Package: bpgmm 1.0.9
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:
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:1727ec38fa. Checks:OK: 1 NOTE: 1. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 04 2024 |
R-4.5-linux-x86_64 | NOTE | Dec 04 2024 |
Exports:CalculateProposalLambdaCalculateProposalPsyEvaluateProposalLambdageneratePriorLambdageneratePriorPsigeneratePriorThetaYpgmmRJMCMCstayMCMCupdatesummerizePgmmRJMCMCtoEthetaYlist
Dependencies:briocallrclicodacodetoolscolorspacecombinatcorrplotcrayondescdiffobjdigestdoParallelellipseevaluatefabMixfansifarverfftwtoolsforeachfsggplot2gluegtablegtoolsisobanditeratorsjsonlitelabel.switchinglabelinglatticelifecyclelpSolvemagrittrMASSMatrixmclustmcmcsemgcvmunsellmvtnormnlmepgmmpillarpkgbuildpkgconfigpkgloadpraiseprocessxpsR6RColorBrewerRcppRcppArmadillorlangrprojrootscalestestthattibbleutf8vctrsviridisLitewaldowithr