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 = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

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

openblascppopenmp

1.00 score 190 downloads 10 exports 64 dependencies

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

TargetResultDate
Doc / VignettesOKDec 04 2024
R-4.5-linux-x86_64NOTEDec 04 2024

Exports:CalculateProposalLambdaCalculateProposalPsyEvaluateProposalLambdageneratePriorLambdageneratePriorPsigeneratePriorThetaYpgmmRJMCMCstayMCMCupdatesummerizePgmmRJMCMCtoEthetaYlist

Dependencies:briocallrclicodacodetoolscolorspacecombinatcorrplotcrayondescdiffobjdigestdoParallelellipseevaluatefabMixfansifarverfftwtoolsforeachfsggplot2gluegtablegtoolsisobanditeratorsjsonlitelabel.switchinglabelinglatticelifecyclelpSolvemagrittrMASSMatrixmclustmcmcsemgcvmunsellmvtnormnlmepgmmpillarpkgbuildpkgconfigpkgloadpraiseprocessxpsR6RColorBrewerRcppRcppArmadillorlangrprojrootscalestestthattibbleutf8vctrsviridisLitewaldowithr