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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.0 |
rolling linux/jammy R-4.5 | otrimle_2.0.tar.gz |
184.9 KiB |
2.0 |
rolling linux/noble R-4.5 | otrimle_2.0.tar.gz |
184.8 KiB |
2.0 |
rolling source/ R- | otrimle_2.0.tar.gz |
84.0 KiB |
2.0 |
latest linux/jammy R-4.5 | otrimle_2.0.tar.gz |
184.9 KiB |
2.0 |
latest linux/noble R-4.5 | otrimle_2.0.tar.gz |
184.8 KiB |
2.0 |
latest source/ R- | otrimle_2.0.tar.gz |
84.0 KiB |
2.0 |
2026-04-26 source/ R- | otrimle_2.0.tar.gz |
84.0 KiB |
2.0 |
2026-04-23 source/ R- | otrimle_2.0.tar.gz |
84.0 KiB |
2.0 |
2026-04-09 windows/windows R-4.5 | otrimle_2.0.zip |
187.8 KiB |
2.0 |
2025-04-20 source/ R- | otrimle_2.0.tar.gz |
84.0 KiB |