[r-cran-recipes] 02/03: Upload to new

Andreas Tille tille at debian.org
Mon Dec 4 20:29:56 UTC 2017


This is an automated email from the git hooks/post-receive script.

tille pushed a commit to branch master
in repository r-cran-recipes.

commit 60b4db3f2b0ebee9324f3ae62284da19d8bf625c
Author: Andreas Tille <tille at debian.org>
Date:   Mon Dec 4 19:15:38 2017 +0100

    Upload to new
---
 debian/changelog |  6 +++---
 debian/control   | 14 +++++++-------
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/debian/changelog b/debian/changelog
index cad0ed7..29c4efa 100644
--- a/debian/changelog
+++ b/debian/changelog
@@ -1,5 +1,5 @@
-r-cran-recipes (0.1.0-1) UNRELEASED; urgency=medium
+r-cran-recipes (0.1.0-1) unstable; urgency=medium
 
-  * Initial release (closes: #xxxxxx)
+  * Initial release (closes: #883510)
 
- -- Andreas Tille <tille at debian.org>  Sun, 22 Oct 2017 20:52:53 +0200
+ -- Andreas Tille <tille at debian.org>  Mon, 04 Dec 2017 19:15:25 +0100
diff --git a/debian/control b/debian/control
index 89093d6..b4b9bc9 100644
--- a/debian/control
+++ b/debian/control
@@ -31,10 +31,10 @@ Depends: ${R:Depends},
          ${misc:Depends}
 Recommends: ${R:Recommends}
 Suggests: ${R:Suggests}
-Description: Preprocessing Tools to Create Design Matrices
- An extensible framework to create and preprocess
- design matrices. Recipes consist of one or more data manipulation
- and analysis "steps". Statistical parameters for the steps can
- be estimated from an initial data set and then applied to
- other data sets. The resulting design matrices can then be used
- as inputs into statistical or machine learning models.
+Description: GNU R preprocessing tools to create design matrices
+ This GNU R package provides an extensible framework to create and
+ preprocess design matrices. Recipes consist of one or more data
+ manipulation and analysis "steps". Statistical parameters for the steps
+ can be estimated from an initial data set and then applied to other data
+ sets. The resulting design matrices can then be used as inputs into
+ statistical or machine learning models.

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