R package BayesLCA: Bayesian Latent Class Analysis. Bayesian Latent Class Analysis using several different methods. The BayesLCA package for R provides tools for performing latent class analysis within a Bayesian setting. Three methods for fitting the model are provided, incorporating an expectation-maximization algorithm, Gibbs sampling and a variational Bayes approximation. The article briefly outlines the methodology behind each of these techniques and discusses some of the technical difficulties associated with them. Methods to remedy these problems are also described. Visualization methods for each of these techniques are included, as well as criteria to aid model selection.
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References in zbMATH (referenced in 1 article )
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- White, Arthur; Wyse, Jason; Murphy, Thomas Brendan: Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler (2016)