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)
- Version2.0
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- otrimle citation info
- Last release05/29/2021
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Team
Pietro Coretto
Christian Hennig
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- Imports9 packages
- Reverse Imports1 package