--- /tmp/r-bioc-bluster-1.0.0+dfsg-2o1ztgwz8/debian/r-bioc-bluster_1.0.0+dfsg-2_amd64.deb
+++ r-bioc-bluster_1.0.0+dfsg-2_amd64.deb
├── file list
│ @@ -1,3 +1,3 @@
│  -rw-r--r--   0        0        0        4 2020-11-24 07:38:24.000000 debian-binary
│ --rw-r--r--   0        0        0     1972 2020-11-24 07:38:24.000000 control.tar.xz
│ +-rw-r--r--   0        0        0     1980 2020-11-24 07:38:24.000000 control.tar.xz
│  -rw-r--r--   0        0        0   286696 2020-11-24 07:38:24.000000 data.tar.xz
├── control.tar.xz
│ ├── control.tar
│ │ ├── file list
│ │ │ @@ -1,3 +1,3 @@
│ │ │  drwxr-xr-x   0 root         (0) root         (0)        0 2020-11-24 07:38:24.000000 ./
│ │ │ --rw-r--r--   0 root         (0) root         (0)      949 2020-11-24 07:38:24.000000 ./control
│ │ │ +-rw-r--r--   0 root         (0) root         (0)      966 2020-11-24 07:38:24.000000 ./control
│ │ │  -rw-r--r--   0 root         (0) root         (0)     3450 2020-11-24 07:38:24.000000 ./md5sums
│ │ ├── ./control
│ │ │ @@ -1,15 +1,15 @@
│ │ │  Package: r-bioc-bluster
│ │ │  Version: 1.0.0+dfsg-2
│ │ │  Architecture: amd64
│ │ │  Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
│ │ │  Installed-Size: 482
│ │ │  Depends: r-base-core (>= 4.0.3-1), r-api-4.0, r-api-bioc-3.12, r-cran-matrix, r-cran-rcpp, r-cran-igraph, r-bioc-s4vectors, r-bioc-biocparallel, r-bioc-biocneighbors, libc6 (>= 2.14), libgcc-s1 (>= 3.0), libstdc++6 (>= 5.2)
│ │ │  Recommends: r-cran-testthat, r-cran-dynamictreecut
│ │ │ -Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-scuttle, r-bioc-scater, r-bioc-scran, r-cran-pheatmap, r-cran-viridis
│ │ │ +Suggests: r-cran-knitr, r-cran-rmarkdown, r-bioc-biocstyle, r-bioc-scrnaseq, r-bioc-scuttle, r-bioc-scater, r-bioc-scran, r-cran-pheatmap, r-cran-viridis
│ │ │  Section: gnu-r
│ │ │  Priority: optional
│ │ │  Homepage: https://bioconductor.org/packages/bluster/
│ │ │  Description: Clustering Algorithms for Bioconductor
│ │ │   Wraps common clustering algorithms in an easily extended S4 framework.
│ │ │   Backends are implemented for hierarchical, k-means and graph-based clustering.
│ │ │   Several utilities are also provided to compare and evaluate clustering results.