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) , Abir Smiti (2020) , and Xiaogang Su, Chih-Ling Tsai (2011) .


Reference manual

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install.packages("UAHDataScienceO")

1.0.0 by Andriy Protsak Protsak, 2 years ago


Browse source code at https://github.com/cran/UAHDataScienceO


Authors: Andres Missiego Manjon [aut] , Juan Jose Cuadrado Gallego [aut] , Andriy Protsak Protsak [aut, cre] , Universidad de Alcala de Henares [cph]


Documentation:   PDF Manual  


MIT + file LICENSE license


Suggests knitr, rmarkdown


See at CRAN