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>.


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install.packages("HDoutliers")

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