In its simplest form, the LCA Stata Plugin allows the user to fit a latent class model by specifying a Stata data set, the number of latent classes, the items measuring the latent variable, and the number of response categories for each item. Multiple-groups LCA can be run using the GROUPs option; users can examine measurement invariance across groups by adding the measurement option. Additional parameter restrictions can be provided as well.
Continuous and categorical covariates can be included in the COVariates option in order to examine the relation between each covariate and the probability of latent class membership. Prediction can be modeled using a baseline-category multinomial logit model or a binary logit model.
A Bayesian stabilizing prior can be invoked when sparseness is an issue for parameter estimation. Random starting values can be generated by the program, or the user can provide starting values.
An empirical demonstration of PROC LCA (which includes the same functionality) appeared in Structural Equation Modeling:
Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14(4), 671-694. PMCID: PMC2785099 View article here.
The download package includes examples that you can run in the LCA Stata Plugin.