Robust Model-Based Clustering

Performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform distribution covering the whole Euclidean space. Parameters are estimated by (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2016) , and Coretto and Hennig (2017) < http://jmlr.org/papers/v18/16-382.html>.


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install.packages("otrimle")

1.3 by Pietro Coretto, 5 months ago


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


Authors: Pietro Coretto [aut, cre] , Christian Hennig [aut]


Documentation:   PDF Manual  


Task views: Robust Statistical Methods


GPL (>= 2) license


Imports stats, utils, graphics, grDevices, mclust, parallel, foreach, doParallel


See at CRAN