Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Arthur White, Thomas Brendan Murphy
Title: BayesLCA: An R Package for Bayesian Latent Class Analysis
Abstract: 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.

Page views:: 6518. Submitted: 2012-06-13. Published: 2014-11-25.
Paper: BayesLCA: An R Package for Bayesian Latent Class Analysis     Download PDF (Downloads: 12095)
Supplements:
BayesLCA_1.6.tar.gz: R source package Download (Downloads: 301; 29KB)
v61i13.R: R example code from the paper Download (Downloads: 432; 3KB)
DOI: 10.18637/jss.v061.i13

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.