Package: otrimle 2.0
Pietro Coretto
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>.
Authors:
otrimle_2.0.tar.gz
otrimle_2.0.tar.gz(r-4.5-noble)otrimle_2.0.tar.gz(r-4.4-noble)
otrimle_2.0.tgz(r-4.4-emscripten)otrimle_2.0.tgz(r-4.3-emscripten)
otrimle.pdf |otrimle.html✨
otrimle/json (API)
NEWS
| # Install 'otrimle' in R: |
| install.packages('otrimle', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- banknote - Swiss Banknotes Data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:14c0d74625. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 09 2024 |
| R-4.5-linux | OK | Sep 09 2024 |
Exports:generator.otrimleInitClustkerndensclusterkerndensmeasurekerndenspkmeanfunksdfunotrimleotrimlegotrimlesimgplot.summary.otrimlesimgdensprint.summary.otrimlesimgdensrimlesummary.otrimlesimgdens
Dependencies:codetoolsDEoptimRdoParallelforeachiteratorsmclustmvtnormrobustbase
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Swiss Banknotes Data | banknote |
| Generates random data from OTRIMLE output model | generator.otrimle |
| Robust Initialization for Model-based Clustering Methods | InitClust |
| Aggregated distance to elliptical unimodal density over clusters | kerndenscluster |
| Statistic measuring closeness to symmetric unimodal distribution | kerndensmeasure |
| Closeness of multivariate distribution to elliptical unimodal distribution | kerndensp |
| Mean and standard deviation of unimodality statistic | kmeanfun ksdfun |
| Optimally Tuned Robust Improper Maximum Likelihood Clustering | otrimle print.otrimle |
| OTRIMLE for a range of numbers of clusters with density-based cluster quality statistic | otrimleg |
| Adequacy approach for number of clusters for OTRIMLE | otrimlesimg plot.summary.otrimlesimgdens print.summary.otrimlesimgdens summary.otrimlesimgdens |
| Plot Methods for OTRIMLE Objects | plot.otrimle |
| Plot Methods for RIMLE Objects | plot.rimle |
| Robust Improper Maximum Likelihood Clustering | print.rimle rimle |