ICSOutlier-package: Outlier Detection Using Invariant Coordinate Selection

ICSOutlier-packageR Documentation

Outlier Detection Using Invariant Coordinate Selection

Description

Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components.

Details

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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 ics2 object. The current implementation targets data sets with only a small percentage of outliers but future extensions are under preparation.

Index: This package was not yet installed at build time.

Author(s)

Klaus Nordhausen [aut, cre] (<https://orcid.org/0000-0002-3758-8501>), Aurore Archimbaud [aut] (<https://orcid.org/0000-0002-6511-9091>), Anne Ruiz-Gazen [aut] (<https://orcid.org/0000-0001-8970-8061>)

Maintainer: Klaus Nordhausen <klausnordhausenR@gmail.com>

References

Archimbaud, A., Nordhausen, K. and Ruiz-Gazen, A. (2018), ICS for multivariate outlier detection with application to quality control. Computational Statistics & Data Analysis, 128:184-199. <doi:10.1016/j.csda.2018.06.011>.

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


ICSOutlier documentation built on Sept. 18, 2023, 5:07 p.m.