bpgmm: Bayesian Model Selection Approach for Parsimonious Gaussian Mixture Models

Model-based clustering using Bayesian parsimonious Gaussian mixture models. MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection. GREEN et al. (1995) <doi:10.1093/biomet/82.4.711>.

Version:	1.3.1
Depends:	R (≥ 3.1.0)
Imports:	methods (≥ 3.5.1), mcmcse (≥ 1.3-2), pgmm (≥ 1.2.3), mvtnorm (≥ 1.0-10), MASS (≥ 7.3-51.1), parallel, Rcpp (≥ 1.0.1), gtools (≥ 3.8.1), label.switching (≥ 1.8), fabMix (≥ 5.0), mclust (≥ 5.4.3)
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	knitr, rmarkdown, testthat
Published:	2026-05-28
DOI:	10.32614/CRAN.package.bpgmm
Author:	Yaoxiang Li [aut, cre], Xiang Lu [aut], Tanzy Love [aut]
Maintainer:	Yaoxiang Li <liyaoxiang at outlook.com>
BugReports:	https://github.com/YaoxiangLi/bpgmm/issues
License:	GPL-3
URL:	https://github.com/YaoxiangLi/bpgmm, https://yaoxiangli.github.io/bpgmm/, https://doi.org/10.1007/s00357-021-09391-8
NeedsCompilation:	yes
Citation:	bpgmm citation info
Materials:	README, NEWS
CRAN checks:	bpgmm results

Documentation:

Reference manual:	bpgmm.html , bpgmm.pdf
Vignettes:	Preparing data and choosing sampler settings (source, R code) Worked examples (source, R code) Getting started with bpgmm (source, R code) Model and sampler details (source, R code) Model selection on a larger simulated MFA data set (source, R code) Posterior diagnostics and multiple chains (source, R code) Exploratory variable prioritization after bpgmm clustering (source, R code)

Downloads:

Package source:	bpgmm_1.3.1.tar.gz
Windows binaries:	r-devel: bpgmm_1.1.1.zip, r-release: bpgmm_1.1.1.zip, r-oldrel: bpgmm_1.1.1.zip
macOS binaries:	r-release (arm64): bpgmm_1.1.1.tgz, r-oldrel (arm64): bpgmm_1.1.1.tgz, r-release (x86_64): bpgmm_1.1.1.tgz, r-oldrel (x86_64): bpgmm_1.1.1.tgz
Old sources:	bpgmm archive

Linking:

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