--- /srv/rebuilderd/tmp/rebuilderdZ9yrdp/inputs/r-cran-forecast_8.23.0-1+b1_arm64.deb
+++ /srv/rebuilderd/tmp/rebuilderdZ9yrdp/out/r-cran-forecast_8.23.0-1+b1_arm64.deb
├── file list
│ @@ -1,3 +1,3 @@
│  -rw-r--r--   0        0        0        4 2024-11-03 06:38:04.000000 debian-binary
│ --rw-r--r--   0        0        0     2544 2024-11-03 06:38:04.000000 control.tar.xz
│ +-rw-r--r--   0        0        0     2528 2024-11-03 06:38:04.000000 control.tar.xz
│  -rw-r--r--   0        0        0  1558236 2024-11-03 06:38:04.000000 data.tar.xz
├── control.tar.xz
│ ├── control.tar
│ │ ├── file list
│ │ │ @@ -1,3 +1,3 @@
│ │ │  drwxr-xr-x   0 root         (0) root         (0)        0 2024-11-03 06:38:04.000000 ./
│ │ │ --rw-r--r--   0 root         (0) root         (0)     1001 2024-11-03 06:38:04.000000 ./control
│ │ │ +-rw-r--r--   0 root         (0) root         (0)      927 2024-11-03 06:38:04.000000 ./control
│ │ │  -rw-r--r--   0 root         (0) root         (0)     5701 2024-11-03 06:38:04.000000 ./md5sums
│ │ ├── ./control
│ │ │ @@ -1,15 +1,15 @@
│ │ │  Package: r-cran-forecast
│ │ │  Source: r-cran-forecast (8.23.0-1)
│ │ │  Version: 8.23.0-1+b1
│ │ │  Architecture: arm64
│ │ │  Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
│ │ │  Installed-Size: 1951
│ │ │  Depends: r-api-4.0, r-cran-colorspace, r-cran-fracdiff, r-cran-generics (>= 0.1.2), r-cran-ggplot2 (>= 2.2.1), r-cran-lmtest, r-cran-magrittr, r-cran-nnet, r-cran-rcpp (>= 0.11.0), r-cran-timedate, r-cran-tseries, r-cran-urca, r-cran-withr, r-cran-zoo, r-cran-rcpparmadillo (>= 0.2.35), libblas3 | libblas.so.3, libc6 (>= 2.35), libgcc-s1 (>= 3.0), libstdc++6 (>= 14)
│ │ │ -Recommends: r-cran-knitr, r-cran-rmarkdown, r-cran-scales, r-cran-testthat (>= 3.0.0), r-cran-uroot
│ │ │ +Recommends: r-cran-scales
│ │ │  Section: gnu-r
│ │ │  Priority: optional
│ │ │  Homepage: https://cran.r-project.org/package=forecast
│ │ │  Description: GNU R forecasting functions for time series and linear models
│ │ │   Methods and tools for displaying and analysing
│ │ │   univariate time series forecasts including exponential smoothing
│ │ │   via state space models and automatic ARIMA modelling.