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>.


Reference manual

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1.0 by Chris Fraley, 2 years ago

https://www.r-project.org, https://www.cs.uic.edu/~wilkinson/Publications/outliers.pdf

Browse source code at https://github.com/cran/HDoutliers

Authors: Chris Fraley [aut, cre] , Leland Wilkinson [ctb]

Documentation:   PDF Manual  

MIT + file LICENSE license

Depends on FNN, FactoMineR, mclust

Imported by OutliersO3.

See at CRAN