Educational Outlier Detection Algorithms with Step-by-Step Tutorials R package UAHDataScienceO version 1.0.0 CRAN Task View: AnomalyDetection (https://CRAN.R-project.org/view=AnomalyDetection) 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>. Software Suggests: knitr, rmarkdown Andriy Protsak Protsak <andriy.protsak@edu.uah.es> Comprehensive R Archive Network (CRAN) Andres Missiego Manjon [aut], Juan Jose Cuadrado Gallego [aut] (ORCID: <https://orcid.org/0000-0001-8178-5556>), Andriy Protsak Protsak [aut, cre], Universidad de Alcala de Henares [cph] MIT + file LICENSE (https://CRAN.R-project.org/package=UAHDataScienceO/LICENSE) 2025-02-20 application/tgz https://CRAN.R-project.org/package=UAHDataScienceO doi:10.32614/CRAN.package.UAHDataScienceO