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:Pietro Coretto [aut, cre], Christian Hennig [aut]

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 = 'https://cloud.r-project.org')
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.48 score 1 stars 1 packages 438 downloads 14 exports 8 dependencies

Last updated 4 years agofrom:14c0d74625. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 08 2025
R-4.5-linuxOKMar 08 2025
R-4.4-linuxOKMar 08 2025

Exports:generator.otrimleInitClustkerndensclusterkerndensmeasurekerndenspkmeanfunksdfunotrimleotrimlegotrimlesimgplot.summary.otrimlesimgdensprint.summary.otrimlesimgdensrimlesummary.otrimlesimgdens

Dependencies:codetoolsDEoptimRdoParallelforeachiteratorsmclustmvtnormrobustbase

Citation

To cite package 'otrimle' in publications use:

P. Coretto and C. Hennig (2021). otrimle: Robust Model-Based Clustering. R package version 2.0url: https://CRAN.R-project.org/package=otrimle

P. Coretto and C. Hennig (2016). Robust improper maximum likelihood: tuning, computation, and a comparison with other methods for robust Gaussian clustering. Journal of the American Statistical Association, Vol. 111(516), pp. 1648-1659.<doi:10.1080/01621459.2015.1100996>

P. Coretto and C. Hennig (2017). Consistency, breakdown robustness, and algorithms for robust improper maximum likelihood clustering. Journal of Machine Learning Research, Vol. 18(142), pp. 1-39

Corresponding BibTeX entries: 

  @Manual{,
    title = {otrimle: Robust Model-Based Clustering},
    author = {Pietro Coretto and Christian Hennig},
    year = {2021},
    note = {R package version 2.0},
  }

  @Article{,
    title = {Robust improper maximum likelihood: tuning, computation,
      and a comparison with other methods for robust Gaussian
      clustering},
    author = {Pietro Coretto and Christian Hennig},
    year = {2016},
    journal = {Journal of the American Statistical Association},
    volume = {111},
    number = {516},
    pages = {1648--1659},
    doi = {10.1080/01621459.2015.1100996},
  }

  @Article{,
    title = {Consistency, breakdown robustness, and algorithms for
      robust improper maximum likelihood clustering},
    author = {Pietro Coretto and Christian Hennig},
    year = {2016},
    journal = {Journal of Machine Learning Research},
    volume = {18},
    number = {142},
    pages = {1--39},
    url = {https://jmlr.org/papers/v18/16-382.html},
  }