@@ -667,6 +667,50 @@ (define-public r-guix-install
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, 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: (1) PERMANOVA using the Freedman-Lane permutation scheme,
+(2) PERMANOVA omnibus test using multiple matrices, and (3) analytical approach
+to approximating PERMANOVA p-value. 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")