Package: bpgmm
Type: Package
Title: Bayesian Model Selection Approach for Parsimonious Gaussian
        Mixture Models
Version: 1.1.1
Date: 2025-10-30
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)
Authors@R: c(
    person(given = "Yaoxiang", family = "Li",  role = c("aut","cre"),email = "yl814@georgetown.edu"),
    person(given = "Xiang",  family = "Lu",    role = "aut"),
    person(given = "Tanzy",  family = "Love",  role = "aut"))
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>.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.2
Suggests: testthat
LinkingTo: Rcpp, RcppArmadillo
NeedsCompilation: yes
Packaged: 2025-10-30 15:15:50 UTC; Bach
Author: Yaoxiang Li [aut, cre],
  Xiang Lu [aut],
  Tanzy Love [aut]
Repository: CRAN
Date/Publication: 2025-10-30 15:40:02 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-25 18:40:30 UTC; unix
Archs: bpgmm.so.dSYM
