 # Test 3: One-Class Decision Values (Standard Check)
 # Verifies that the upgrade didn't break standard One-Class prediction (-b 0)
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 model_oc = svmtrain (L, D, '-s 2 -n 0.1 -g 0.07');
 [pred, acc, dec] = svmpredict (L, D, model_oc);
 # Standard One-Class output is N x 1 (Scalar decision values)
 assert (size (dec), [length(L), 1]);
 clear model_oc

***** shared L, D, model
 # Test 4: Error Handling (Original Checks)
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 model = svmtrain (L, D, '-c 1 -g 0.07');

***** error <svmpredict: wrong number of output arguments.> ...
 [p, a] = svmpredict (L, D, model);
***** error <svmpredict: wrong number of input arguments.> p = svmpredict (L, D);
***** error <svmpredict: label vector and instance matrix must be double.> ...
 p = svmpredict (single (L), D, model);
***** error <svmpredict: model should be a struct array.> p = svmpredict (L, D, 123);
7 tests, 7 passed, 0 known failure, 0 skipped
[src/fcnntrain.cc]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/src/fcnntrain.cc
***** shared X, Y, MODEL
 load fisheriris
 X = meas;
 Y = grp2idx (species);
***** error <fcnntrain: too few input arguments.> ...
 model = fcnntrain (X, Y);
***** error <fcnntrain: too many output arguments.> ...
 [Q, W] = fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: X must be a real numeric matrix.> ...
 fcnntrain (complex (X), Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: X must be a real numeric matrix.> ...
 fcnntrain ({X}, Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: X cannot be empty.> ...
 fcnntrain ([], Y, 10, [1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: Y must be a real numeric matrix.> ...
 fcnntrain (X, complex (Y), 10, 1, 0.01, [1, 1], 0.025, 50, false);
***** error <fcnntrain: Y must be a real numeric matrix.> ...
 fcnntrain (X, {Y}, 10, [1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: Y cannot be empty.> ...
 fcnntrain (X, [], 10, [1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: X and Y must have the same number of rows.> ...
 fcnntrain (X, Y([1:50]), 10, [1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: labels in Y must be positive integers.> ...
 fcnntrain (X, Y - 1, 10, [1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
 fcnntrain (X, Y, [10; 5], [1, 1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
 fcnntrain (X, Y, "10", [1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
 fcnntrain (X, Y, {10}, [1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: 'LayerSizes' must be a row vector of integer values.> ...
 fcnntrain (X, Y, complex (10), [1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
 fcnntrain (X, Y, 10, [1; 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
 fcnntrain (X, Y, 10, {1, 1}, 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
 fcnntrain (X, Y, 10, "1", 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: 'Activations' must be a row vector of integer values.> ...
 fcnntrain (X, Y, 10, complex ([1, 1]), 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: 'Activations' do not match LayerSizes.> ...
 fcnntrain (X, Y, 10, [1, 1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: cannot have a layer of zero size.> ...
 fcnntrain (X, Y, [10, 0, 5], [1, 1, 1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: invalid 'Activations' code.> ...
 fcnntrain (X, Y, 10, [-1, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: invalid 'Activations' code.> ...
 fcnntrain (X, Y, 10, [8, 1], 1, 0.01, 0.025, 50, false);
***** error <fcnntrain: 'NumThreads' must be a positive integer scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], 0, 0.01, 0.025, 50, false);
***** error <fcnntrain: 'Alpha' must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], 1, -0.01, 0.025, 50, false);
***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], 1, 0.01, -0.025, 50, false);
***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0, 50, false);
***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], 1, 0.01, [0.025, 0.001], 50, false);
***** error <fcnntrain: 'LearningRate' must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], 1, 0.01, {0.025}, 50, false);
***** error <fcnntrain: 'Epochs' must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 0, false);
***** error <fcnntrain: 'Epochs' must be a positive scalar value.> ...
 fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, [50, 25], false);
***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
 fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, 0);
***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
 fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, 1);
***** error <fcnntrain: 'DisplayInfo' must be a boolean scalar.> ...
 fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 50, [false, false]);
33 tests, 33 passed, 0 known failure, 0 skipped
[src/fcnnpredict.cc]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/src/fcnnpredict.cc
***** shared X, Y, MODEL
 load fisheriris
 X = meas;
 Y = grp2idx (species);
 MODEL = fcnntrain (X, Y, 10, [1, 1], 1, 0.01, 0.025, 100, false);
***** test
 [Y_pred, Y_scores] = fcnnpredict (MODEL, X);
 assert (numel (Y_pred), numel (Y));
 assert (isequal (size (Y_pred), size (Y)), true);
 assert (columns (Y_scores), numel (unique (Y)));
 assert (rows (Y_scores), numel (Y));
***** error <fcnnpredict: too few input arguments.> ...
 fcnnpredict (MODEL);
***** error <fcnnpredict: too many output arguments.> ...
 [Q, W, E] = fcnnpredict (MODEL, X);
***** error <fcnnpredict: first argument must be a scalar structure.> ...
 fcnnpredict (1, X);
***** error <fcnnpredict: first argument must be a scalar structure.> ...
 fcnnpredict (struct ("L", {1, 2, 3}), X);
***** error <fcnnpredict: model does not have a 'LayerWeights' field.> ...
 fcnnpredict (struct ("L", 1), X);
***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
 fcnnpredict (struct ("LayerWeights", 1), X);
***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
 fcnnpredict (struct ("LayerWeights", {1}), X);
***** error <fcnnpredict: 'LayerWeights' must be a cell row vector.> ...
 fcnnpredict (struct ("LayerWeights", {{1; 2; 3}}), X);
***** error <fcnnpredict: model does not have an 'Activations' field.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, "R", 2), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", [2]), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", [2; 2]), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", {{2, 2}}), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", {{"sigmoid", "softmax"}}), X);
***** error <fcnnpredict: 'Activations' must be a numeric row vector.> ...
 fcnnpredict (struct ("LayerWeights", {[{ones(3)},{ones(3)}]}, ...
                      "Activations", "sigmoid"), X);
***** error <fcnnpredict: XC must be a real numeric matrix.> ...
 fcnnpredict (MODEL, complex (X));
***** error <fcnnpredict: XC must be a real numeric matrix.> ...
 fcnnpredict (MODEL, {1, 2, 3, 4});
***** error <fcnnpredict: XC must be a real numeric matrix.> ...
 fcnnpredict (MODEL, "asd");
***** error <fcnnpredict: XC cannot be empty.> ...
 fcnnpredict (MODEL, []);
***** error <fcnnpredict: the features in XC do not match the trained model.> ...
 fcnnpredict (MODEL, X(:,[1:3]));
20 tests, 20 passed, 0 known failure, 0 skipped
[src/libsvmread.cc]
>>>>> /build/reproducible-path/octave-statistics-1.8.1/src/libsvmread.cc
***** error <libsvmread: filename must be a string.> [L, D] = libsvmread (24);
***** error <libsvmread: wrong number of input or output arguments.> ...
 D = libsvmread ("filename");
***** test
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 assert (size (L), [270, 1]);
 assert (size (D), [270, 13]);
***** test
 [L, D] = libsvmread (file_in_loadpath ("heart_scale.dat"));
 assert (issparse (L), false);
 assert (issparse (D), true);
4 tests, 4 passed, 0 known failure, 0 skipped
Done running the unit tests.
Summary: 11500 tests, 11500 passed, 0 known failures, 0 skipped
   dh_install -a -O--buildsystem=octave
   dh_installdocs -a -O--buildsystem=octave
   dh_installchangelogs -a -O--buildsystem=octave
   dh_octave_changelogs -a -O--buildsystem=octave
   dh_octave_examples -a -O--buildsystem=octave
   dh_installsystemduser -a -O--buildsystem=octave
   dh_perl -a -O--buildsystem=octave
   dh_link -a -O--buildsystem=octave
   dh_strip_nondeterminism -a -O--buildsystem=octave
   dh_compress -a -O--buildsystem=octave
   dh_fixperms -a -O--buildsystem=octave
   dh_missing -a -O--buildsystem=octave
   dh_dwz -a -O--buildsystem=octave
   dh_strip -a -O--buildsystem=octave
   dh_makeshlibs -a -O--buildsystem=octave
   dh_shlibdeps -a -l/usr/lib/riscv64-linux-gnu/octave/10.3.0 -O--buildsystem=octave
dpkg-shlibdeps: warning: diversions involved - output may be incorrect
 diversion by libc6 from: /lib/ld-linux-riscv64-lp64d.so.1
dpkg-shlibdeps: warning: diversions involved - output may be incorrect
 diversion by libc6 to: /lib/ld-linux-riscv64-lp64d.so.1.usr-is-merged
   dh_octave_substvar -a -O--buildsystem=octave
   dh_installdeb -a -O--buildsystem=octave
   dh_gencontrol -a -O--buildsystem=octave
dpkg-gencontrol: warning: package octave-statistics: substitution variable ${octave:Upstream-Description} unused, but is defined
dpkg-gencontrol: warning: package octave-statistics: substitution variable ${octave:Upstream-Description} unused, but is defined
   dh_md5sums -a -O--buildsystem=octave
   dh_builddeb -a -O--buildsystem=octave
dpkg-deb: building package 'octave-statistics-dbgsym' in '../octave-statistics-dbgsym_1.8.1-3_riscv64.deb'.
dpkg-deb: building package 'octave-statistics' in '../octave-statistics_1.8.1-3_riscv64.deb'.
 dpkg-genbuildinfo --build=any -O../octave-statistics_1.8.1-3_riscv64.buildinfo
 dpkg-genchanges --build=any -O../octave-statistics_1.8.1-3_riscv64.changes
dpkg-genchanges: info: binary-only arch-specific upload (source code and arch-indep packages not included)
 dpkg-source --after-build .
dpkg-buildpackage: info: binary-only upload (no source included)
--------------------------------------------------------------------------------
Build finished at 2026-02-26T23:51:28Z

