Bayesian Robust Generalized Mixed Models for Longitudinal Data [R package BayesRGMM version 2.2]

Kuo-Jung Lee

BayesRGMM: Bayesian Robust Generalized Mixed Models for Longitudinal Data

To perform model estimation using MCMC algorithms with Bayesian methods for incomplete longitudinal studies on binary and ordinal outcomes that are measured repeatedly on subjects over time with drop-outs. Details about the method can be found in the vignette or <https://sites.google.com/view/kuojunglee/r-packages/bayesrgmm>.

Version: 2.2

Depends: R (≥ 3.5.0)

Imports: Rcpp (≥ 1.0.1), MASS, batchmeans, abind, reshape, msm, mvtnorm, plyr, Rdpack

LinkingTo: Rcpp, RcppArmadillo, RcppDist

Suggests: testthat

Published: 2022年05月10日

DOI: 10.32614/CRAN.package.BayesRGMM

Author: Kuo-Jung Lee ORCID iD [aut, cre], Hsing-Ming Chang [ctb], Ray-Bing Chen [ctb], Keunbaik Lee [ctb], Chanmin Kim [ctb]

Maintainer: Kuo-Jung Lee <kuojunglee at ncku.edu.tw>

License: GPL-2

URL: https://sites.google.com/view/kuojunglee/r-packages

NeedsCompilation: yes

CRAN checks: BayesRGMM results [issues need fixing before 2025年11月15日]

Documentation:

Reference manual: BayesRGMM.html , BayesRGMM.pdf

Vignettes: Bayesian Robust Generalized Mixed Models for Longitudinal Data (source, R code)

Downloads:

Package source: BayesRGMM_2.2.tar.gz

Windows binaries: r-devel: BayesRGMM_2.2.zip, r-release: BayesRGMM_2.2.zip, r-oldrel: BayesRGMM_2.2.zip

macOS binaries: r-release (arm64): BayesRGMM_2.2.tgz, r-oldrel (arm64): BayesRGMM_2.2.tgz, r-release (x86_64): BayesRGMM_2.2.tgz, r-oldrel (x86_64): BayesRGMM_2.2.tgz

Old sources: BayesRGMM archive

Linking:

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