This package is considered a duplicate. The official version of this package is found at:https://yaoxiangli.r-universe.dev/bpgmm

Package: bpgmm 1.3.1

Yaoxiang Li

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:Yaoxiang Li [aut, cre], Xiang Lu [aut], Tanzy Love [aut]

bpgmm_1.3.1.tar.gz
bpgmm_1.3.1.tar.gz(r-4.7-arm64)bpgmm_1.3.1.tar.gz(r-4.7-x86_64)bpgmm_1.3.1.tar.gz(r-4.6-arm64)bpgmm_1.3.1.tar.gz(r-4.6-x86_64)
bpgmm_1.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
bpgmm/json (API)
NEWS

# Install 'bpgmm' in R:

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

Bug tracker:https://github.com/yaoxiangli/bpgmm/issues

Pkgdown/docs site:https://yaoxiangli.github.io

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On CRAN:

openblas cpp

3.85 score 10 scripts 211 downloads 5 exports 55 dependencies

Last updated from:f3ae6fa17c. Checks:6 OK. Indexed: no.

Target	Result	Time
linux-devel-arm64	OK	192
linux-devel-x86_64	OK	186
source / vignettes	OK	286
linux-release-arm64	OK	215
linux-release-x86_64	OK	192
wasm-release	OK	155

Exports:constraint_to_model model_to_constraint pgmm_rjmcmc pgmm_rjmcmc_chains summarize_pgmm_rjmcmc

Dependencies:brio callr cli coda codetools combinat corrplot cpp11 crayon desc diffobj doParallel ellipse evaluate fabMix farver fftwtools foreach fs ggplot2 glue gtable gtools isoband iterators jsonlite label.switching labeling lattice lifecycle lpSolve magrittr MASS mclust mcmcse mvtnorm pgmm pkgbuild pkgload praise processx ps R6 RColorBrewer Rcpp RcppArmadillo rlang rprojroot S7 scales testthat vctrs viridisLite waldo withr