Package: BayesRGMM 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>.

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

BayesRGMM_2.2.tar.gz
BayesRGMM_2.2.tar.gz(r-4.5-noble)BayesRGMM_2.2.tar.gz(r-4.4-noble)
BayesRGMM_2.2.tgz(r-4.4-emscripten)BayesRGMM_2.2.tgz(r-4.3-emscripten)
BayesRGMM.pdf |BayesRGMM.html✨
BayesRGMM/json (API)

# Install 'BayesRGMM' in R:
install.packages('BayesRGMM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • GSPS - The German socioeconomic panel study data

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

2.30 score 20 scripts 227 downloads 7 exports 28 dependencies

Last updated 3 years agofrom:63df167ec3. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 18 2024
R-4.5-linux-x86_64NOTEDec 18 2024

Exports:AR1.corBayesCumulativeProbitHSDBayesRobustProbitBayesRobustProbitSummaryCorrMat.HSDSimulatedDataGeneratorSimulatedDataGenerator.CumulativeProbit

Dependencies:abindbatchmeanscliexpmfansigenericsgluelatticelifecyclemagrittrMASSMatrixmsmmvtnormpillarpkgconfigplyrrbibutilsRcppRcppArmadilloRcppDistRdpackreshaperlangsurvivaltibbleutf8vctrs

Bayesian Robust Generalized Mixed Models for Longitudinal Data

Rendered fromBayesRGMM.Rnwusingutils::Sweaveon Dec 18 2024.

Last update: 2022-05-10
Started: 2021-03-31