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) <https://jmlr.org/papers/v18/16-382.html>.

Version: 2.0
Imports: stats, utils, graphics, grDevices, mvtnorm, parallel, foreach, doParallel, robustbase, mclust
Published: 2021-05-29
Author: Pietro Coretto [aut, cre] (Homepage: <https://pietro-coretto.github.io>), Christian Hennig [aut] (Homepage: <https://www.unibo.it/sitoweb/christian.hennig/en>)
Maintainer: Pietro Coretto <pcoretto at unisa.it>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: otrimle citation info
Materials: NEWS
In views: Robust
CRAN checks: otrimle results

Documentation:

Reference manual: otrimle.pdf

Downloads:

Package source: otrimle_2.0.tar.gz
Windows binaries: r-devel: otrimle_2.0.zip, r-release: otrimle_2.0.zip, r-oldrel: otrimle_2.0.zip
macOS binaries: r-release (arm64): otrimle_2.0.tgz, r-release (x86_64): otrimle_2.0.tgz, r-oldrel: otrimle_2.0.tgz
Old sources: otrimle archive

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