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HDoutliers  

Leland Wilkinson's Algorithm for Detecting Multidimensional Outliers
View on CRAN: Click here


Download and install HDoutliers package within the R console
Install from CRAN:
install.packages("HDoutliers")

Install from Github:
library("remotes")
install_github("cran/HDoutliers")

Install by package version:
library("remotes")
install_version("HDoutliers", "1.0.4")



Attach the package and use:
library("HDoutliers")
Maintained by
Chris Fraley
[Scholar Profile | Author Map]
First Published: 2016-06-23
Latest Update: 2022-02-11
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.
How to cite:
Chris Fraley (2016). HDoutliers: Leland Wilkinson's Algorithm for Detecting Multidimensional Outliers. R package version 1.0.4, https://cran.r-project.org/web/packages/HDoutliers. Accessed 04 May. 2025.
Previous versions and publish date:
0.2 (2016-06-23 01:24), 0.5 (2016-07-18 20:10), 0.8 (2016-09-09 10:13), 0.9 (2016-11-01 22:44), 0.12 (2016-11-17 08:27), 0.14 (2016-11-28 22:13), 0.15 (2016-12-24 11:23), 1.0.1 (2020-04-01 07:40), 1.0.2 (2020-07-10 07:20), 1.0.3 (2020-09-29 09:20), 1.0 (2018-02-11 15:41)
Other packages that cited HDoutliers R package
View HDoutliers citation profile
Other R packages that HDoutliers depends, imports, suggests or enhances
Complete documentation for HDoutliers
Functions, R codes and Examples using the HDoutliers R package
Some associated functions: HDoutliers . dataTrans . dots . ex2D . getHDmembers . getHDoutliers . plotHDoutliers . 
Some associated R codes: HDoutliers.R . dataTrans.R . getHDmembers.R . getHDoutliers.R . plotHDoutliers.R .  Full HDoutliers package functions and examples
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