Finished
--------

I: Built successfully

+------------------------------------------------------------------------------+
| Changes                                      Thu, 26 Feb 2026 23:51:29 +0000 |
+------------------------------------------------------------------------------+


octave-statistics_1.8.1-3_riscv64.changes:
------------------------------------------

Format: 1.8
Date: Tue, 24 Feb 2026 11:27:48 +0000
Source: octave-statistics
Binary: octave-statistics octave-statistics-dbgsym
Architecture: riscv64
Version: 1.8.1-3
Distribution: unstable
Urgency: medium
Maintainer: Debian Octave Group <team+pkg-octave-team@tracker.debian.org>
Changed-By: Rafael Laboissière <rafael@debian.org>
Description:
 octave-statistics - additional statistical functions for Octave
Changes:
 octave-statistics (1.8.1-3) unstable; urgency=medium
 .
   * d/p/relax-tolerance-unit-test-grpstats.patch: New patch.
     This fixes the FTBFS problem on i368
Checksums-Sha1:
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 e05026c17fa89a435d6ee15a42d92df458795fd3 21807 octave-statistics_1.8.1-3_riscv64.buildinfo
 58e2017e6e81d64f4c252cb9a5373d2afb79290f 139624 octave-statistics_1.8.1-3_riscv64.deb
Checksums-Sha256:
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 01fb8ab79d383fa848e026e0709a943eae73263d2dadb215dfe66eabe1fe0606 21807 octave-statistics_1.8.1-3_riscv64.buildinfo
 0988e81dd4403f0e775d0b147935b568fb16711dfcabb1fdc36408300b286b30 139624 octave-statistics_1.8.1-3_riscv64.deb
Files:
 7804e89c59ef7a9d5c2f16b9436e8352 3365756 debug optional octave-statistics-dbgsym_1.8.1-3_riscv64.deb
 942e351248e5c9998facd757d39b6fff 21807 math optional octave-statistics_1.8.1-3_riscv64.buildinfo
 ad0e8833d3afa2385c3f7b341a0c7630 139624 math optional octave-statistics_1.8.1-3_riscv64.deb

+------------------------------------------------------------------------------+
| Buildinfo                                    Thu, 26 Feb 2026 23:51:30 +0000 |
+------------------------------------------------------------------------------+

