bagged.outliertrees: Robust Explainable Outlier Detection Based on OutlierTree

Bagged OutlierTrees is an explainable unsupervised outlier detection method based on an ensemble implementation of the existing OutlierTree procedure (Cortes, 2020). This implementation takes advantage of bootstrap aggregating (bagging) to improve robustness by reducing the possible masking effect and subsequent high variance (similarly to Isolation Forest), hence the name "Bagged OutlierTrees". To learn more about the base procedure OutlierTree (Cortes, 2020), please refer to <arXiv:2001.00636>.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: outliertree, dplyr, doSNOW, parallel, foreach, rlist, data.table
Published: 2021-07-06
Author: Rafael Santos [aut, cre]
Maintainer: Rafael Santos <rafael.jpsantos at outlook.com>
BugReports: https://github.com/RafaJPSantos/bagged.outliertrees/issues
License: MIT + file LICENSE
URL: https://github.com/RafaJPSantos/bagged.outliertrees
NeedsCompilation: no
Materials: README NEWS
CRAN checks: bagged.outliertrees results

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Reference manual: bagged.outliertrees.pdf
Package source: bagged.outliertrees_1.0.0.tar.gz
Windows binaries: r-devel: bagged.outliertrees_1.0.0.zip, r-release: bagged.outliertrees_1.0.0.zip, r-oldrel: bagged.outliertrees_1.0.0.zip
macOS binaries: r-release (arm64): bagged.outliertrees_1.0.0.tgz, r-release (x86_64): bagged.outliertrees_1.0.0.tgz, r-oldrel: bagged.outliertrees_1.0.0.tgz

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