Datasets | Auxiliary functions | Main outlier detection methods | Box and Whiskers (box_and_whiskers()) | DBSCAN (DBSCAN_method()) | KNN (knn()) | LOF simplified (lof()) | Mahalanobis Method (mahalanobis_method()) | Z-score method (z_score_method()) | [Previous content remains the same until after the main methods section] | Comparing Methods | Multivariate Methods Comparison | Univariate Methods Comparison
Package: UAHDataScienceO 1.0.0
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>.
Authors:
UAHDataScienceO_1.0.0.tar.gz
UAHDataScienceO_1.0.0.zip(r-4.7)UAHDataScienceO_1.0.0.zip(r-4.6)UAHDataScienceO_1.0.0.zip(r-4.5)
UAHDataScienceO_1.0.0.tgz(r-4.6-any)UAHDataScienceO_1.0.0.tgz(r-4.5-any)
UAHDataScienceO_1.0.0.tar.gz(r-4.7-any)UAHDataScienceO_1.0.0.tar.gz(r-4.6-any)
UAHDataScienceO_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
UAHDataScienceO/json (API)
| # Install 'UAHDataScienceO' in R: |
| install.packages('UAHDataScienceO', repos = c('https://andriyprotsak5.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/andriyprotsak5/uahdatascienceo/issues
Last updated from:f3b55c81a9. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 111 | ||
| source / vignettes | OK | 136 | ||
| linux-release-x86_64 | OK | 112 | ||
| macos-release-arm64 | OK | 94 | ||
| macos-oldrel-arm64 | OK | 85 | ||
| windows-devel | OK | 73 | ||
| windows-release | OK | 66 | ||
| windows-oldrel | OK | 69 | ||
| wasm-release | OK | 88 |
Exports:boxandwhiskerscompare_multivariate_methodscompare_univariate_methodsDBSCAN_methodeuclidean_distanceknnlofmahalanobis_distancemahalanobis_methodmanhattan_distmean_outliersLearnquantile_outliersLearnsd_outliersLearntransform_to_vectorz_score_method
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Box And Whiskers | boxandwhiskers |
| Compare Multivariate Outlier Detection Methods | compare_multivariate_methods |
| Compare Univariate Outlier Detection Methods | compare_univariate_methods |
| DBSCAN_method | DBSCAN_method |
| euclidean_distance | euclidean_distance |
| knn | knn |
| lof | lof |
| mahalanobis_distance | mahalanobis_distance |
| mahalanobis_method | mahalanobis_method |
| manhattan_dist | manhattan_dist |
| mean_outliersLearn | mean_outliersLearn |
| quantile_outliersLearn | quantile_outliersLearn |
| sd_outliersLearn | sd_outliersLearn |
| transform_to_vector | transform_to_vector |
| z_score_method | z_score_method |