Format: 1.0
Source: octave-statistics
Binary: octave-statistics octave-statistics-dbgsym
Architecture: riscv64
Version: 1.8.1-3
Checksums-Md5:
 7804e89c59ef7a9d5c2f16b9436e8352 3365756 octave-statistics-dbgsym_1.8.1-3_riscv64.deb
 ad0e8833d3afa2385c3f7b341a0c7630 139624 octave-statistics_1.8.1-3_riscv64.deb
Checksums-Sha1:
 cce67b564a30962b5330769b41519b7f682f47d3 3365756 octave-statistics-dbgsym_1.8.1-3_riscv64.deb
 58e2017e6e81d64f4c252cb9a5373d2afb79290f 139624 octave-statistics_1.8.1-3_riscv64.deb
Checksums-Sha256:
 fb00a53d73b01c4704bd37de9f93b4fe58ff7eda661a6368bf9600321e434cf4 3365756 octave-statistics-dbgsym_1.8.1-3_riscv64.deb
 0988e81dd4403f0e775d0b147935b568fb16711dfcabb1fdc36408300b286b30 139624 octave-statistics_1.8.1-3_riscv64.deb
Build-Origin: Debian
Build-Architecture: riscv64
Build-Date: Thu, 26 Feb 2026 23:51:26 +0000
Build-Path: /build/reproducible-path/octave-statistics-1.8.1
Installed-Build-Depends:
 aglfn (= 1.7+git20191031.4036a9c-2),
 appstream (= 1.1.2-1),
 autoconf (= 2.72-3.1),
 automake (= 1:1.18.1-3),
 autopoint (= 0.23.2-1),
 autotools-dev (= 20240727.1),
 base-files (= 14),
 base-passwd (= 3.6.8),
 bash (= 5.3-2),
 binutils (= 2.46-2),
 binutils-common (= 2.46-2),
 binutils-riscv64-linux-gnu (= 2.46-2),
 bsdextrautils (= 2.41.3-4),
 build-essential (= 12.12),
 bzip2 (= 1.0.8-6+b1),
 ca-certificates (= 20250419),
 cme (= 1.044-2),
 comerr-dev (= 2.1-1.47.2-3+b8),
 coreutils (= 9.7-3),
 cpp (= 4:15.2.0-5),
 cpp-15 (= 15.2.0-13),
 cpp-15-riscv64-linux-gnu (= 15.2.0-13),
 cpp-riscv64-linux-gnu (= 4:15.2.0-5),
 dash (= 0.5.12-12),
 debconf (= 1.5.92),
 debhelper (= 13.30),
 debianutils (= 5.23.2),
 dh-autoreconf (= 21+nmu1),
 dh-octave (= 1.14.1),
 dh-octave-autopkgtest (= 1.14.1),
 dh-strip-nondeterminism (= 1.15.0-1),
 diffstat (= 1.68-1),
 diffutils (= 1:3.12-1),
 dpkg (= 1.23.5),
 dpkg-dev (= 1.23.5),
 dwz (= 0.16-2),
 file (= 1:5.46-5+b1),
 findutils (= 4.10.0-3),
 fontconfig (= 2.17.1-5),
 fontconfig-config (= 2.17.1-5),
 fonts-freefont-otf (= 20211204+svn4273-4),
 g++ (= 4:15.2.0-5),
 g++-15 (= 15.2.0-13),
 g++-15-riscv64-linux-gnu (= 15.2.0-13),
 g++-riscv64-linux-gnu (= 4:15.2.0-5),
 gcc (= 4:15.2.0-5),
 gcc-15 (= 15.2.0-13),
 gcc-15-base (= 15.2.0-13),
 gcc-15-riscv64-linux-gnu (= 15.2.0-13),
 gcc-riscv64-linux-gnu (= 4:15.2.0-5),
 gettext (= 0.23.2-1),
 gettext-base (= 0.23.2-1),
 gfortran (= 4:15.2.0-5),
 gfortran-15 (= 15.2.0-13),
 gfortran-15-riscv64-linux-gnu (= 15.2.0-13),
 gfortran-riscv64-linux-gnu (= 4:15.2.0-5),
 gnuplot-data (= 6.0.3+dfsg1-1),
 gnuplot-nox (= 6.0.3+dfsg1-1),
 gpg (= 2.4.8-5),
 gpgconf (= 2.4.8-5),
 grep (= 3.12-1),
 groff-base (= 1.23.0-10),
 gzip (= 1.13-1),
 hdf5-helpers (= 1.14.6+repack-2),
 hostname (= 3.25),
 init-system-helpers (= 1.69),
 intltool-debian (= 0.35.0+20060710.6),
 iso-codes (= 4.20.1-1),
 krb5-multidev (= 1.22.1-2),
 libabsl20240722 (= 20240722.0-4),
 libacl1 (= 2.3.2-3),
 libaec-dev (= 1.1.5-1),
 libaec0 (= 1.1.5-1),
 libalgorithm-c3-perl (= 0.11-2),
 libaliased-perl (= 0.34-3),
 libamd3 (= 1:7.12.2+dfsg-1),
 libaom3 (= 3.13.1-2),
 libapp-cmd-perl (= 0.339-1),
 libappstream5 (= 1.1.2-1),
 libapt-pkg-perl (= 0.1.43),
 libapt-pkg7.0 (= 3.1.16),
 libarchive-zip-perl (= 1.68-1),
 libarpack2t64 (= 3.9.1-6+b1),
 libarray-intspan-perl (= 2.004-2),
 libasan8 (= 15.2.0-13),
 libasound2-data (= 1.2.15.3-1),
 libasound2t64 (= 1.2.15.3-1),
 libassuan9 (= 3.0.2-2+b1),
 libatomic1 (= 15.2.0-13),
 libattr1 (= 1:2.5.2-4),
 libaudit-common (= 1:4.1.2-1),
 libaudit1 (= 1:4.1.2-1+b1),
 libavahi-client3 (= 0.8-18),
 libavahi-common-data (= 0.8-18),
 libavahi-common3 (= 0.8-18),
 libavif16 (= 1.3.0-1+b2),
 libb-hooks-endofscope-perl (= 0.28-2),
 libb-hooks-op-check-perl (= 0.22-3+b3),
 libb-keywords-perl (= 1.29-1),
 libb2-1 (= 0.98.1-1.1+b3),
 libberkeleydb-perl (= 0.66-2),
 libbinutils (= 2.46-2),
 libblas-dev (= 3.12.1-7+b1),
 libblas3 (= 3.12.1-7+b1),
 libblkid1 (= 2.41.3-4),
 libboolean-perl (= 0.46-3),
 libbrotli-dev (= 1.2.0-3),
 libbrotli1 (= 1.2.0-3),
 libbsd0 (= 0.12.2-2+b1),
 libbz2-1.0 (= 1.0.8-6+b1),
 libc-bin (= 2.42-13),
 libc-dev-bin (= 2.42-13),
 libc-gconv-modules-extra (= 2.42-13),
 libc6 (= 2.42-13),
 libc6-dev (= 2.42-13),
 libcairo2 (= 1.18.4-3),
 libcamd3 (= 1:7.12.2+dfsg-1),
 libcap-ng0 (= 0.9.1-1),
 libcap2 (= 1:2.75-10+b5),
 libcapture-tiny-perl (= 0.50-1),
 libcarp-assert-more-perl (= 2.9.0-1),
 libcc1-0 (= 15.2.0-13),
 libccolamd3 (= 1:7.12.2+dfsg-1),
 libcgi-pm-perl (= 4.71-1),
 libcholmod5 (= 1:7.12.2+dfsg-1),
 libclass-c3-perl (= 0.35-2),
 libclass-data-inheritable-perl (= 0.10-1),
 libclass-inspector-perl (= 1.36-3),
 libclass-load-perl (= 0.25-2),
 libclass-method-modifiers-perl (= 2.15-1),
 libclass-tiny-perl (= 1.008-2),
 libclass-xsaccessor-perl (= 1.19-4+b4),
 libclone-choose-perl (= 0.010-2),
 libclone-perl (= 0.47-1+b2),
 libcolamd3 (= 1:7.12.2+dfsg-1),
 libcom-err2 (= 1.47.2-3+b8),
 libconfig-inifiles-perl (= 3.000003-4),
 libconfig-model-backend-yaml-perl (= 2.134-2),
 libconfig-model-dpkg-perl (= 3.017),
 libconfig-model-perl (= 2.155-1),
 libconfig-tiny-perl (= 2.30-1),
 libconst-fast-perl (= 0.014-2),
 libconvert-binhex-perl (= 1.125-3),
 libcpanel-json-xs-perl (= 4.40-1),
 libcrypt1 (= 1:4.5.1-1),
 libctf-nobfd0 (= 2.46-2),
 libctf0 (= 2.46-2),
 libcups2t64 (= 2.4.16-1),
 libcurl3t64-gnutls (= 8.19.0~rc2-2),
 libcurl4-openssl-dev (= 8.19.0~rc2-2),
 libcurl4t64 (= 8.19.0~rc2-2),
 libcxsparse4 (= 1:7.12.2+dfsg-1),
 libdata-dpath-perl (= 0.60-1),
