metamicrobiomeR: Microbiome Data Analysis & Meta-Analysis with GAMLSS-BEZI & Random Effects

Generalized Additive Model for Location, Scale and Shape (GAMLSS) with zero inflated beta (BEZI) family for analysis of microbiome relative abundance data (with various options for data transformation/normalization to address compositional effects) and random effects meta-analysis models for meta-analysis pooling estimates across microbiome studies are implemented. Random Forest model to predict microbiome age based on relative abundances of shared bacterial genera with the Bangladesh data (Subramanian et al 2014), comparison of multiple diversity indexes using linear/linear mixed effect models and some data display/visualization are also implemented. The reference paper is published by Ho NT, Li F, Wang S, Kuhn L (2019) <doi:10.1186/s12859-019-2744-2> .

Version: 1.2
Depends: R (≥ 4.0.0), gamlss
Imports: meta, lme4, gdata, plyr, dplyr, tidyr, ggplot2, gridExtra, lmerTest, matrixStats, zCompositions, compositions
Suggests: testthat, RCurl, httr, repmis, jsonlite, knitr, rmarkdown, grDevices, gplots, magrittr, tools, foreign, mgcv, reshape2, caret, randomForest, tsibble, RColorBrewer
Published: 2020-11-09
Author: Nhan Ho [aut, cre]
Maintainer: Nhan Ho <nhanhocumc at>
License: GPL-2
NeedsCompilation: no
CRAN checks: metamicrobiomeR results


Reference manual: metamicrobiomeR.pdf
Vignettes: metamicrobiomeR
Package source: metamicrobiomeR_1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel: not available
macOS binaries: r-release: metamicrobiomeR_1.2.tgz, r-oldrel: not available
Old sources: metamicrobiomeR archive


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