HDoutliers (1.0)

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Leland Wilkinson's Algorithm for Detecting Multidimensional Outliers.

https://www.r-project.org
https://www.cs.uic.edu/~wilkinson/Publications/outliers.pdf
http://cran.r-project.org/web/packages/HDoutliers

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 .

Maintainer: Chris Fraley
Author(s): Chris Fraley [aut, cre], Leland Wilkinson [ctb]

License: MIT + file LICENSE

Uses: FactoMineR, FNN, mclust

Released about 1 year ago.


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