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GUniFrac: Generalized UniFrac Distances, Distance-Based Multivariate Methods and Feature-Based Univariate Methods for Microbiome Data Analysis

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.

Version: 1.8
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 0.12.13), vegan, ggplot2, matrixStats, Matrix, ape, parallel, stats, utils, statmod, rmutil, dirmult, MASS, ggrepel, foreach, modeest, inline, methods
LinkingTo: Rcpp
Suggests: ade4, knitr, markdown, ggpubr
Published: 2023-09-14
DOI: 10.32614/CRAN.package.GUniFrac
Author: Jun Chen, Xianyang Zhang, Lu Yang, Lujun Zhang
Maintainer: Jun Chen <chen.jun2 at mayo.edu>
License: GPL-3
NeedsCompilation: yes
In views: Phylogenetics
CRAN checks: GUniFrac results

Documentation:

Reference manual: GUniFrac.pdf
Vignettes: Performing differential abundance analysis using ZicoSeq

Downloads:

Package source: GUniFrac_1.8.tar.gz
Windows binaries: r-devel: GUniFrac_1.8.zip, r-release: GUniFrac_1.8.zip, r-oldrel: GUniFrac_1.8.zip
macOS binaries: r-release (arm64): GUniFrac_1.8.tgz, r-oldrel (arm64): GUniFrac_1.8.tgz, r-release (x86_64): GUniFrac_1.8.tgz, r-oldrel (x86_64): GUniFrac_1.8.tgz
Old sources: GUniFrac archive

Reverse dependencies:

Reverse imports: animalcules, chemodiv, LDM, mecoturn, MiRKAT, RPANDA
Reverse suggests: microeco

Linking:

Please use the canonical form https://CRAN.R-project.org/package=GUniFrac to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.