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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>.
Getting started
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Package details |
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| 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@unisa.it> |
| License | GPL (>= 2) |
| Version | 2.0 |
| Package repository | View on CRAN |
| Installation |
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