UAHDataScienceO: Educational Outlier Detection Algorithms with Step-by-Step Tutorials

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: 2025-02-20
Author: Andres Missiego Manjon [aut], Juan Jose Cuadrado Gallego ORCID iD [aut], Andriy Protsak Protsak [aut, cre], Universidad de Alcala de Henares [cph]
Maintainer: Andriy Protsak Protsak <andriy.protsak at edu.uah.es>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: NEWS
CRAN checks: UAHDataScienceO results

Documentation:

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

Downloads:

Package source: UAHDataScienceO_1.0.0.tar.gz
Windows binaries: r-devel: UAHDataScienceO_1.0.0.zip, r-release: UAHDataScienceO_1.0.0.zip, r-oldrel: UAHDataScienceO_1.0.0.zip
macOS binaries: r-release (arm64): UAHDataScienceO_1.0.0.tgz, r-oldrel (arm64): UAHDataScienceO_1.0.0.tgz, r-release (x86_64): UAHDataScienceO_1.0.0.tgz, r-oldrel (x86_64): UAHDataScienceO_1.0.0.tgz

Linking:

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