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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>.
| Version: | 1.0.0 |
| Suggests: | knitr, rmarkdown |
| Published: | 2024-06-05 |
| DOI: | 10.32614/CRAN.package.OutliersLearn |
| Author: | Andres Missiego Manjon [aut, cre], Juan Jose Cuadrado Gallego [aut] |
| Maintainer: | Andres Missiego Manjon <andres.missiego at edu.uah.es> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | OutliersLearn results |
| Reference manual: | OutliersLearn.pdf |
| Vignettes: |
OutliersLearnVignette |
| Package source: | OutliersLearn_1.0.0.tar.gz |
| Windows binaries: | r-devel: OutliersLearn_1.0.0.zip, r-release: OutliersLearn_1.0.0.zip, r-oldrel: OutliersLearn_1.0.0.zip |
| macOS binaries: | r-release (arm64): OutliersLearn_1.0.0.tgz, r-oldrel (arm64): OutliersLearn_1.0.0.tgz, r-release (x86_64): OutliersLearn_1.0.0.tgz, r-oldrel (x86_64): OutliersLearn_1.0.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.