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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bpgmm.pdf |bpgmm.html✨
bpgmm/json (API)

# Install 'bpgmm' in R:

install.packages('bpgmm', repos = c('https://yaoxiangli.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 231 downloads 10 exports 64 dependencies

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

Target	Result	Date
Doc / Vignettes	OK	Nov 12 2024
R-4.5-win-x86_64	NOTE	Nov 12 2024
R-4.5-linux-x86_64	NOTE	Nov 12 2024
R-4.4-win-x86_64	NOTE	Nov 12 2024
R-4.4-mac-x86_64	NOTE	Nov 12 2024
R-4.4-mac-aarch64	NOTE	Nov 12 2024
R-4.3-win-x86_64	NOTE	Nov 12 2024
R-4.3-mac-x86_64	NOTE	Nov 12 2024
R-4.3-mac-aarch64	NOTE	Nov 12 2024

Exports:CalculateProposalLambda CalculateProposalPsy EvaluateProposalLambda generatePriorLambda generatePriorPsi generatePriorThetaY pgmmRJMCMC stayMCMCupdate summerizePgmmRJMCMC toEthetaYlist

Dependencies:brio callr cli coda codetools colorspace combinat corrplot crayon desc diffobj digest doParallel ellipse evaluate fabMix fansi farver fftwtools foreach fs ggplot2 glue gtable gtools isoband iterators jsonlite label.switching labeling lattice lifecycle lpSolve magrittr MASS Matrix mclust mcmcse mgcv munsell mvtnorm nlme pgmm pillar pkgbuild pkgconfig pkgload praise processx ps R6 RColorBrewer Rcpp RcppArmadillo rlang rprojroot scales testthat tibble utf8 vctrs viridisLite waldo withr

Help page	Topics
CalculateProposalLambda	CalculateProposalLambda
CalculateProposalPsy	CalculateProposalPsy
Log scale ratio calculation	calculateRatio
calculateVarList	calculateVarList
changeConstraintFormat	changeConstraintFormat
clearCurrentThetaYlist	clearCurrentThetaYlist
combineClusterPara	combineClusterPara
evaluate Prior	evaluatePrior
evaluatePriorLambda	evaluatePriorLambda
evaluatePriorPsi	evaluatePriorPsi
EvaluateProposalLambda	EvaluateProposalLambda
EvaluateProposalPsy	EvaluateProposalPsy
generatePriorLambda	generatePriorLambda
generatePriorPsi	generatePriorPsi
PriorThetaY list	generatePriorThetaY
getIndThetaY	getIndThetaY
getmode	getmode
getRemovedIndThetaY	getRemovedIndThetaY
getThetaYWithEmpty	getThetaYWithEmpty
Tool for vector to matrix	getZmat
An S4 class to represent a Hyper parameter.	Hparam-class
likelihood	likelihood
Convert list of string to vector of string	listToStrVec
MstepRJMCMCupdate	MstepRJMCMCupdate
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.	pgmmRJMCMC
stayMCMCupdate	stayMCMCupdate
sumerizeZ	sumerizeZ
summerizePgmmRJMCMC	summerizePgmmRJMCMC
ThetaYList-class	ThetaYList
Title	toEthetaYlist
toNEthetaYlist	toNEthetaYlist
Update posterior theta Y list	updatePostThetaY
updatePostZ	updatePostZ
VstepRJMCMCupdate	VstepRJMCMCupdate

Package: bpgmm 1.0.9

bpgmm: Bayesian Model Selection Approach for Parsimonious Gaussian Mixture Models

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