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

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10 exports 0.00 score 65 dependencies 271 downloads

Last updated 2 years agofrom:1727ec38fa. Checks:OK: 1 NOTE: 8. Indexed: yes.

Exports:CalculateProposalLambdaCalculateProposalPsyEvaluateProposalLambdageneratePriorLambdageneratePriorPsigeneratePriorThetaYpgmmRJMCMCstayMCMCupdatesummerizePgmmRJMCMCtoEthetaYlist

Dependencies:briocallrclicodacodetoolscolorspacecombinatcorrplotcrayondescdiffobjdigestdoParallelellipseevaluatefabMixfansifarverfftwtoolsforeachfsggplot2gluegtablegtoolsisobanditeratorsjsonlitelabel.switchinglabelinglatticelifecyclelpSolvemagrittrMASSMatrixmclustmcmcsemgcvmunsellmvtnormnlmepgmmpillarpkgbuildpkgconfigpkgloadpraiseprocessxpsR6RColorBrewerRcppRcppArmadillorematch2rlangrprojrootscalestestthattibbleutf8vctrsviridisLitewaldowithr