 libdata-messagepack-perl (= 1.02-3),
 libdata-optlist-perl (= 0.114-1),
 libdata-section-perl (= 0.200008-1),
 libdata-validate-domain-perl (= 0.15-1),
 libdata-validate-ip-perl (= 0.31-1),
 libdata-validate-uri-perl (= 0.07-3),
 libdatrie1 (= 0.2.14-1),
 libdav1d7 (= 1.5.3-1+b1),
 libdb5.3t64 (= 5.3.28+dfsg2-11),
 libdbus-1-3 (= 1.16.2-4),
 libde265-0 (= 1.0.16-1+b1),
 libdebconfclient0 (= 0.282+b2),
 libdebhelper-perl (= 13.30),
 libdeflate0 (= 1.23-2+b1),
 libdevel-callchecker-perl (= 0.009-3),
 libdevel-size-perl (= 0.86-1),
 libdevel-stacktrace-perl (= 2.0500-1),
 libdouble-conversion3 (= 3.4.0-1),
 libdpkg-perl (= 1.23.5),
 libdrm-amdgpu1 (= 2.4.131-1),
 libdrm-common (= 2.4.131-1),
 libdrm2 (= 2.4.131-1),
 libduktape207 (= 2.7.0-2+b3),
 libdynaloader-functions-perl (= 0.004-2),
 libedit2 (= 3.1-20251016-1),
 libegl-mesa0 (= 26.0.0-1),
 libegl1 (= 1.7.0-3),
 libelf1t64 (= 0.194-1),
 libemail-address-xs-perl (= 1.05-1+b3),
 libencode-locale-perl (= 1.05-3),
 liberror-perl (= 0.17030-1),
 libevdev2 (= 1.13.6+dfsg-1),
 libevent-2.1-7t64 (= 2.1.12-stable-10+b2),
 libexception-class-perl (= 1.45-1),
 libexpat1 (= 2.7.4-1),
 libexporter-lite-perl (= 0.09-2),
 libexporter-tiny-perl (= 1.006003-1),
 libfeature-compat-class-perl (= 0.08-1),
 libfeature-compat-try-perl (= 0.05-1),
 libffi8 (= 3.5.2-3+b1),
 libfftw3-bin (= 3.3.10-2+b2),
 libfftw3-dev (= 3.3.10-2+b2),
 libfftw3-double3 (= 3.3.10-2+b2),
 libfftw3-long3 (= 3.3.10-2+b2),
 libfftw3-single3 (= 3.3.10-2+b2),
 libfile-basedir-perl (= 0.09-2),
 libfile-find-rule-perl (= 0.35-1),
 libfile-homedir-perl (= 1.006-2),
 libfile-libmagic-perl (= 1.23-2+b2),
 libfile-listing-perl (= 6.16-1),
 libfile-sharedir-perl (= 1.118-3),
 libfile-stripnondeterminism-perl (= 1.15.0-1),
 libfile-which-perl (= 1.27-2),
 libflac14 (= 1.5.0+ds-5),
 libfltk-gl1.3t64 (= 1.3.11-3),
 libfltk1.3t64 (= 1.3.11-3),
 libfont-ttf-perl (= 1.06-2),
 libfontconfig1 (= 2.17.1-5),
 libfreetype6 (= 2.14.1+dfsg-2),
 libfribidi0 (= 1.0.16-5),
 libfyaml0 (= 0.9.4-1),
 libgav1-2 (= 0.20.0-2),
 libgbm1 (= 26.0.0-1),
 libgcc-15-dev (= 15.2.0-13),
 libgcc-s1 (= 15.2.0-13),
 libgcrypt20 (= 1.11.2-3+b1),
 libgd3 (= 2.3.3-13+b1),
 libgdbm-compat4t64 (= 1.26-1+b1),
 libgdbm6t64 (= 1.26-1+b1),
 libgetopt-long-descriptive-perl (= 0.117-1),
 libgfortran-15-dev (= 15.2.0-13),
 libgfortran5 (= 15.2.0-13),
 libgl-dev (= 1.7.0-3),
 libgl1 (= 1.7.0-3),
 libgl1-mesa-dri (= 26.0.0-1),
 libgl2ps1.4 (= 1.4.2+dfsg1-4),
 libglib2.0-0t64 (= 2.87.2-3),
 libglpk40 (= 5.0-2+b1),
 libglu1-mesa (= 9.0.2-1.1+b4),
 libglvnd0 (= 1.7.0-3),
 libglx-dev (= 1.7.0-3),
 libglx-mesa0 (= 26.0.0-1),
 libglx0 (= 1.7.0-3),
 libgmp-dev (= 2:6.3.0+dfsg-5+b1),
 libgmp10 (= 2:6.3.0+dfsg-5+b1),
 libgmpxx4ldbl (= 2:6.3.0+dfsg-5+b1),
 libgnutls-dane0t64 (= 3.8.12-3),
 libgnutls28-dev (= 3.8.12-3),
 libgnutls30t64 (= 3.8.12-3),
 libgomp1 (= 15.2.0-13),
 libgpg-error0 (= 1.58-2),
 libgprofng0 (= 2.46-2),
 libgraphicsmagick++-q16-12t64 (= 1.4+really1.3.46-2),
 libgraphicsmagick-q16-3t64 (= 1.4+really1.3.46-2),
 libgraphite2-3 (= 1.3.14-11+b1),
 libgssapi-krb5-2 (= 1.22.1-2),
 libgssrpc4t64 (= 1.22.1-2),
 libgudev-1.0-0 (= 238-7+b1),
 libharfbuzz0b (= 12.3.2-2),
 libhash-merge-perl (= 0.302-1),
 libhdf5-310 (= 1.14.6+repack-2),
 libhdf5-cpp-310 (= 1.14.6+repack-2),
 libhdf5-dev (= 1.14.6+repack-2),
 libhdf5-fortran-310 (= 1.14.6+repack-2),
 libhdf5-hl-310 (= 1.14.6+repack-2),
 libhdf5-hl-cpp-310 (= 1.14.6+repack-2),
 libhdf5-hl-fortran-310 (= 1.14.6+repack-2),
 libheif-plugin-dav1d (= 1.21.2-3),
 libheif-plugin-libde265 (= 1.21.2-3),
 libheif1 (= 1.21.2-3),
 libhogweed6t64 (= 3.10.2-1),
 libhtml-form-perl (= 6.13-1),
 libhtml-html5-entities-perl (= 0.004-3),
 libhtml-parser-perl (= 3.83-1+b3),
 libhtml-tagset-perl (= 3.24-1),
 libhtml-tokeparser-simple-perl (= 3.16-4),
 libhtml-tree-perl (= 5.07-3),
 libhttp-cookies-perl (= 6.11-1),
 libhttp-date-perl (= 6.06-1),
 libhttp-message-perl (= 7.01-1),
 libhttp-negotiate-perl (= 6.01-2),
 libice6 (= 2:1.1.1-1+b1),
 libicu76 (= 76.1-4+b1),
 libidn2-0 (= 2.3.8-4+b1),
 libidn2-dev (= 2.3.8-4+b1),
 libimagequant0 (= 4.4.1-1+b1),
 libimport-into-perl (= 1.002005-2),
 libindirect-perl (= 0.39-2+b3),
 libinput-bin (= 1.31.0-1),
 libinput10 (= 1.31.0-1),
 libintl-perl (= 1.37-1),
 libio-html-perl (= 1.004-3),
 libio-interactive-perl (= 1.027-1),
 libio-socket-ssl-perl (= 2.098-1),
 libio-string-perl (= 1.08-4),
 libio-stringy-perl (= 2.113-2),
 libio-tiecombine-perl (= 1.005-3),
 libipc-run3-perl (= 0.049-1),
 libipc-system-simple-perl (= 1.30-2),
 libisl23 (= 0.27-1+b1),
 libiterator-perl (= 0.03+ds1-2),
 libiterator-util-perl (= 0.02+ds1-2),
 libitm1 (= 15.2.0-13),
 libjack-jackd2-0 (= 1.9.22~dfsg-5+b1),
 libjansson4 (= 2.14-2+b4),
 libjbig0 (= 2.1-6.1+b3),
 libjpeg-dev (= 1:2.1.5-4),
 libjpeg62-turbo (= 1:2.1.5-4),
 libjpeg62-turbo-dev (= 1:2.1.5-4),
 libjson-maybexs-perl (= 1.004008-1),
 libjson-perl (= 4.10000-1),
 libjxl0.11 (= 0.11.1-6),
 libk5crypto3 (= 1.22.1-2),
 libkadm5clnt-mit12 (= 1.22.1-2),
 libkadm5srv-mit12 (= 1.22.1-2),
 libkdb5-10t64 (= 1.22.1-2),
 libkeyutils1 (= 1.6.3-6+b1),
 libkrb5-3 (= 1.22.1-2),
 libkrb5-dev (= 1.22.1-2),
 libkrb5support0 (= 1.22.1-2),
 libksba8 (= 1.6.7-2+b2),
 liblapack-dev (= 3.12.1-7+b1),
 liblapack3 (= 3.12.1-7+b1),
 liblcms2-2 (= 2.17-1),
 libldap-dev (= 2.6.10+dfsg-1+b1),
 libldap2 (= 2.6.10+dfsg-1+b1),
 liblerc4 (= 4.0.0+ds-5+b1),
 liblingua-en-inflect-perl (= 1.905-2),
 liblist-compare-perl (= 0.55-2),
