bpgmm (version 1.1.1)

Bayesian Model Selection Approach for Parsimonious Gaussian Mixture Models

Description

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) .

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Version

Install

install.packages('bpgmm')

Monthly Downloads

232

Version

1.1.1

License

GPL-3

Maintainer

Yaoxiang Li

Last Published

October 30th, 2025

Functions in bpgmm (1.1.1)

pgmmRJMCMC

bpgmm Model-Based Clustering Using Baysian PGMM Carries out model-based clustering using parsimonious Gaussian mixture models. MCMC are used for parameter estimation. The RJMCMC is used for model selection.

generatePriorPsi

ThetaYList

ThetaYList-class

CalculateProposalLambda

generatePriorThetaY

PriorThetaY list

Hparam-class

An S4 class to represent a Hyper parameter.

EvaluateProposalLambda