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An implementation of an algorithm for outlier detection that can handle a) data with a mixed categorical and continuous variables, b) many columns of data, c) many rows of data, d) outliers that mask other outliers, and e) both unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, HDoutliers is based on a distributional model that uses probabilities to determine outliers.
| Version: | 1.0.4 |
| Depends: | R (≥ 3.1.0), FNN, FactoMineR, mclust |
| Published: | 2022-02-11 |
| DOI: | 10.32614/CRAN.package.HDoutliers |
| Author: | Chris Fraley [aut, cre], Leland Wilkinson [ctb] |
| Maintainer: | Chris Fraley <fraley at u.washington.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | ChangeLog |
| CRAN checks: | HDoutliers results |
| Reference manual: | HDoutliers.pdf |
| Package source: | HDoutliers_1.0.4.tar.gz |
| Windows binaries: | r-devel: HDoutliers_1.0.4.zip, r-release: HDoutliers_1.0.4.zip, r-oldrel: HDoutliers_1.0.4.zip |
| macOS binaries: | r-release (arm64): HDoutliers_1.0.4.tgz, r-oldrel (arm64): HDoutliers_1.0.4.tgz, r-release (x86_64): HDoutliers_1.0.4.tgz, r-oldrel (x86_64): HDoutliers_1.0.4.tgz |
| Old sources: | HDoutliers archive |
| Reverse imports: | OutliersO3 |
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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.