Robust Model Based Clustering

A robust clustering algorithm (Model-Based) similar to Expectation Maximization for finite mixture normal distributions is implemented, its main advantage is that the estimator is resistant to outliers, that means that results of parameter estimation are still correct when there are atypical values in the sample (see Gonzalez, Maronna, Yohai and Zamar (2021) ).


Reference manual

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0.1.0 by Juan Domingo Gonzalez, 3 months ago

Browse source code at

Authors: Juan Domingo Gonzalez [cre, aut] , Victor J. Yohai [aut] , Ruben H. Zamar [aut] , Ricardo Maronna [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports ktaucenters, mvtnorm, MASS

Depends on stats

Suggests tclust, knitr, testthat, rmarkdown

See at CRAN