Package: bpgmm
Type: Package
Title: Bayesian Model Selection Approach for Parsimonious Gaussian
        Mixture Models
Version: 1.0.9
Date: 2022-05-31
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)
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@georgetown.edu>
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) <doi:10.1093/biomet/82.4.711>.
SystemRequirements: C++11
License: GPL-3
Encoding: UTF-8
RoxygenNote: 6.1.1
Suggests: testthat
LinkingTo: Rcpp, RcppArmadillo
NeedsCompilation: yes
Packaged: 2022-06-01 03:33:44 UTC; bach
Repository: CRAN
Date/Publication: 2022-06-01 10:20:12 UTC
Built: R 4.4.0; x86_64-apple-darwin20; 2024-01-22 14:01:34 UTC; unix
Archs: bpgmm.so.dSYM
