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 (2016) <doi:10.1080/01621459.2015.1100996>, and Coretto and Hennig (2017) <http://jmlr.org/papers/v18/16-382.html>.

Getting started

Package details

AuthorPietro Coretto [aut, cre], Christian Hennig [aut]
MaintainerPietro Coretto <pcoretto@unisa.it>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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otrimle documentation built on Sept. 24, 2019, 5:06 p.m.