Package: bpgmm 1.0.9

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:Xiang Lu <Xiang_Lu at urmc.rochester.edu>, Yaoxiang Li <yl814 at georgetown.edu>, Tanzy Love <tanzy_love at urmc.rochester.edu>

bpgmm_1.0.9.tar.gz
bpgmm_1.0.9.tar.gz(r-4.5-noble)bpgmm_1.0.9.tar.gz(r-4.4-noble)
bpgmm_1.0.9.tgz(r-4.4-emscripten)bpgmm_1.0.9.tgz(r-4.3-emscripten)
bpgmm.pdf |bpgmm.html✨
bpgmm/json (API)

# Install 'bpgmm' in R:
install.packages('bpgmm', repos = 'https://cloud.r-project.org')
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

1.00 score 268 downloads 10 exports 64 dependencies

Last updated 3 years agofrom:1727ec38fa. Checks:1 OK, 2 NOTE. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 04 2025
R-4.5-linux-x86_64NOTEMar 04 2025
R-4.4-linux-x86_64NOTEMar 04 2025

Exports:CalculateProposalLambdaCalculateProposalPsyEvaluateProposalLambdageneratePriorLambdageneratePriorPsigeneratePriorThetaYpgmmRJMCMCstayMCMCupdatesummerizePgmmRJMCMCtoEthetaYlist

Dependencies:briocallrclicodacodetoolscolorspacecombinatcorrplotcrayondescdiffobjdigestdoParallelellipseevaluatefabMixfansifarverfftwtoolsforeachfsggplot2gluegtablegtoolsisobanditeratorsjsonlitelabel.switchinglabelinglatticelifecyclelpSolvemagrittrMASSMatrixmclustmcmcsemgcvmunsellmvtnormnlmepgmmpillarpkgbuildpkgconfigpkgloadpraiseprocessxpsR6RColorBrewerRcppRcppArmadillorlangrprojrootscalestestthattibbleutf8vctrsviridisLitewaldowithr

Citation

To cite package ‘bpgmm’ in publications use:

Lu X, Li Y, Love T (2022). bpgmm: Bayesian Model Selection Approach for Parsimonious Gaussian Mixture Models. R package version 1.0.9, https://CRAN.R-project.org/package=bpgmm.

ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.

Corresponding BibTeX entry: 

  @Manual{,
    title = {bpgmm: Bayesian Model Selection Approach for Parsimonious
      Gaussian Mixture Models},
    author = {Xiang Lu and Yaoxiang Li and Tanzy Love},
    year = {2022},
    note = {R package version 1.0.9},
    url = {https://CRAN.R-project.org/package=bpgmm},
  }