Package: ICSOutlier 0.4-0

Klaus Nordhausen

ICSOutlier: Outlier Detection Using Invariant Coordinate Selection

Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components. ICS is a general multivariate technique with many applications in multivariate analysis. ICSOutlier offers a selection of functions for automated detection of outliers in the data based on a fitted ICS object or by specifying the dataset and the scatters of interest. The current implementation targets data sets with only a small percentage of outliers.

Authors:Klaus Nordhausen [aut, cre], Aurore Archimbaud [aut], Anne Ruiz-Gazen [aut]

ICSOutlier_0.4-0.tar.gz
ICSOutlier_0.4-0.tar.gz(r-4.5-noble)ICSOutlier_0.4-0.tar.gz(r-4.4-noble)
ICSOutlier_0.4-0.tgz(r-4.4-emscripten)ICSOutlier_0.4-0.tgz(r-4.3-emscripten)
ICSOutlier.pdf |ICSOutlier.html✨
ICSOutlier/json (API)
NEWS

# Install 'ICSOutlier' in R:
install.packages('ICSOutlier', repos = 'https://cloud.r-project.org')
Datasets:
  • HTP - Production Measurements of High-Tech Parts - Full Rank Case
  • HTP2 - Production Measurements of High-Tech Parts - Singular Case
  • HTP3 - Production Measurements of High-Tech Parts - Nearly Singular Case

On CRAN:

Conda:

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

1.56 score 1 packages 1.2k downloads 10 exports 13 dependencies

Last updated 1 years agofrom:1c884b07e4. Checks:2 OK, 1 NOTE. Indexed: yes.

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

Exports:comp_norm_testcomp_simu_testcomp.norm.testcomp.simu.testdist_simu_testdist.simu.testics_distancesICS_outlierics.distancesics.outlier

Dependencies:DBIICSlatticeMatrixminqamitoolsmomentsmvtnormnumDerivRcppRcppArmadillosurveysurvival

Citation

To cite ICSOutlier in publications use:

Archimbaud A, Nordhausen K, Ruiz-Gazen A (2018). “ICSOutlier: Unsupervised Outlier Detection for Low-Dimensional Contamination Structure.” The R Journal, 10(1), 234–250. doi:10.32614/RJ-2018-034.

Corresponding BibTeX entry: 

  @Article{,
    title = {{ICSOutlier}: Unsupervised Outlier Detection for
      Low-Dimensional Contamination Structure},
    author = {Aurore Archimbaud and Klaus Nordhausen and Anne
      Ruiz-Gazen},
    year = {2018},
    journal = {The R Journal},
    volume = {10},
    number = {1},
    pages = {234--250},
    doi = {10.32614/RJ-2018-034},
  }