README.md

In lockEF/BayesianPGMM: Bayesian Piecewise Growth Mixture Modeling

BayesianPGMM

This is an R package to estimates a Bayesian piecewise growth mixture model with linear segments, for a given number of latent classes and a latent number of possible change points in each class. See [1] for methodological details. This package requires Just Another Gibbs Sampler (JAGS) to be installed on your computer (http://mcmc-jags.sourceforge.net/), and depends on the packages rjags and label.switching.

The BayesianPGMM package can then be installed, directly from GitHub, using the devtools library:

install.packages("devtools")
library(devtools)
install_github("lockEF/BayesianPGMM")

The additional R function BayesBPLMEM.R estimates a bivariate piecewise linear mixed-effects model. This function was developed by Yadira Peralta Torres and is described in [2]. SimDataBPLMEMExample.R loads the required libraries and includes example code to run the BayesPLMEM function on the simulated data in the SimDataBPLMEM.rda file.

The additional R function BayesPCREM.R estimates a piecewise linear mixed-effects model with crossed random effects, developed by Corissa Rohloff [3]. SimDataBPCREM_Example.R loads the required libraries and includes example code to run the function on the data in the SimDataBPCREM.Rdata file.

[1] Lock, E.F., Kohli, N., & Bose, M. (2018). Detecting multiple random changepoints in Bayesian piecewise growth mixture models. Psychometrika, 83 (3): 733-750. Preprint: https://arxiv.org/abs/1710.10626

[2] Peralta Y., Kohli N., Lock E.F., & Davison M.L. Bayesian modeling of associations in bivariate piecewise linear mixed-effects models. Psychological Methods, 2020. https://psycnet.apa.org/doiLanding?doi=10.1037%2Fmet0000358

[3] Rohloff C.T., Kohli N., & Lock E.F. Identifiability and Estimability of Bayesian Linear and Nonlinear Crossed Random Effects Models. In Preparation.

lockEF/BayesianPGMM documentation built on Sept. 30, 2022, 6:53 a.m.