Finite Mixture Modeling, Clustering & Classification

Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac or circular von Mises parametric families.


Reference manual

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2.12.0 by Marko Nagode, 5 months ago

Browse source code at

Authors: Marko Nagode [aut, cre] , Branislav Panic [ctb] , Jernej Klemenc [ctb] , Simon Oman [ctb]

Documentation:   PDF Manual  

Task views: Cluster Analysis & Finite Mixture Models

GPL (>= 2) license

Imports methods, stats, utils, graphics, grDevices, mvtnorm

Suggests flexmix, mclust, mixtools

See at CRAN