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UAHDataScienceO (version 1.0.0)

Educational Outlier Detection Algorithms with Step-by-Step Tutorials

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

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Version

Install

install.packages('UAHDataScienceO')

Monthly Downloads

151

Version

1.0.0

License

MIT + file LICENSE

Maintainer

Andriy Protsak Protsak

Last Published

February 20th, 2025

Functions in UAHDataScienceO (1.0.0)

sd_outliersLearn

sd_outliersLearn
transform_to_vector

transform_to_vector
quantile_outliersLearn

quantile_outliersLearn
lof

lof
mahalanobis_method

mahalanobis_method
DBSCAN_method

DBSCAN_method
manhattan_dist

manhattan_dist
boxandwhiskers

Box And Whiskers
knn

knn
euclidean_distance

euclidean_distance
compare_multivariate_methods

Compare Multivariate Outlier Detection Methods
compare_univariate_methods

Compare Univariate Outlier Detection Methods
mahalanobis_distance

mahalanobis_distance
mean_outliersLearn

mean_outliersLearn
z_score_method

z_score_method