Bayesian Model Selection Approach for Parsimonious Gaussian Mixture Models R package bpgmm version 1.3.1 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>. Software 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 Yaoxiang Li <liyaoxiang@outlook.com> Comprehensive R Archive Network (CRAN) Yaoxiang Li [aut, cre], Xiang Lu [aut], Tanzy Love [aut] GPL-3 2026-05-28 application/tgz https://CRAN.R-project.org/package=bpgmm doi:10.32614/CRAN.package.bpgmm