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: | 2.1.2 |
| Depends: | R (≥ 3.5.0),lattice (≥ 0.20) |
| Imports: | Rcpp (≥ 1.0.2), methods |
| LinkingTo: | Rcpp,RcppArmadillo,BH,RcppGSL |
| Published: | 2025-05-06 |
| DOI: | 10.32614/CRAN.package.mixture |
| Author: | Nik Pocuca [aut], Ryan P. Browne [aut], Paul D. McNicholas [aut, cre], Alexa A. Sochaniwsky [aut] |
| 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 |
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