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bpgmm (version 1.0.5)

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

169

Version

1.0.5

License

GPL-3

Maintainer

Yaoxiang Li

Last Published

August 2nd, 2019

Functions in bpgmm (1.0.5)

sumerizeZ

sumerizeZ
CalculateProposalPsy

CalculateProposalPsy
generatePriorLambda

generatePriorLambda
evaluatePriorPsi

evaluatePriorPsi
updatePostThetaY

Update posterior theta Y list
CalculateProposalLambda

CalculateProposalLambda
listToStrVec

Convert list of string to vector of string
likelihood

likelihood
evaluatePrior

evaluate Prior
generatePriorThetaY

PriorThetaY list
evaluatePriorLambda

evaluatePriorLambda
generatePriorPsi

generatePriorPsi
EvaluateProposalLambda

EvaluateProposalLambda
calculateRatio

Log scale ratio calculation
EvaluateProposalPsy

EvaluateProposalPsy
Hparam-class

An S4 class to represent a Hyper parameter.
calculateVarList

calculateVarList
parsimoniousGaussianMixtureModel

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.
ThetaYList

ThetaYList-class
getmode

getmode
getZmat

Tool for vector to matrix