Package: UAHDataScienceO
Type: Package
Title: Educational Outlier Detection Algorithms with Step-by-Step
        Tutorials
Version: 1.0.0
Author: Andres Missiego Manjon [aut], Juan Jose Cuadrado Gallego [aut],
        Andriy Protsak Protsak [aut, cre], Universidad de Alcalá de
        Henares [cph]
Maintainer: Andriy Protsak Protsak <andriy.protsak@edu.uah.es>
Description: 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>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.2
VignetteBuilder: knitr
Suggests: knitr, rmarkdown
NeedsCompilation: no
Repository: https://andriyprotsak5.r-universe.dev
Date/Publication: 2025-02-09 09:35:44 UTC
RemoteUrl: https://github.com/andriyprotsak5/UAHDataScienceO
RemoteRef: HEAD
RemoteSha: f3b55c81a9b2ec688ce2614533672ac0d07f980e
Packaged: 2026-06-22 07:41:37 UTC; root