 liblist-moreutils-perl (= 0.430-2),
 liblist-moreutils-xs-perl (= 0.430-4+b2),
 liblist-someutils-perl (= 0.59-1),
 liblist-utilsby-perl (= 0.12-2),
 libllvm21 (= 1:21.1.8-3),
 liblog-any-adapter-screen-perl (= 0.141-2),
 liblog-any-perl (= 1.718-1),
 liblog-log4perl-perl (= 1.57-1),
 liblsan0 (= 15.2.0-13),
 libltdl7 (= 2.5.4-9),
 liblua5.4-0 (= 5.4.8-1+b1),
 liblwp-mediatypes-perl (= 6.04-2),
 liblwp-protocol-https-perl (= 6.14-1),
 liblz1 (= 1.16~rc1-3),
 liblz4-1 (= 1.10.0-6),
 liblzma5 (= 5.8.2-2),
 liblzo2-2 (= 2.10-3+b2),
 libmagic-mgc (= 1:5.46-5+b1),
 libmagic1t64 (= 1:5.46-5+b1),
 libmailtools-perl (= 2.22-1),
 libmarkdown2 (= 2.2.7-2.1+b1),
 libmd0 (= 1.1.0-2+b2),
 libmd4c0 (= 0.5.2-2+b2),
 libmime-tools-perl (= 5.517-1),
 libmldbm-perl (= 2.05-4),
 libmodule-implementation-perl (= 0.09-2),
 libmodule-pluggable-perl (= 6.3-1),
 libmodule-runtime-perl (= 0.018-1),
 libmoo-perl (= 2.005005-1),
 libmoox-aliases-perl (= 0.001006-3),
 libmount1 (= 2.41.3-4),
 libmouse-perl (= 2.6.1-1),
 libmousex-nativetraits-perl (= 1.09-3),
 libmousex-strictconstructor-perl (= 0.02-3),
 libmp3lame0 (= 3.101~svn6525+dfsg-2),
 libmpc3 (= 1.3.1-2+b1),
 libmpfr6 (= 4.2.2-2+b1),
 libmpg123-0t64 (= 1.33.3-2),
 libmro-compat-perl (= 0.15-2),
 libmtdev1t64 (= 1.1.7-1+b1),
 libnamespace-clean-perl (= 0.27-2),
 libncurses-dev (= 6.6+20251231-1),
 libncurses6 (= 6.6+20251231-1),
 libncursesw6 (= 6.6+20251231-1),
 libnet-domain-tld-perl (= 1.75-4),
 libnet-http-perl (= 6.24-1),
 libnet-ipv6addr-perl (= 1.02-1),
 libnet-netmask-perl (= 2.0003-1),
 libnet-smtp-ssl-perl (= 1.04-2),
 libnet-ssleay-perl (= 1.94-3+b1),
 libnetaddr-ip-perl (= 4.079+dfsg-2+b4),
 libnettle8t64 (= 3.10.2-1),
 libnghttp2-14 (= 1.68.0-1),
 libnghttp2-dev (= 1.68.0-1),
 libnghttp3-9 (= 1.12.0-1),
 libnghttp3-dev (= 1.12.0-1),
 libngtcp2-16 (= 1.16.0-1),
 libngtcp2-crypto-gnutls8 (= 1.16.0-1),
 libngtcp2-crypto-ossl-dev (= 1.16.0-1),
 libngtcp2-crypto-ossl0 (= 1.16.0-1),
 libngtcp2-dev (= 1.16.0-1),
 libnpth0t64 (= 1.8-3+b1),
 libnumber-compare-perl (= 0.03-3),
 libobject-pad-perl (= 0.823-2),
 libogg0 (= 1.3.6-2),
 libopengl0 (= 1.7.0-3),
 libopus0 (= 1.6.1-1),
 libp11-kit-dev (= 0.26.2-2),
 libp11-kit0 (= 0.26.2-2),
 libpackage-stash-perl (= 0.40-1),
 libpam-modules (= 1.7.0-5+b1),
 libpam-modules-bin (= 1.7.0-5+b1),
 libpam-runtime (= 1.7.0-5),
 libpam0g (= 1.7.0-5+b1),
 libpango-1.0-0 (= 1.57.0-1),
 libpangocairo-1.0-0 (= 1.57.0-1),
 libpangoft2-1.0-0 (= 1.57.0-1),
 libparams-classify-perl (= 0.015-2+b5),
 libparams-util-perl (= 1.102-3+b1),
 libparams-validate-perl (= 1.31-2+b4),
 libparse-debcontrol-perl (= 2.005-6),
 libparse-recdescent-perl (= 1.967015+dfsg-4),
 libpath-iterator-rule-perl (= 1.015-2),
 libpath-tiny-perl (= 0.148-1),
 libpcre2-16-0 (= 10.46-1+b1),
 libpcre2-8-0 (= 10.46-1+b1),
 libperl-critic-perl (= 1.156-1),
 libperl5.40 (= 5.40.1-7),
 libperlio-gzip-perl (= 0.20-1+b3),
 libperlio-utf8-strict-perl (= 0.010-1+b3),
 libpipeline1 (= 1.5.8-2),
 libpixman-1-0 (= 0.46.4-1+b1),
 libpkgconf7 (= 2.5.1-4),
 libpng16-16t64 (= 1.6.55-1),
 libpod-constants-perl (= 0.19-2),
 libpod-parser-perl (= 1.67-1),
 libpod-pom-perl (= 2.01-4),
 libpod-spell-perl (= 1.27-1),
 libportaudio2 (= 19.7.0-1),
 libppi-perl (= 1.284-1),
 libppix-quotelike-perl (= 0.023-1),
 libppix-regexp-perl (= 0.091-1),
 libppix-utils-perl (= 0.003-2),
 libproc-processtable-perl (= 0.637-1+b1),
 libproc2-0 (= 2:4.0.4-9+b1),
 libproxy1v5 (= 0.5.12-1),
 libpsl-dev (= 0.21.2-1.1+b2),
 libpsl5t64 (= 0.21.2-1.1+b2),
 libqhull-r8.0 (= 2020.2-8),
 libqrupdate1 (= 1.1.5-3),
 libqscintilla2-qt6-15 (= 2.14.1+dfsg-2),
 libqscintilla2-qt6-l10n (= 2.14.1+dfsg-2),
 libqt6core5compat6 (= 6.9.2-3),
 libqt6core6t64 (= 6.9.2+dfsg-4),
 libqt6dbus6 (= 6.9.2+dfsg-4),
 libqt6gui6 (= 6.9.2+dfsg-4),
 libqt6help6 (= 6.9.2-5),
 libqt6network6 (= 6.9.2+dfsg-4),
 libqt6opengl6 (= 6.9.2+dfsg-4),
 libqt6openglwidgets6 (= 6.9.2+dfsg-4),
 libqt6printsupport6 (= 6.9.2+dfsg-4),
 libqt6sql6 (= 6.9.2+dfsg-4),
 libqt6widgets6 (= 6.9.2+dfsg-4),
 libqt6xml6 (= 6.9.2+dfsg-4),
 librav1e0.8 (= 0.8.1-7),
 libreadline-dev (= 8.3-4),
 libreadline8t64 (= 8.3-4),
 libreadonly-perl (= 2.050-3),
 libregexp-common-perl (= 2024080801-1),
 libregexp-pattern-license-perl (= 3.11.2-1),
 libregexp-pattern-perl (= 0.2.14-3),
 libregexp-wildcards-perl (= 1.05-3),
 librole-tiny-perl (= 2.002004-1),
 librtmp-dev (= 2.4+20151223.gitfa8646d.1-3+b1),
 librtmp1 (= 2.4+20151223.gitfa8646d.1-3+b1),
 libsafe-isa-perl (= 1.000010-1),
 libsamplerate0 (= 0.2.2-4+b3),
 libsasl2-2 (= 2.1.28+dfsg1-10),
 libsasl2-modules-db (= 2.1.28+dfsg1-10),
 libselinux1 (= 3.9-4+b1),
 libsensors-config (= 1:3.6.2-2),
 libsensors5 (= 1:3.6.2-2+b1),
 libsereal-decoder-perl (= 5.004+ds-1+b4),
 libsereal-encoder-perl (= 5.004+ds-1+b3),
 libset-intspan-perl (= 1.19-3),
 libsframe3 (= 2.46-2),
 libsharpyuv0 (= 1.5.0-0.1+b1),
 libsm6 (= 2:1.2.6-1+b1),
 libsmartcols1 (= 2.41.3-4),
 libsndfile1 (= 1.2.2-4),
 libsoftware-copyright-perl (= 0.015-1),
 libsoftware-license-perl (= 0.104007-1),
 libsoftware-licensemoreutils-perl (= 1.009-1),
 libsort-versions-perl (= 1.62-3),
 libspqr4 (= 1:7.12.2+dfsg-1),
 libsqlite3-0 (= 3.46.1-9),
 libssh2-1-dev (= 1.11.1-1+b1),
 libssh2-1t64 (= 1.11.1-1+b1),
 libssl-dev (= 3.5.5-1),
 libssl3t64 (= 3.5.5-1),
 libstdc++-15-dev (= 15.2.0-13),
 libstdc++6 (= 15.2.0-13),
 libstemmer0d (= 3.0.1-1+b1),
 libstrictures-perl (= 2.000006-1),
 libstring-copyright-perl (= 0.003014-1),
