CRAN/E | HDoutliers

HDoutliers

Leland Wilkinson's Algorithm for Detecting Multidimensional Outliers

Installation

About

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.

Key Metrics

Version 1.0.4
R ≥ 3.1.0
Published 2022-02-11 812 days ago
Needs compilation? no
License MIT
License File
CRAN checks HDoutliers results

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Maintainer

Maintainer

Chris Fraley

fraley@u.washington.edu

Authors

Chris Fraley

aut / cre

Lel
Wilkinson

ctb

Material

ChangeLog
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

HDoutliers archive

Depends

R ≥ 3.1.0
FNN
FactoMineR
mclust

Reverse Imports

OutliersO3