Package: bpgmm 1.3.1

Yaoxiang Li

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_1.3.1.tar.gz
bpgmm_1.3.1.zip(r-4.7-x86_64)bpgmm_1.3.1.zip(r-4.7-arm64)bpgmm_1.3.1.zip(r-4.6-x86_64)bpgmm_1.3.1.zip(r-4.6-arm64)
bpgmm_1.3.1.tar.gz(r-4.7-arm64)bpgmm_1.3.1.tar.gz(r-4.7-x86_64)bpgmm_1.3.1.tar.gz(r-4.6-arm64)bpgmm_1.3.1.tar.gz(r-4.6-x86_64)
bpgmm_1.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bpgmm/json (API)

# Install 'bpgmm' in R:
install.packages('bpgmm', repos = c('https://cran.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:

Conda:

openblascpp

4.32 score 30 scripts 526 downloads 5 exports 56 dependencies

Last updated from:f3ae6fa17c. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK194
linux-devel-x86_64OK225
source / vignettesOK297
linux-release-arm64OK146
linux-release-x86_64OK220
windows-devel-arm64OK259
windows-devel-x86_64OK161
windows-release-arm64OK252
windows-release-x86_64OK185
wasm-releaseOK144

Exports:constraint_to_modelmodel_to_constraintpgmm_rjmcmcpgmm_rjmcmc_chainssummarize_pgmm_rjmcmc

Dependencies:briocallrclicodacodetoolscombinatcorrplotcpp11crayondescdiffobjdoParallelellipseevaluatefabMixfarverfftwtoolsforeachfsggplot2gluegtablegtoolsisobanditeratorsjsonlitelabel.switchinglabelinglatticelifecyclelpSolvemagrittrMASSmclustmcmcsemvtnormotelpgmmpkgbuildpkgloadpraiseprocessxpsR6RColorBrewerRcppRcppArmadillorlangrprojrootS7scalestestthatvctrsviridisLitewaldowithr

Exploratory variable prioritization after bpgmm clustering
Simulate data with informative and weak variables | Fit a short clustering chain | Rank variables by posterior allocation separation | Compare with loading magnitudes | Interpretation | Suggested reporting language
Rendered fromvariable-prioritization.Rmdusingknitr::rmarkdown

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

Getting started with bpgmm
Fit a model | Summarize posterior samples | Common early mistakes | Naming convention | Citation
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

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

Model and sampler details
Observation model | PGMM covariance constraints | Priors and posterior updates | RJMCMC moves | Interface mapping | Citation
Rendered frommodel-and-sampler.Rmdusingknitr::rmarkdown

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

Model selection on a larger simulated MFA data set
Simulate clustered factor-analytic data | Run RJMCMC model selection | Compare true and posterior allocations | Interpreting short versus long runs
Rendered frommodel-selection.Rmdusingknitr::rmarkdown

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

Posterior diagnostics and multiple chains
Simulate a diagnostic example | Run independent chains | Summarize each chain | Trace cluster counts and covariance models | Posterior co-clustering matrix | What to look for
Rendered fromposterior-diagnostics.Rmdusingknitr::rmarkdown

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

Preparing data and choosing sampler settings
Matrix orientation | Check finite numeric inputs | Scale variables | Choose q_new | Choose m_range | Choose the starting covariance model | A prepared call | Practical checklist
Rendered fromdata-preparation.Rmdusingknitr::rmarkdown

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

Worked examples
Simulate a small clustering problem | Fit a fixed-model chain | Summarize posterior samples | Inspect component parameters | Scale the example to real data | What to report
Rendered fromexamples.Rmdusingknitr::rmarkdown

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