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>.

Version: 1.0.9
Depends: R (≥ 3.1.0)
Imports: methods (≥ 3.5.1), mcmcse (≥ 1.3-2), pgmm (≥ 1.2.3), mvtnorm (≥ 1.0-10), MASS (≥ 7.3-51.1), Rcpp (≥ 1.0.1), gtools (≥ 3.8.1), label.switching (≥ 1.8), fabMix (≥ 5.0), mclust (≥ 5.4.3)
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat
Published: 2022-06-01
DOI: 10.32614/CRAN.package.bpgmm
Author: Xiang Lu <Xiang_Lu at urmc.rochester.edu>, Yaoxiang Li <yl814 at georgetown.edu>, Tanzy Love <tanzy_love at urmc.rochester.edu>
Maintainer: Yaoxiang Li <yl814 at georgetown.edu>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: C++11
CRAN checks: bpgmm results


Reference manual: bpgmm.pdf


Package source: bpgmm_1.0.9.tar.gz
Windows binaries: r-devel: bpgmm_1.0.9.zip, r-release: bpgmm_1.0.9.zip, r-oldrel: bpgmm_1.0.9.zip
macOS binaries: r-release (arm64): bpgmm_1.0.9.tgz, r-oldrel (arm64): bpgmm_1.0.9.tgz, r-release (x86_64): bpgmm_1.0.9.tgz, r-oldrel (x86_64): bpgmm_1.0.9.tgz
Old sources: bpgmm archive


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