BayesLCA : An R Package for Bayesian Latent Class Analysis
|Title:||BayesLCA : An R Package for Bayesian Latent Class Analysis||Authors:||White, Arthur
Murphy, Thomas Brendan
|Permanent link:||http://hdl.handle.net/10197/8214||Date:||25-Nov-2014||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.||Type of material:||Journal Article||Publisher:||Foundation for Open Access Statistics||Keywords:||Machine learning; Statistics; Latent class analysis; EM algorithm; Gibbs sampling; Variational Bayes; Model-based clustering; R||DOI:||10.18637/jss.v061.i13||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mathematics and Statistics Research Collection|
Insight Research Collection
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