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BayesRGMM  

Bayesian Robust Generalized Mixed Models for Longitudinal Data
View on CRAN: Click here


Download and install BayesRGMM package within the R console
Install from CRAN:
install.packages("BayesRGMM")

Install from Github:
library("remotes")
install_github("cran/BayesRGMM")

Install by package version:
library("remotes")
install_version("BayesRGMM", "2.2")



Attach the package and use:
library("BayesRGMM")
Maintained by
Kuo-Jung Lee
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-03-31
Latest Update: 2022-05-10
Description:
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 .
How to cite:
Kuo-Jung Lee (2021). BayesRGMM: Bayesian Robust Generalized Mixed Models for Longitudinal Data. R package version 2.2, https://cran.r-project.org/web/packages/BayesRGMM. Accessed 06 Mar. 2026.
Previous versions and publish date:
1.0 (2021-03-31 11:40), 1.1 (2021-05-27 08:50), 2.0 (2021-11-27 13:30), 2.1 (2021-12-20 00:32), 2.2 (2022-05-10 14:30)
Other packages that cited BayesRGMM R package
View BayesRGMM citation profile
Other R packages that BayesRGMM depends, imports, suggests or enhances
Complete documentation for BayesRGMM
Functions, R codes and Examples using the BayesRGMM R package
Some associated functions: AR1.cor . BayesCumulativeProbitHSD . BayesRobustProbit . BayesRobustProbitSummary . CorrMat.HSD . GSPS . SimulatedDataGenerator.CumulativeProbit . SimulatedDataGenerator . 
Some associated R codes: RcppExports.R .  Full BayesRGMM package functions and examples
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