bpgmm (version 1.0.9)

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

209

Version

1.0.9

License

GPL-3

Maintainer

Yaoxiang Li

Last Published

June 1st, 2022

Functions in bpgmm (1.0.9)

Hparam-class

An S4 class to represent a Hyper parameter.

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.

Tool for vector to matrix

CalculateProposalLambda

EvaluateProposalLambda

Convert list of string to vector of string

getIndThetaY

toNEthetaYlist

calculateRatio

Log scale ratio calculation

changeConstraintFormat

updatePostThetaY

Update posterior theta Y list

calculateVarList

clearCurrentThetaYlist

sumerizeZ

summerizePgmmRJMCMC

updatePostZ