OutliersLearn: Educational Outlier Package with Common Outlier Detection Algorithms

Provides implementations of some of the most important outlier detection algorithms. Includes a tutorial mode option that shows a description of each algorithm and provides a step-by-step execution explanation of how it identifies outliers from the given data with the specified input parameters. References include the works of Azzedine Boukerche, Lining Zheng, and Omar Alfandi (2020) <doi:10.1145/3381028>, Abir Smiti (2020) <doi:10.1016/j.cosrev.2020.100306>, and Xiaogang Su, Chih-Ling Tsai (2011) <doi:10.1002/widm.19>.

Version:	1.0.0
Suggests:	knitr, rmarkdown
Published:	2024-06-05
DOI:	10.32614/CRAN.package.OutliersLearn
Author:	Andres Missiego Manjon [aut, cre], Juan Jose Cuadrado Gallego [aut]
Maintainer:	Andres Missiego Manjon <andres.missiego at edu.uah.es>
License:	MIT + file LICENSE
NeedsCompilation:	no
Materials:	README
In views:	AnomalyDetection
CRAN checks:	OutliersLearn results

Documentation:

Reference manual:	OutliersLearn.html , OutliersLearn.pdf
Vignettes:	OutliersLearnVignette (source, R code)

Downloads:

Package source:	OutliersLearn_1.0.0.tar.gz
Windows binaries:	r-devel: OutliersLearn_1.0.0.zip, r-release: OutliersLearn_1.0.0.zip, r-oldrel: OutliersLearn_1.0.0.zip
macOS binaries:	r-release (arm64): OutliersLearn_1.0.0.tgz, r-oldrel (arm64): OutliersLearn_1.0.0.tgz, r-release (x86_64): OutliersLearn_1.0.0.tgz, r-oldrel (x86_64): OutliersLearn_1.0.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=OutliersLearn to link to this page.