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authorMădălin Ionel Patrașcu <madalinionel.patrascu@mdc-berlin.de>2022-12-07 06:18:34 +0100
committerRicardo Wurmus <rekado@elephly.net>2022-12-12 16:21:50 +0100
commit5154e76a2e6d492419cdbc8338367138a38c4836 (patch)
treeca778d51e08095bf1287edf50966cc6be339373c
parent48e6e6799078a084d637a336067db9cd5f6b2b05 (diff)
downloadguix-5154e76a2e6d492419cdbc8338367138a38c4836.tar
guix-5154e76a2e6d492419cdbc8338367138a38c4836.tar.gz
gnu: Add r-gunifrac.
* gnu/packages/cran.scm (r-gunifrac): New variable.
-rw-r--r--gnu/packages/cran.scm50
1 files changed, 50 insertions, 0 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm
index 613f569cc0..eefdc16b4c 100644
--- a/gnu/packages/cran.scm
+++ b/gnu/packages/cran.scm
@@ -667,6 +667,56 @@ from CRAN, but also from Bioconductor or even arbitrary git or mercurial
repositories, replacing the need for installation via @code{devtools}.")
(license license:gpl3+)))
+(define-public r-gunifrac
+ (package
+ (name "r-gunifrac")
+ (version "1.7")
+ (source (origin
+ (method url-fetch)
+ (uri (cran-uri "GUniFrac" version))
+ (sha256
+ (base32
+ "13qb5fw9km6p5x8li9x3liqbh833wf2v73npj8jl3msplzfk82vp"))))
+ (properties `((upstream-name . "GUniFrac")))
+ (build-system r-build-system)
+ (propagated-inputs
+ (list r-ape
+ r-dirmult
+ r-foreach
+ r-ggplot2
+ r-ggrepel
+ r-mass
+ r-matrix
+ r-matrixstats
+ r-modeest
+ r-rcpp
+ r-rmutil
+ r-statmod
+ r-vegan))
+ (native-inputs (list r-knitr))
+ (home-page "https://cran.r-project.org/package=GUniFrac")
+ (synopsis
+ "Generalized UniFrac distances and methods for microbiome data analysis")
+ (description
+ "This package provides a suite of methods for powerful and robust
+microbiome data analysis, including data normalization, data simulation,
+community-level association testing and differential abundance analysis. It
+implements generalized UniFrac distances, @dfn{Geometric Mean of Pairwise
+Ratios} (GMPR) normalization, semiparametric data simulator, distance-based
+statistical methods, and feature- based statistical methods. The
+distance-based statistical methods include three extensions of PERMANOVA:
+
+@itemize
+@item PERMANOVA using the Freedman-Lane permutation scheme,
+@item PERMANOVA omnibus test using multiple matrices, and
+@item analytical approach to approximating PERMANOVA p-value.
+@end itemize
+
+Feature-based statistical methods include linear model-based methods for
+differential abundance analysis of zero-inflated high-dimensional
+compositional data.")
+ (license license:gpl3)))
+
(define-public r-ids
(package
(name "r-ids")