glca: An R package for multiple-group latent class analysis (LCA)
The R package glca deals with the fixed-effect LCA and the random-effect LCA: the former can be applied in situations where populations are segmented by the observed group itself, whereas the latter can be used when there are too many levels in the group variable to make meaningful group comparisons.
R packages for latent variable models with categorical data
You can download an all-inclusive package for latent class analysis (LCA), latent transition analysis (LTA), and latent class-profile analysis (LCPA) here. This package fits a variety of latent variable models, including:
LCA, LTA, and LCPA with multiple subgroups
LTA and LCPA with multiple latent classes (i.e. latent subgroups)
LCA with covariates to predict latent class membership
LTA with covariates to predict latent class membership at the initial time and transition between adjacent time points
LCPA with covariates to predict latent class-profile membership
LCA, LTA, and LCPA with flexible parameter restrictions
Estimation algorithms
Maximum-likelihood estimation using EM algorithm
Automatically use different sets of starting values to assure of global maximum
Bayesian estimation using Markov chain Monte Carlo (MCMC) algorithm
Optional dynamic data-dependent prior
Time-series plots for model parameters
Download the package (written in R software)
Source files for maximum-likelihood estimation (CAT_LVM, Version 0.9.0 alpha):Â
Source files for Bayesian estimation (CAT_LVM_BAYESIAN, Version 0.9.0 alpha):
Source files for the data generator (GNR_DATA, Version 0.9.0 alpha):
Pre-loaded R Workspace (CAT_LVM, CAT_LVM_BAYESIAN, and GNR_DATA, Version 0.9.0 alpha):
User's guide and examples
Maximum-likelihood estimation (CATLVM)
Bayesian estimation (CATLVM_MCMC)
Please send questions or comments about this package here.