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OutliersLearn: Educational Outlier Package with Common Outlier DetectionAlgorithms

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 + fileLICENSE
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 formhttps://CRAN.R-project.org/package=OutliersLearnto link to this page.


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