ICSOutlier-package: Outlier Detection Using Invariant Coordinate Selection

In ICSOutlier: Outlier Detection Using Invariant Coordinate Selection

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. 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.

Details

The DESCRIPTION file: This package was not yet installed at build time.
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 <[e-mail]>

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. ISSN 0167-9473. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.32614/RJ-2018-034")}.

ICSOutlier documentation built on May 29, 2024, 2:08 a.m.