r-bioc-edger 3.2.3~dfsg-1 source package in Ubuntu


r-bioc-edger (3.2.3~dfsg-1) unstable; urgency=low

  [ Charles Plessy ]
  74f1a5f Imported Upstream version 2.6.10~dfsg
  d2d70ab Corrected VCS URLs.
  44c9d4e Regression tests for autopkgtest.

  [ Andreas Tille ]
  * Imported new upstream version 3.2.3~dfsg
  * debian/control
     - Added myself as Uploader
     - Add ${shlibs:Depends} to ensure dependency from libc
     - Removed DM-Upload-Allowed
     - Standards-Version: 3.9.4 (no changes needed)
     - Debhelper 9
     - normalised format
     - Removed unneeded Pre-Depends: dpkg (>= 1.15.6)
     - Removed versioned dependency of r-base-dev
     - Package is "Architecture: any" (rather than all)
  * debian/source/format: Add explicit source format specification
  * debian/copyright:
     - Update Source location
     - s/Removed/Files-Excluded/ to comply with the suggested format
       that might be processed by uscan as alternative method to
     - Add years to Copyright field
  * debian/upstream: inst/CITATION asks for citing this more recent
    paper that is now on top of debian/upstream
  * debian/rules: remove executable flag from installed documentation

 -- Andreas Tille <email address hidden>  Fri, 17 May 2013 14:36:51 +0200

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Uploaded by:
Debian Med on 2013-05-17
Uploaded to:
Original maintainer:
Debian Med
Low Urgency

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File Size SHA-256 Checksum
r-bioc-edger_3.2.3~dfsg-1.dsc 1.4 KiB d1ca91a3c8a83c0e88e23c6fb6cb4788ee6608ad317345961db71741e851f8d7
r-bioc-edger_3.2.3~dfsg.orig.tar.gz 1.2 MiB 85d92d2c9d64db39d5ff5645aa4f6b3f7a38d73e0f59d774dac469f39eaf270d
r-bioc-edger_3.2.3~dfsg-1.debian.tar.gz 4.2 KiB 1a1f129a94629c3196fae23604424f785aec32a73693445b578d3c39ea43e324

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Binary packages built by this source

r-bioc-edger: Empirical analysis of digital gene expression data in R

 Bioconductor package for differential expression analysis of whole
 transcriptome sequencing (RNA-seq) and digital gene expression
 profiles with biological replication. It uses empirical Bayes
 estimation and exact tests based on the negative binomial
 distribution. It is also useful for differential signal analysis with
 other types of genome-scale count data.