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) , Browne and McNicholas (2014) , Browne and McNicholas (2015) .


Reference manual

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2.0.4 by Paul D. McNicholas, 9 months ago

Browse source code at https://github.com/cran/mixture

Authors: Nik Pocuca [aut] , Ryan P. Browne [aut] , Paul D. McNicholas [aut, cre]

Documentation:   PDF Manual  

Task views: Cluster Analysis & Finite Mixture Models, Missing Data

GPL (>= 2) license

Imports Rcpp

Depends on lattice

Linking to Rcpp, RcppArmadillo, BH, RcppGSL

System requirements: GNU GSL

Imported by Compositional, ContaminatedMixt, MixGHD, pmcgd.

See at CRAN