BayesLCA : An R Package for Bayesian Latent Class Analysis

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Title: BayesLCA : An R Package for Bayesian Latent Class Analysis
Authors: White, Arthur
Murphy, Thomas Brendan
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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 learningStatisticsLatent class analysisEM algorithmGibbs samplingVariational BayesModel-based clusteringR
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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