HDoutliers: Leland Wilkinson's Algorithm for Detecting Multidimensional Outliers

An implementation of an algorithm for outlier detection that can handle a) data with a mixed categorical and continuous variables, b) many columns of data, c) many rows of data, d) outliers that mask other outliers, and e) both unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, HDoutliers is based on a distributional model that uses probabilities to determine outliers.

Version:	1.0.4
Depends:	R (≥ 3.1.0), FNN, FactoMineR, mclust
Published:	2022-02-11
Author:	Chris Fraley [aut, cre], Leland Wilkinson [ctb]
Maintainer:	Chris Fraley <fraley at u.washington.edu>
License:	MIT + file LICENSE
NeedsCompilation:	no
Materials:	ChangeLog
CRAN checks:	HDoutliers results

Documentation:

Reference manual:

HDoutliers.pdf

Downloads:

Package source:	HDoutliers_1.0.4.tar.gz
Windows binaries:	r-devel: HDoutliers_1.0.4.zip, r-release: HDoutliers_1.0.4.zip, r-oldrel: HDoutliers_1.0.4.zip
macOS binaries:	r-release (arm64): HDoutliers_1.0.4.tgz, r-oldrel (arm64): HDoutliers_1.0.4.tgz, r-release (x86_64): HDoutliers_1.0.4.tgz, r-oldrel (x86_64): HDoutliers_1.0.4.tgz
Old sources:	HDoutliers archive

Reverse dependencies:

Reverse imports:

OutliersO3

Linking:

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