Package: ContaminatedMixt 1.3.8

Angelo Mazza

ContaminatedMixt: Clustering and Classification with the Contaminated Normal

Fits mixtures of multivariate contaminated normal distributions (with eigen-decomposed scale matrices) via the expectation conditional- maximization algorithm under a clustering or classification paradigm Methods are described in Antonio Punzo, Angelo Mazza, and Paul D McNicholas (2018) <doi:10.18637/jss.v085.i10>.

Authors:Antonio Punzo, Angelo Mazza, Paul D. McNicholas

ContaminatedMixt_1.3.8.tar.gz
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ContaminatedMixt.pdf |ContaminatedMixt.html✨
ContaminatedMixt/json (API)

# Install 'ContaminatedMixt' in R:
install.packages('ContaminatedMixt', repos = 'https://cloud.r-project.org')
Uses libs:
  • openblas– Optimized BLAS
Datasets:
  • wine - Wine Data Set

On CRAN:

Conda:

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

openblas

1.48 score 1 packages 384 downloads 17 exports 83 dependencies

Last updated 2 years agofrom:130631abc1. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 27 2025
R-4.5-linux-x86_64OKMar 27 2025
R-4.4-linux-x86_64OKMar 27 2025

Exports:agreeCNmixtCNmixtCVCNpredictdCNgetBestModelgetClustergetCVgetDetectiongetICgetPargetPosteriorgetSizem.steprCNwhichBestwhichBestCV

Dependencies:BHcaretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdplyre1071fansifarverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmclustmgcvmixturemnormtModelMetricsmunsellmvtnormnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcppRcppArmadilloRcppGSLrecipesreshape2rlangrpartscalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr

Citation

To cite ContaminatedMixt in publications use:

Punzo A, Mazza A, McNicholas PD (2018). “ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions.” Journal of Statistical Software, 85(10), 1–25. doi:10.18637/jss.v085.i10.

Corresponding BibTeX entry: 

  @Article{,
    title = {{ContaminatedMixt}: An {R} Package for Fitting
      Parsimonious Mixtures of Multivariate Contaminated Normal
      Distributions},
    author = {Antonio Punzo and Angelo Mazza and Paul D. McNicholas},
    journal = {Journal of Statistical Software},
    year = {2018},
    volume = {85},
    number = {10},
    pages = {1--25},
    doi = {10.18637/jss.v085.i10},
  }