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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