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author | Mădălin Ionel Patrașcu <madalinionel.patrascu@mdc-berlin.de> | 2022-12-07 06:18:34 +0100 |
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committer | Ricardo Wurmus <rekado@elephly.net> | 2022-12-12 16:21:50 +0100 |
commit | 5154e76a2e6d492419cdbc8338367138a38c4836 (patch) | |
tree | ca778d51e08095bf1287edf50966cc6be339373c | |
parent | 48e6e6799078a084d637a336067db9cd5f6b2b05 (diff) | |
download | guix-5154e76a2e6d492419cdbc8338367138a38c4836.tar guix-5154e76a2e6d492419cdbc8338367138a38c4836.tar.gz |
gnu: Add r-gunifrac.
* gnu/packages/cran.scm (r-gunifrac): New variable.
-rw-r--r-- | gnu/packages/cran.scm | 50 |
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") |