mixture: Mixture Models for Clustering and Classification
An implementation of 14 parsimonious mixture models for model-based clustering or model-based classification. Gaussian, Student's t, generalized hyperbolic, variance-gamma or skew-t mixtures are available. All approaches work with missing data. Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>, Browne and McNicholas (2014) <doi:10.1007/s11634-013-0139-1>, Browne and McNicholas (2015) <doi:10.1002/cjs.11246>.
| Version: |
2.0.4 |
| Depends: |
R (≥ 3.5.0), lattice (≥ 0.20) |
| Imports: |
Rcpp (≥ 1.0.2) |
| LinkingTo: |
Rcpp, RcppArmadillo, BH, RcppGSL |
| Published: |
2021-04-19 |
| Author: |
Nik Pocuca [aut],
Ryan P. Browne [aut],
Paul D. McNicholas [aut, cre] |
| Maintainer: |
Paul D. McNicholas <mcnicholas at math.mcmaster.ca> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| SystemRequirements: |
GNU GSL |
| Materials: |
ChangeLog |
| In views: |
Cluster, MissingData |
| CRAN checks: |
mixture results |
Documentation:
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