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: 2022-05-10
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

Documentation:

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

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
Old sources: BayesRGMM archive

Linking:

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