Statistical Methods for Microbiome Compositional Data R package MicrobiomeStat version 1.4 CRAN Task View: CompositionalData (https://CRAN.R-project.org/view=CompositionalData) A suite of methods for powerful and robust microbiome data analysis addressing zero-inflation, phylogenetic structure and compositional effects. Includes the LinDA method for differential abundance analysis (Zhou et al. (2022)<doi:10.1186/s13059-022-02655-5>), the BMDD (Bimodal Dirichlet Distribution) method for accurate modeling and imputation of zero-inflated microbiome sequencing data (Zhou et al. (2025)<doi:10.1371/journal.pcbi.1013124>) and compositional sparse CCA methods for microbiome multi-omics data integration (Deng et al. (2024) <doi: 10.3389/fgene.2024.1489694>). Software Depends: R (>= 3.5.0) Imports: ggplot2, matrixStats, parallel, stats, utils, Matrix, statmod, MASS, ggrepel, lmerTest, foreach, modeest, dplyr, Rcpp, mlr3, mlr3mbo, bbotk, paradox LinkingTo: Rcpp, RcppArmadillo Suggests: DiceKriging, randomForest Jun Chen <chen.jun2@mayo.edu> Comprehensive R Archive Network (CRAN) Xianyang Zhang [aut], Jun Chen [aut, cre], Huijuan Zhou [ctb], Linsui Deng [ctb] GPL-3 2026-03-03 application/tgz https://CRAN.R-project.org/package=MicrobiomeStat doi:10.32614/CRAN.package.MicrobiomeStat