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01/02: gnu: Add r-ropls.

From: guix-commits
Subject: 01/02: gnu: Add r-ropls.
Date: Mon, 10 Jun 2019 07:42:13 -0400 (EDT)

rekado pushed a commit to branch master
in repository guix.

commit a9fac3f4d3e8fb579314afc6d22add1394f2fe7f
Author: Ricardo Wurmus <address@hidden>
Date:   Mon Jun 10 10:58:30 2019 +0200

    gnu: Add r-ropls.
    * gnu/packages/bioconductor.scm (r-ropls): New variable.
 gnu/packages/bioconductor.scm | 36 ++++++++++++++++++++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/gnu/packages/bioconductor.scm b/gnu/packages/bioconductor.scm
index c1bbcdc..f8bcb8e 100644
--- a/gnu/packages/bioconductor.scm
+++ b/gnu/packages/bioconductor.scm
@@ -4606,3 +4606,39 @@ expression data to predict switches in regulatory 
activity between two
 conditions.  A Bayesian network is used to model the regulatory structure and
 Markov-Chain-Monte-Carlo is applied to sample the activity states.")
     (license license:gpl2+)))
+(define-public r-ropls
+  (package
+    (name "r-ropls")
+    (version "1.16.0")
+    (source
+     (origin
+       (method url-fetch)
+       (uri (bioconductor-uri "ropls" version))
+       (sha256
+        (base32
+         "099nv9dgmw3avkxv7cd27r16yj56svjlp5q4i389yp1n0r5zhyl2"))))
+    (build-system r-build-system)
+    (propagated-inputs `(("r-biobase" ,r-biobase)))
+    (native-inputs
+     `(("r-knitr" ,r-knitr))) ; for vignettes
+    (home-page "";)
+    (synopsis "Multivariate analysis and feature selection of omics data")
+    (description
+     "Latent variable modeling with @dfn{Principal Component Analysis} (PCA)
+and @dfn{Partial Least Squares} (PLS) are powerful methods for visualization,
+regression, classification, and feature selection of omics data where the
+number of variables exceeds the number of samples and with multicollinearity
+among variables.  @dfn{Orthogonal Partial Least Squares} (OPLS) enables to
+separately model the variation correlated (predictive) to the factor of
+interest and the uncorrelated (orthogonal) variation.  While performing
+similarly to PLS, OPLS facilitates interpretation.
+This package provides imlementations of PCA, PLS, and OPLS for multivariate
+analysis and feature selection of omics data.  In addition to scores, loadings
+and weights plots, the package provides metrics and graphics to determine the
+optimal number of components (e.g. with the R2 and Q2 coefficients), check the
+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)))

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