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 |
Documentation:
Downloads:
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