otrimle (1.2)

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) .

Maintainer: Pietro Coretto
Author(s): Pietro Coretto [aut, cre], Christian Hennig [aut]

License: GPL (>= 2)

Uses: doParallel, foreach, mclust

Released 7 months ago.