 libstring-escape-perl (= 2010.002-3),
 libstring-format-perl (= 1.18-1),
 libstring-license-perl (= 0.0.11-1),
 libstring-rewriteprefix-perl (= 0.009-1),
 libsub-exporter-perl (= 0.990-1),
 libsub-exporter-progressive-perl (= 0.001013-3),
 libsub-identify-perl (= 0.14-4),
 libsub-install-perl (= 0.929-1),
 libsub-name-perl (= 0.28-1+b1),
 libsub-quote-perl (= 2.006009-1),
 libsub-uplevel-perl (= 0.2800-3),
 libsuitesparseconfig7 (= 1:7.12.2+dfsg-1),
 libsvtav1enc2 (= 2.3.0+dfsg-1),
 libsyntax-keyword-try-perl (= 0.31-1),
 libsystemd0 (= 259.1-1),
 libsz2 (= 1.1.5-1),
 libtask-weaken-perl (= 1.06-2),
 libtasn1-6 (= 4.21.0-2),
 libtasn1-6-dev (= 4.21.0-2),
 libterm-readkey-perl (= 2.38-2+b3),
 libtest-exception-perl (= 0.43-3),
 libtext-autoformat-perl (= 1.750000-2),
 libtext-charwidth-perl (= 0.04-11+b5),
 libtext-glob-perl (= 0.11-3),
 libtext-levenshtein-damerau-perl (= 0.41-3),
 libtext-levenshteinxs-perl (= 0.03-5+b3),
 libtext-markdown-discount-perl (= 0.18-1),
 libtext-reform-perl (= 1.20-5),
 libtext-template-perl (= 1.61-1),
 libtext-unidecode-perl (= 1.30-3),
 libtext-wrapi18n-perl (= 0.06-10),
 libtext-wrapper-perl (= 1.05-4),
 libtext-xslate-perl (= 3.5.9-2+b2),
 libthai-data (= 0.1.30-1),
 libthai0 (= 0.1.30-1),
 libtiff6 (= 4.7.1-1),
 libtime-duration-perl (= 1.21-2),
 libtime-moment-perl (= 0.46-1),
 libtimedate-perl (= 2.3300-2),
 libtinfo6 (= 6.6+20251231-1),
 libtoml-tiny-perl (= 0.20-1),
 libtool (= 2.5.4-9),
 libtry-tiny-perl (= 0.32-1),
 libts0t64 (= 1.22-1.1+b2),
 libtsan2 (= 15.2.0-13),
 libubsan1 (= 15.2.0-13),
 libuchardet0 (= 0.0.8-2+b1),
 libudev1 (= 259.1-1),
 libumfpack6 (= 1:7.12.2+dfsg-1),
 libunbound8 (= 1.24.2-1),
 libunicode-utf8-perl (= 0.64-1),
 libunistring5 (= 1.3-2+b1),
 liburi-perl (= 5.34-2),
 libuuid1 (= 2.41.3-4),
 libvariable-magic-perl (= 0.64-1+b1),
 libvorbis0a (= 1.3.7-3+b1),
 libvorbisenc2 (= 1.3.7-3+b1),
 libvulkan1 (= 1.4.341.0-1),
 libwacom-common (= 2.18.0-1),
 libwacom9 (= 2.18.0-1),
 libwayland-client0 (= 1.24.0-2+b2),
 libwebp7 (= 1.5.0-0.1+b1),
 libwebpmux3 (= 1.5.0-0.1+b1),
 libwmflite-0.2-7 (= 0.2.13-2),
 libwww-mechanize-perl (= 2.20-1),
 libwww-perl (= 6.81-1),
 libwww-robotrules-perl (= 6.02-1),
 libx11-6 (= 2:1.8.13-1),
 libx11-data (= 2:1.8.13-1),
 libx11-dev (= 2:1.8.13-1),
 libx11-xcb1 (= 2:1.8.13-1),
 libxau-dev (= 1:1.0.11-1+b1),
 libxau6 (= 1:1.0.11-1+b1),
 libxcb-cursor0 (= 0.1.6-1),
 libxcb-dri3-0 (= 1.17.0-2+b2),
 libxcb-glx0 (= 1.17.0-2+b2),
 libxcb-icccm4 (= 0.4.2-1+b1),
 libxcb-image0 (= 0.4.0-2+b3),
 libxcb-keysyms1 (= 0.4.1-1+b1),
 libxcb-present0 (= 1.17.0-2+b2),
 libxcb-randr0 (= 1.17.0-2+b2),
 libxcb-render-util0 (= 0.3.10-1+b1),
 libxcb-render0 (= 1.17.0-2+b2),
 libxcb-shape0 (= 1.17.0-2+b2),
 libxcb-shm0 (= 1.17.0-2+b2),
 libxcb-sync1 (= 1.17.0-2+b2),
 libxcb-util1 (= 0.4.1-1+b1),
 libxcb-xfixes0 (= 1.17.0-2+b2),
 libxcb-xinput0 (= 1.17.0-2+b2),
 libxcb-xkb1 (= 1.17.0-2+b2),
 libxcb1 (= 1.17.0-2+b2),
 libxcb1-dev (= 1.17.0-2+b2),
 libxcursor1 (= 1:1.2.3-1+b1),
 libxdmcp-dev (= 1:1.1.5-2),
 libxdmcp6 (= 1:1.1.5-2),
 libxext6 (= 2:1.3.4-1+b4),
 libxfixes3 (= 1:6.0.0-2+b5),
 libxft2 (= 2.3.6-1+b5),
 libxinerama1 (= 2:1.1.4-3+b5),
 libxkbcommon-x11-0 (= 1.13.1-1),
 libxkbcommon0 (= 1.13.1-1),
 libxml-libxml-perl (= 2.0207+dfsg+really+2.0134-7),
 libxml-namespacesupport-perl (= 1.12-2),
 libxml-sax-base-perl (= 1.09-3),
 libxml-sax-perl (= 1.02+dfsg-4),
 libxml2-16 (= 2.15.1+dfsg-2+b1),
 libxmlb2 (= 0.3.24-2),
 libxpm4 (= 1:3.5.17-1+b4),
 libxrender1 (= 1:0.9.12-1+b1),
 libxs-parse-keyword-perl (= 0.49-1),
 libxs-parse-sublike-perl (= 0.41-1),
 libxshmfence1 (= 1.3.3-1+b1),
 libxxf86vm1 (= 1:1.1.4-2),
 libxxhash0 (= 0.8.3-2+b1),
 libyaml-0-2 (= 0.2.5-2+b1),
 libyaml-libyaml-perl (= 0.904.0+ds-1),
 libyaml-pp-perl (= 0.39.0-1),
 libyaml-tiny-perl (= 1.76-1),
 libyuv0 (= 0.0.1922.20260106-1),
 libz3-4 (= 4.13.3-1+b1),
 libzstd-dev (= 1.5.7+dfsg-3+b1),
 libzstd1 (= 1.5.7+dfsg-3+b1),
 licensecheck (= 3.3.9-1),
 lintian (= 2.130.0),
 linux-libc-dev (= 6.18.12-1),
 lzop (= 1.04-2+b1),
 m4 (= 1.4.21-1),
 make (= 4.4.1-3),
 man-db (= 2.13.1-1),
 mawk (= 1.3.4.20260129-1),
 mesa-libgallium (= 26.0.0-1),
 ncurses-base (= 6.6+20251231-1),
 ncurses-bin (= 6.6+20251231-1),
 netbase (= 6.5),
 nettle-dev (= 3.10.2-1),
 octave (= 10.3.0-3),
 octave-common (= 10.3.0-3),
 octave-datatypes (= 1.1.8-2),
 octave-dev (= 10.3.0-3),
 octave-io (= 2.7.1-1),
 openssl (= 3.5.5-1),
 openssl-provider-legacy (= 3.5.5-1),
 patch (= 2.8-2),
 patchutils (= 0.4.3-1),
 perl (= 5.40.1-7),
 perl-base (= 5.40.1-7),
 perl-modules-5.40 (= 5.40.1-7),
 perl-openssl-defaults (= 7+b2),
 perltidy (= 20250105-1),
 pkgconf (= 2.5.1-4),
 pkgconf-bin (= 2.5.1-4),
 plzip (= 1.13~rc1-3),
 po-debconf (= 1.0.22),
 procps (= 2:4.0.4-9+b1),
 readline-common (= 8.3-4),
 rpcsvc-proto (= 1.4.3-1+b2),
 sed (= 4.9-2),
 sensible-utils (= 0.0.26),
 shared-mime-info (= 2.4-5+b3),
 sysvinit-utils (= 3.15-6),
 t1utils (= 1.41-4+b1),
 tar (= 1.35+dfsg-4),
 tex-common (= 6.20),
 texinfo (= 7.2-5),
 texinfo-lib (= 7.2-5),
 ucf (= 3.0052),
 unzip (= 6.0-29),
 util-linux (= 2.41.3-4),
 x11-common (= 1:7.7+26),
 x11proto-dev (= 2025.1-1),
 xkb-data (= 2.46-2),
 xorg-sgml-doctools (= 1:1.11-1.1),
 xtrans-dev (= 1.6.0-1),
 xz-utils (= 5.8.2-2),
 zlib1g (= 1:1.3.dfsg+really1.3.1-3),
 zlib1g-dev (= 1:1.3.dfsg+really1.3.1-3)
Environment:
 DEB_BUILD_OPTIONS="parallel=4"
 LANG="C.UTF-8"
 LC_COLLATE="C.UTF-8"
 LC_CTYPE="C.UTF-8"
 SOURCE_DATE_EPOCH="1771932468"


