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. See <https://www.cs.uic.edu/~wilkinson/Publications/outliers.pdf>.

Version: 0.14
Depends: R (≥ 3.1.0), FNN, FactoMineR
Published: 2016-11-28
Author: Chris Fraley [aut, cre], Leland Wilkinson [ctb]
Maintainer: Chris Fraley <cfraley at tableau.com>
License: MIT + file LICENSE
URL: https://www.r-project.org, https://www.cs.uic.edu/~wilkinson/Publications/outliers.pdf
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: HDoutliers results

Downloads:

Reference manual: HDoutliers.pdf
Package source: HDoutliers_0.14.tar.gz
Windows binaries: r-devel: HDoutliers_0.14.zip, r-release: HDoutliers_0.14.zip, r-oldrel: HDoutliers_0.14.zip
OS X Mavericks binaries: r-release: HDoutliers_0.14.tgz, r-oldrel: HDoutliers_0.14.tgz
Old sources: HDoutliers archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=HDoutliers to link to this page.