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HDoutliers (version 1.0.4)

Leland Wilkinson's Algorithm for Detecting Multidimensional Outliers

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.

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Install

install.packages('HDoutliers')

Monthly Downloads

355

Version

1.0.4

License

MIT + file LICENSE

Last Published

February 11th, 2022

Functions in HDoutliers (1.0.4)

HDoutliers

Leland Wilkinson's hdoutliers Algorithm for Outlier Detection
dataTrans

Data Transformation for Leland Wilkinson's hdoutliers Algorithm
ex2D

Two dimensional dataset --- outlier detection example
dots

One dimensional dots dataset --- outlier detection example
getHDmembers

Partitioning Stage of the hdoutliers Algorithm
getHDoutliers

Outlier Detection Stage of Wilkinson's hdoutliers Algorithm
plotHDoutliers

Display Outlier Detection Results