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 <>.

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: 2022-05-10
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>
License: GPL-2
NeedsCompilation: yes
CRAN checks: BayesRGMM results


Reference manual: BayesRGMM.pdf
Vignettes: Bayesian Robust Generalized Mixed Models for Longitudinal Data


Package source: BayesRGMM_2.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


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