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: 1.0.5
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)
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat
Published: 2019-08-02
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_1.0.5.tar.gz
Windows binaries: r-devel: bpgmm_1.0.5.zip, r-devel-gcc8: bpgmm_1.0.5.zip, r-release: bpgmm_1.0.5.zip, r-oldrel: bpgmm_1.0.5.zip
OS X binaries: r-release: bpgmm_1.0.5.tgz, r-oldrel: bpgmm_1.0.5.tgz


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