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.
- Version2.0
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- otrimle citation info
- Last release05/29/2021
Documentation
Team
Pietro Coretto
MaintainerShow author detailsChristian Hennig
Show author detailsRolesAuthor
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports5 packages
- Reverse Imports1 package