Package: bpgmm 1.3.4

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>.

Authors:Yaoxiang Li [aut, cre], Xiang Lu [aut], Tanzy Love [aut]

bpgmm_1.3.4.tar.gz
bpgmm_1.3.4.zip(r-4.7)bpgmm_1.3.4.zip(r-4.6)bpgmm_1.3.4.zip(r-4.5)
bpgmm_1.3.4.tgz(r-4.6-x86_64)bpgmm_1.3.4.tgz(r-4.6-arm64)bpgmm_1.3.4.tgz(r-4.5-x86_64)bpgmm_1.3.4.tgz(r-4.5-arm64)
bpgmm_1.3.4.tar.gz(r-4.7-arm64)bpgmm_1.3.4.tar.gz(r-4.7-x86_64)bpgmm_1.3.4.tar.gz(r-4.6-arm64)bpgmm_1.3.4.tar.gz(r-4.6-x86_64)
bpgmm_1.3.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
bpgmm/json (API)
NEWS

# Install 'bpgmm' in R:

install.packages('bpgmm', repos = c('https://yaoxiangli.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/yaoxiangli/bpgmm/issues

Pkgdown/docs site:https://yaoxiangli.github.io

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On CRAN:

armadillo clustering clustering-algorithm cpp machine-learning mcmc rjmcmc openblas cpp

5.02 score 1 stars 10 scripts 207 downloads 5 exports 55 dependencies

Last updated from:7adb261ec2. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	196
linux-devel-x86_64	OK	200
source / vignettes	OK	443
linux-release-arm64	OK	189
linux-release-x86_64	OK	188
macos-release-arm64	OK	155
macos-release-x86_64	OK	472
macos-oldrel-arm64	OK	151
macos-oldrel-x86_64	OK	230
windows-devel	OK	179
windows-release	OK	219
windows-oldrel	OK	171
wasm-release	OK	152

Exports:constraint_to_model model_to_constraint pgmm_rjmcmc pgmm_rjmcmc_chains summarize_pgmm_rjmcmc

Dependencies:brio callr cli coda codetools combinat corrplot cpp11 crayon desc diffobj doParallel ellipse evaluate fabMix farver fftwtools foreach fs ggplot2 glue gtable gtools isoband iterators jsonlite label.switching labeling lattice lifecycle lpSolve magrittr MASS mclust mcmcse mvtnorm pgmm pkgbuild pkgload praise processx ps R6 RColorBrewer Rcpp RcppArmadillo rlang rprojroot S7 scales testthat vctrs viridisLite waldo withr

Exploratory variable prioritization after bpgmm clustering

Rendered fromvariable-prioritization.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2026-05-28
Started: 2026-05-22

Getting started with bpgmm

Rendered fromgetting-started.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2026-05-28
Started: 2026-05-05

Model and sampler details

Rendered frommodel-and-sampler.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2026-05-28
Started: 2026-05-21

Model selection on a larger simulated MFA data set

Rendered frommodel-selection.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2026-05-26
Started: 2026-05-22

Posterior diagnostics and multiple chains

Rendered fromposterior-diagnostics.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2026-05-26
Started: 2026-05-22

Preparing data and choosing sampler settings

Rendered fromdata-preparation.Rmdusingknitr::rmarkdownon May 28 2026.

Last update: 2026-05-28
Started: 2026-05-22

Worked examples

Rendered fromexamples.Rmdusingknitr::rmarkdownon May 28 2026.