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Performs angle-based outlier detection on a given dataframe. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection.
| Version: | 0.1 |
| Depends: | cluster, R (≥ 3.1.2) |
| Published: | 2015-08-31 |
| Author: | Jose Jimenez |
| Maintainer: | Jose Jimenez <jose at jimenezluna.com> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | abodOutlier results |
| Reference manual: | abodOutlier.pdf |
| Package source: | abodOutlier_0.1.tar.gz |
| Windows binaries: | r-devel: abodOutlier_0.1.zip, r-release: abodOutlier_0.1.zip, r-oldrel: abodOutlier_0.1.zip |
| macOS binaries: | r-release (arm64): abodOutlier_0.1.tgz, r-oldrel (arm64): abodOutlier_0.1.tgz, r-release (x86_64): abodOutlier_0.1.tgz, r-oldrel (x86_64): abodOutlier_0.1.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.
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