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: 2.0.5
Depends: R (≥ 3.5.0), lattice (≥ 0.20)
Imports: Rcpp (≥ 1.0.2), methods
LinkingTo: Rcpp, RcppArmadillo, BH, RcppGSL
Published: 2022-09-23
Author: Nik Pocuca ORCID iD [aut], Ryan P. Browne ORCID iD [aut], Paul D. McNicholas ORCID iD [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:

Reference manual: mixture.pdf

Downloads:

Package source: mixture_2.0.5.tar.gz
Windows binaries: r-devel: mixture_2.0.5.zip, r-release: mixture_2.0.5.zip, r-oldrel: mixture_2.0.5.zip
macOS binaries: r-release (arm64): mixture_2.0.5.tgz, r-oldrel (arm64): mixture_2.0.5.tgz, r-release (x86_64): mixture_2.0.5.tgz, r-oldrel (x86_64): mixture_2.0.5.tgz
Old sources: mixture archive

Reverse dependencies:

Reverse imports: Compositional, ContaminatedMixt, MixGHD

Linking:

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