outliertree: Explainable Outlier Detection Through Decision Tree Conditioning

Outlier detection method that flags suspicious values within observations, constrasting them against the normal values in a user-readable format, potentially describing conditions within the data that make a given outlier more rare. Full procedure is described in Cortes (2020) <arXiv:2001.00636>. Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.

Version: 1.7.6-1
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 1.0.1)
LinkingTo: Rcpp, Rcereal
Suggests: knitr, rmarkdown
Published: 2021-10-29
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera at gmail.com>
BugReports: https://github.com/david-cortes/outliertree/issues
License: GPL (≥ 3)
URL: https://github.com/david-cortes/outliertree
NeedsCompilation: yes
CRAN checks: outliertree results

Documentation:

Reference manual: outliertree.pdf
Vignettes: Introducing OutlierTree

Downloads:

Package source: outliertree_1.7.6-1.tar.gz
Windows binaries: r-devel: outliertree_1.7.6-1.zip, r-devel-UCRT: outliertree_1.7.6-1.zip, r-release: outliertree_1.7.6-1.zip, r-oldrel: outliertree_1.7.6-1.zip
macOS binaries: r-release (arm64): outliertree_1.7.6-1.tgz, r-release (x86_64): outliertree_1.7.6-1.tgz, r-oldrel: outliertree_1.7.6-1.tgz
Old sources: outliertree archive

Reverse dependencies:

Reverse imports: bagged.outliertrees
Reverse suggests: isotree

Linking:

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