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 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: 1.0
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: 2021-03-31
Author: Kuo-Jung Lee ORCID iD [aut, cre], 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

Downloads:

Reference manual: BayesRGMM.pdf
Vignettes: Bayesian Robust Generalized Mixed Models for Longitudinal Data
Package source: BayesRGMM_1.0.tar.gz
Windows binaries: r-devel: BayesRGMM_1.0.zip, r-release: BayesRGMM_1.0.zip, r-oldrel: BayesRGMM_1.0.zip
macOS binaries: r-release: BayesRGMM_1.0.tgz, r-oldrel: BayesRGMM_1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=BayesRGMM to link to this page.