otrimle: Robust Model-Based Clustering (original) (raw)
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 |
| DOI: | 10.32614/CRAN.package.otrimle |
| 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 |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Citation: | otrimle citation info |
| Materials: | |
| In views: | Cluster, Robust |
| CRAN checks: | otrimle results |
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