Learn R Programming

BayesRGMM (version 2.2)

Bayesian Robust Generalized Mixed Models for Longitudinal Data

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 .

Copy Link

Version

Install

install.packages('BayesRGMM')

Monthly Downloads

107

Version

2.2

License

GPL-2

Maintainer

Kuo-Jung Lee

Last Published

May 10th, 2022

Functions in BayesRGMM (2.2)

BayesRobustProbitSummary

To summarizes model estimation outcomes
GSPS

The German socioeconomic panel study data
BayesCumulativeProbitHSD

Perform MCMC algorithm to generate the posterior samples for longitudinal ordinal data
SimulatedDataGenerator

Generate simulated data with either ARMA or MCD correlation structures.
SimulatedDataGenerator.CumulativeProbit

Simulating a longitudinal ordinal data with HSD correlation structures.
CorrMat.HSD

To compute the correlation matrix in terms of hypersphere decomposition approach
BayesRobustProbit

Perform MCMC algorithm to generate the posterior samples
AR1.cor

AR(1) correlation matrix