+------------------------------------------------------------------------------+
| Package contents                             Thu, 26 Feb 2026 23:51:31 +0000 |
+------------------------------------------------------------------------------+


octave-statistics-dbgsym_1.8.1-3_riscv64.deb
--------------------------------------------

 new Debian package, version 2.0.
 size 3365756 bytes: control archive=904 bytes.
     648 bytes,    12 lines      control
     837 bytes,     8 lines      md5sums
 Package: octave-statistics-dbgsym
 Source: octave-statistics
 Version: 1.8.1-3
 Auto-Built-Package: debug-symbols
 Architecture: riscv64
 Maintainer: Debian Octave Group <team+pkg-octave-team@tracker.debian.org>
 Installed-Size: 3419
 Depends: octave-statistics (= 1.8.1-3)
 Section: debug
 Priority: optional
 Description: debug symbols for octave-statistics
 Build-Ids: 00c3859015b1c400ed1afa6b337e8a4ca998c717 15fafa2f662777a56ea19189ebc6b8afac98d66e 82d6289456527889c226c7bc67e98a05cea4972c 9842b79d73dfcdcf4c4195484dcee779bbe6a730 da23cba3581e2befcb5adab49b8e3baf8faa9514 f86edcc4886502225bce88edcfff7e0a405c7b85 ff5ee2a7c32e8f1444e4c5f21c8a7ba35b38fead

