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02/02: gnu: Add r-biosigner.

From: guix-commits
Subject: 02/02: gnu: Add r-biosigner.
Date: Mon, 10 Jun 2019 07:42:14 -0400 (EDT)

rekado pushed a commit to branch master
in repository guix.

commit 075a90946b93eefeb30996978dd293147aaeff94
Author: Ricardo Wurmus <address@hidden>
Date:   Mon Jun 10 10:58:39 2019 +0200

    gnu: Add r-biosigner.
    * gnu/packages/bioconductor.scm (r-biosigner): New variable.
 gnu/packages/bioconductor.scm | 35 +++++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)

diff --git a/gnu/packages/bioconductor.scm b/gnu/packages/bioconductor.scm
index f8bcb8e..ff15963 100644
--- a/gnu/packages/bioconductor.scm
+++ b/gnu/packages/bioconductor.scm
@@ -4642,3 +4642,38 @@ validity of the model by permutation testing, detect 
outliers, and perform
 feature selection (e.g. with Variable Importance in Projection or regression
     (license license:cecill)))
+(define-public r-biosigner
+  (package
+    (name "r-biosigner")
+    (version "1.12.0")
+    (source
+     (origin
+       (method url-fetch)
+       (uri (bioconductor-uri "biosigner" version))
+       (sha256
+        (base32
+         "1643iya40v6whb7lw7y34w5sanbasvj4yhvcygbip667yhphyv5b"))))
+    (build-system r-build-system)
+    (propagated-inputs
+     `(("r-biobase" ,r-biobase)
+       ("r-e1071" ,r-e1071)
+       ("r-randomforest" ,r-randomforest)
+       ("r-ropls" ,r-ropls)))
+    (native-inputs
+     `(("r-knitr" ,r-knitr)
+       ("r-rmarkdown" ,r-rmarkdown)
+       ("pandoc" ,ghc-pandoc)
+       ("pandoc-citeproc" ,ghc-pandoc-citeproc))) ; all for vignettes
+    (home-page "";)
+    (synopsis "Signature discovery from omics data")
+    (description
+     "Feature selection is critical in omics data analysis to extract
+restricted and meaningful molecular signatures from complex and high-dimension
+data, and to build robust classifiers.  This package implements a method to
+assess the relevance of the variables for the prediction performances of the
+classifier.  The approach can be run in parallel with the PLS-DA, Random
+Forest, and SVM binary classifiers.  The signatures and the corresponding
+'restricted' models are returned, enabling future predictions on new
+    (license license:cecill)))

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