gnu: Add r-gunifrac.
* gnu/packages/cran.scm (r-gunifrac): New variable.
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@ -667,6 +667,56 @@ from CRAN, but also from Bioconductor or even arbitrary git or mercurial
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repositories, replacing the need for installation via @code{devtools}.")
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(license license:gpl3+)))
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(define-public r-gunifrac
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(package
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(name "r-gunifrac")
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(version "1.7")
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(source (origin
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(method url-fetch)
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(uri (cran-uri "GUniFrac" version))
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(sha256
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(base32
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"13qb5fw9km6p5x8li9x3liqbh833wf2v73npj8jl3msplzfk82vp"))))
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(properties `((upstream-name . "GUniFrac")))
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(build-system r-build-system)
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(propagated-inputs
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(list r-ape
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r-dirmult
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r-foreach
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r-ggplot2
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r-ggrepel
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r-mass
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r-matrix
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r-matrixstats
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r-modeest
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r-rcpp
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r-rmutil
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r-statmod
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r-vegan))
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(native-inputs (list r-knitr))
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(home-page "https://cran.r-project.org/package=GUniFrac")
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(synopsis
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"Generalized UniFrac distances and methods for microbiome data analysis")
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(description
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"This package provides a suite of methods for powerful and robust
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microbiome data analysis, including data normalization, data simulation,
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community-level association testing and differential abundance analysis. It
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implements generalized UniFrac distances, @dfn{Geometric Mean of Pairwise
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Ratios} (GMPR) normalization, semiparametric data simulator, distance-based
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statistical methods, and feature- based statistical methods. The
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distance-based statistical methods include three extensions of PERMANOVA:
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@itemize
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@item PERMANOVA using the Freedman-Lane permutation scheme,
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@item PERMANOVA omnibus test using multiple matrices, and
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@item analytical approach to approximating PERMANOVA p-value.
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@end itemize
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Feature-based statistical methods include linear model-based methods for
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differential abundance analysis of zero-inflated high-dimensional
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compositional data.")
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(license license:gpl3)))
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(define-public r-ids
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(package
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(name "r-ids")
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