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drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/lib/debug/
drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/lib/debug/.build-id/
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drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/lib/debug/.build-id/f8/
-rw-r--r-- root/root    639992 2026-02-24 11:27 ./usr/lib/debug/.build-id/f8/6edcc4886502225bce88edcfff7e0a405c7b85.debug
drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/lib/debug/.build-id/ff/
-rw-r--r-- root/root    139032 2026-02-24 11:27 ./usr/lib/debug/.build-id/ff/5ee2a7c32e8f1444e4c5f21c8a7ba35b38fead.debug
drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/lib/debug/.dwz/
drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/lib/debug/.dwz/riscv64-linux-gnu/
-rw-r--r-- root/root    521816 2026-02-24 11:27 ./usr/lib/debug/.dwz/riscv64-linux-gnu/octave-statistics.debug
drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/share/
drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/share/doc/
lrwxrwxrwx root/root         0 2026-02-24 11:27 ./usr/share/doc/octave-statistics-dbgsym -> octave-statistics


octave-statistics_1.8.1-3_riscv64.deb
-------------------------------------

 new Debian package, version 2.0.
 size 139624 bytes: control archive=1220 bytes.
     847 bytes,    18 lines      control
    2173 bytes,    16 lines      md5sums
 Package: octave-statistics
 Version: 1.8.1-3
 Architecture: riscv64
 Maintainer: Debian Octave Group <team+pkg-octave-team@tracker.debian.org>
 Installed-Size: 472
 Depends: libc6 (>= 2.38), libgcc-s1 (>= 3.4), libgomp1 (>= 4.9), libstdc++6 (>= 14), octave-abi-60, octave (>= 10.3.0), octave-datatypes (>= 1.1.8), octave-statistics-common (= 1.8.1-3)
 Section: math
 Priority: optional
 Homepage: https://gnu-octave.github.io/packages/statistics/
 Description: additional statistical functions for Octave
  This package provides additional statistical functions for Octave,
  including mean and variance for several distributions (geometric,
  hypergeometric, exponential, lognormal and others).
  .
  This Octave add-on package is part of the Octave-Forge project.
  .
  This package contains the architecture-dependent files for the
  Octave statistics package.

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drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/
drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/packages/
drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/packages/statistics-1.8.1/
drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/packages/statistics-1.8.1/riscv64-unknown-linux-gnu-api-v60/
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-rw-r--r-- root/root     59944 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/packages/statistics-1.8.1/riscv64-unknown-linux-gnu-api-v60/fcnnpredict.oct
-rw-r--r-- root/root      4428 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/packages/statistics-1.8.1/riscv64-unknown-linux-gnu-api-v60/fcnntrain.cc-tst
-rw-r--r-- root/root     68280 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/packages/statistics-1.8.1/riscv64-unknown-linux-gnu-api-v60/fcnntrain.oct
-rw-r--r-- root/root       561 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/packages/statistics-1.8.1/riscv64-unknown-linux-gnu-api-v60/libsvmread.cc-tst
-rw-r--r-- root/root     27232 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/packages/statistics-1.8.1/riscv64-unknown-linux-gnu-api-v60/libsvmread.oct
-rw-r--r-- root/root       888 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/packages/statistics-1.8.1/riscv64-unknown-linux-gnu-api-v60/libsvmwrite.cc-tst
-rw-r--r-- root/root     22992 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/packages/statistics-1.8.1/riscv64-unknown-linux-gnu-api-v60/libsvmwrite.oct
-rw-r--r-- root/root      2514 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/packages/statistics-1.8.1/riscv64-unknown-linux-gnu-api-v60/svmpredict.cc-tst
-rw-r--r-- root/root    101200 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/packages/statistics-1.8.1/riscv64-unknown-linux-gnu-api-v60/svmpredict.oct
-rw-r--r-- root/root      2618 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/packages/statistics-1.8.1/riscv64-unknown-linux-gnu-api-v60/svmtrain.cc-tst
-rw-r--r-- root/root     97112 2026-02-24 11:27 ./usr/lib/riscv64-linux-gnu/octave/packages/statistics-1.8.1/riscv64-unknown-linux-gnu-api-v60/svmtrain.oct
drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/share/
drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/share/doc/
drwxr-xr-x root/root         0 2026-02-24 11:27 ./usr/share/doc/octave-statistics/
-rw-r--r-- root/root      4953 2026-02-24 11:27 ./usr/share/doc/octave-statistics/changelog.Debian.gz
-rw-r--r-- root/root      4673 2026-02-24 11:27 ./usr/share/doc/octave-statistics/copyright


+------------------------------------------------------------------------------+
| Post Build                                   Thu, 26 Feb 2026 23:51:33 +0000 |
+------------------------------------------------------------------------------+


+------------------------------------------------------------------------------+
| Cleanup                                      Thu, 26 Feb 2026 23:51:33 +0000 |
+------------------------------------------------------------------------------+

Purging /build/reproducible-path
Not cleaning session: cloned chroot in use

+------------------------------------------------------------------------------+
| Summary                                      Thu, 26 Feb 2026 23:51:38 +0000 |
+------------------------------------------------------------------------------+

Build Architecture: riscv64
Build Type: any
Build-Space: 49864
Build-Time: 1521
Distribution: unstable
Host Architecture: riscv64
Install-Time: 8
Job: /srv/rebuilderd/tmp/rebuilderdQSPWid/inputs/octave-statistics_1.8.1-3.dsc
Machine Architecture: riscv64
Package: octave-statistics
Package-Time: 1554
Source-Version: 1.8.1-3
Space: 49864
Status: successful
Version: 1.8.1-3
--------------------------------------------------------------------------------
Finished at 2026-02-26T23:51:28Z
Build needed 00:25:54, 49864k disk space
build artifacts stored in /srv/rebuilderd/tmp/rebuilderdQSPWid/out
checking octave-statistics-dbgsym_1.8.1-3_riscv64.deb: size differs for octave-statistics-dbgsym_1.8.1-3_riscv64.deb