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>.

AuthorChris Fraley [aut, cre], Leland Wilkinson [ctb]
Date of publication2016-12-24 11:23:58
MaintainerChris Fraley <cfraley@tableau.com>
LicenseMIT + file LICENSE
Version0.15
https://www.r-project.org, https://www.cs.uic.edu/~wilkinson/Publications/outliers.pdf

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Files

HDoutliers
HDoutliers/inst
HDoutliers/inst/doc
HDoutliers/inst/doc/HDoutliers.pdf
HDoutliers/NAMESPACE
HDoutliers/CHANGELOG
HDoutliers/data
HDoutliers/data/dots.rda
HDoutliers/data/ex2D.rda
HDoutliers/R
HDoutliers/R/getHDmembers.R HDoutliers/R/HDoutliers.R HDoutliers/R/plotHDoutliers.R HDoutliers/R/getHDoutliers.R
HDoutliers/MD5
HDoutliers/DESCRIPTION
HDoutliers/man
HDoutliers/man/getHDoutliers.Rd HDoutliers/man/ex2D.Rd HDoutliers/man/getHDmembers.Rd HDoutliers/man/dots.Rd HDoutliers/man/plotHDoutliers.Rd HDoutliers/man/HDoutliers.Rd
HDoutliers/LICENSE

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