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 |
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