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.
Keywords for this software
References in zbMATH (referenced in 8 articles , 1 standard article )
Showing results 1 to 8 of 8.
- Giordani, Paolo; Ferraro, Maria Brigida; Martella, Francesca: An introduction to clustering with R (2020)
- Jeffrey Pullin, Lyle Gurrin, Damjan Vukcevic: Rater: An R Package for Fitting Statistical Models of Repeated Categorical Ratings (2020) arXiv
- O’Hagan, Adrian; White, Arthur: Improved model-based clustering performance using Bayesian initialization averaging (2019)
- Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
- Ye, Mao; Zhang, Peng; Nie, Lizhen: Clustering sparse binary data with hierarchical Bayesian Bernoulli mixture model (2018)
- Ken Beath: randomLCA: An R Package for Latent Class with Random Effects Analysis (2017) not zbMATH
- White, Arthur; Wyse, Jason; Murphy, Thomas Brendan: Bayesian variable selection for latent class analysis using a collapsed Gibbs sampler (2016)
- Arthur White; Thomas Murphy: BayesLCA: An R Package for Bayesian Latent Class Analysis (2014) not zbMATH