--- /tmp/rebuilderdasd0Fq/inputs/r-bioc-glmgampoi_1.18.0+dfsg-2_riscv64.deb +++ /tmp/rebuilderdasd0Fq/out/r-bioc-glmgampoi_1.18.0+dfsg-2_riscv64.deb ├── file list │ @@ -1,3 +1,3 @@ │ -rw-r--r-- 0 0 0 4 2025-01-13 13:51:17.000000 debian-binary │ --rw-r--r-- 0 0 0 2024 2025-01-13 13:51:17.000000 control.tar.xz │ +-rw-r--r-- 0 0 0 1940 2025-01-13 13:51:17.000000 control.tar.xz │ -rw-r--r-- 0 0 0 1274612 2025-01-13 13:51:17.000000 data.tar.xz ├── control.tar.xz │ ├── control.tar │ │ ├── file list │ │ │ @@ -1,3 +1,3 @@ │ │ │ drwxr-xr-x 0 root (0) root (0) 0 2025-01-13 13:51:17.000000 ./ │ │ │ --rw-r--r-- 0 root (0) root (0) 1279 2025-01-13 13:51:17.000000 ./control │ │ │ +-rw-r--r-- 0 root (0) root (0) 1038 2025-01-13 13:51:17.000000 ./control │ │ │ -rw-r--r-- 0 root (0) root (0) 3147 2025-01-13 13:51:17.000000 ./md5sums │ │ ├── ./control │ │ │ @@ -1,14 +1,14 @@ │ │ │ Package: r-bioc-glmgampoi │ │ │ Version: 1.18.0+dfsg-2 │ │ │ Architecture: riscv64 │ │ │ Maintainer: Debian R Packages Maintainers │ │ │ Installed-Size: 2263 │ │ │ Depends: r-api-4.0, r-api-bioc-3.20, r-cran-rcpp, r-bioc-delayedmatrixstats, r-cran-matrixstats, r-bioc-matrixgenerics, r-bioc-sparsearray (>= 1.5.21), r-bioc-delayedarray, r-bioc-hdf5array, r-bioc-summarizedexperiment, r-bioc-singlecellexperiment, r-bioc-biocgenerics, r-cran-rlang, r-cran-vctrs, r-cran-rcpparmadillo, r-bioc-beachmat, libblas3 | libblas.so.3, libc6 (>= 2.27), libgcc-s1 (>= 3.0), liblapack3 | liblapack.so.3, libstdc++6 (>= 14) │ │ │ -Suggests: r-cran-testthat (>= 2.1.0), r-cran-zoo, r-bioc-deseq2, r-bioc-edger, r-bioc-limma, r-cran-mass, r-cran-statmod, r-cran-ggplot2, r-cran-bench, r-bioc-biocparallel, r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-scran, r-cran-matrix, r-cran-dplyr │ │ │ +Suggests: r-cran-matrix │ │ │ Section: gnu-r │ │ │ Priority: optional │ │ │ Homepage: https://bioconductor.org/packages/glmGamPoi/ │ │ │ Description: GNU R fit a Gamma-Poisson generalized linear model │ │ │ Fit linear models to overdispersed count data. │ │ │ The package can estimate the overdispersion and fit repeated models │ │ │ for matrix input. It is designed to handle large input datasets as they