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>.

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Description file content

Package
HDoutliers
Version
1.0
Date
2018-02-09
Title
Leland Wilkinson's Algorithm for Detecting Multidimensional Outliers
Author
Chris Fraley [aut, cre], Leland Wilkinson [ctb]
Maintainer
Chris Fraley
Depends
R (>= 3.1.0), FNN, FactoMineR, mclust
Description
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 .
License
MIT + file LICENSE
URL
NeedsCompilation
no
Packaged
2018-02-09 20:13:12 UTC; cfraley
Repository
CRAN
Date/Publication
2018-02-11 14:41:07 UTC

install.packages('HDoutliers')

1.0

8 months ago

https://www.r-project.org

Chris Fraley

MIT + file LICENSE

Depends on

R (>= 3.1.0), FNN, FactoMineR, mclust

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