BayesLCA: An R Package for Bayesian Latent Class Analysis

BayesLCA: An R Package for Bayesian Latent Class Analysis

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 meth...

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Main Authors: Arthur White, Thomas Brendan Murphy
Format: Article
Language:English
Published: Foundation for Open Access Statistics 2014-11-01
Series:Journal of Statistical Software
Online Access:http://www.jstatsoft.org/index.php/jss/article/view/2201
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spelling doaj-023334d5d6a8457298d269f98ddb50ac2020-11-24T23:20:35ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602014-11-0161112810.18637/jss.v061.i13805BayesLCA: An R Package for Bayesian Latent Class AnalysisArthur WhiteThomas Brendan MurphyThe 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.http://www.jstatsoft.org/index.php/jss/article/view/2201
collection DOAJ
language English
format Article
sources DOAJ
author Arthur White
Thomas Brendan Murphy
spellingShingle Arthur White
Thomas Brendan Murphy
BayesLCA: An R Package for Bayesian Latent Class Analysis
Journal of Statistical Software
author_facet Arthur White
Thomas Brendan Murphy
author_sort Arthur White
title BayesLCA: An R Package for Bayesian Latent Class Analysis
title_short BayesLCA: An R Package for Bayesian Latent Class Analysis
title_full BayesLCA: An R Package for Bayesian Latent Class Analysis
title_fullStr BayesLCA: An R Package for Bayesian Latent Class Analysis
title_full_unstemmed BayesLCA: An R Package for Bayesian Latent Class Analysis
title_sort bayeslca: an r package for bayesian latent class analysis
publisher Foundation for Open Access Statistics
series Journal of Statistical Software
issn 1548-7660
publishDate 2014-11-01
description 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.
url http://www.jstatsoft.org/index.php/jss/article/view/2201
work_keys_str_mv AT arthurwhite bayeslcaanrpackageforbayesianlatentclassanalysis
AT thomasbrendanmurphy bayeslcaanrpackageforbayesianlatentclassanalysis
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