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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_1.3.1.zip, r-release: bpgmm_1.3.1.zip, r-oldrel: bpgmm_1.3.1.zip
macOS binaries: r-release (arm64): bpgmm_1.3.1.tgz, r-oldrel (arm64): bpgmm_1.3.1.tgz, r-release (x86_64): bpgmm_1.3.1.tgz, r-oldrel (x86_64): bpgmm_1.3.1.tgz
Old sources: bpgmm archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=bpgmm to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.