otrimle: 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 (2015) <https://arxiv.org/abs/1406.0808>, and Coretto and Hennig (2016) <https://arxiv.org/abs/1309.6895>.

AuthorPietro Coretto [aut, cre], Christian Hennig [aut]
Date of publication2016-11-30 14:55:24
MaintainerPietro Coretto <pcoretto@unisa.it>
LicenseGPL (>= 2)
Version0.4

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