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
DOI:	10.32614/CRAN.package.UAHDataScienceO
Author:	Andres Missiego Manjon [aut], Juan Jose Cuadrado Gallego [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.pdf
Vignettes:	UAHDataScienceO (source, R code)

Downloads:

Package source:	UAHDataScienceO_1.0.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: UAHDataScienceO_1.0.0.zip
macOS binaries:	r-devel (arm64): UAHDataScienceO_1.0.0.tgz, r-release (arm64): UAHDataScienceO_1.0.0.tgz, r-oldrel (arm64): UAHDataScienceO_1.0.0.tgz, r-devel (x86_64): UAHDataScienceO_1.0.0.tgz, r-release (x86_64): UAHDataScienceO_1.0.0.tgz, r-oldrel (x86_64): UAHDataScienceO_1.0.0.tgz

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