isotree: Isolation-Based Outlier Detection

Fast and multi-threaded implementation of isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>), extended isolation forest (Hariri, Kind, Brunner (2018) <arXiv:1811.02141>), SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>), and fair-cut forest (Cortes (2019) <arXiv:1911.06646>), for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) <arXiv:1910.12362>), and imputation of missing values (Cortes (2019) <arXiv:1911.06646>), based on random or guided decision tree splitting. Provides simple heuristics for fitting the model to categorical columns and handling missing data, and offers options for varying between random and guided splits, and for using different splitting criteria.

Version: 0.1.18
Imports: Rcpp (≥ 1.0.1)
LinkingTo: Rcpp, Rcereal
Suggests: MASS, outliertree, jsonlite, readr
Enhances: Matrix, SparseM
Published: 2020-07-29
Author: David Cortes
Maintainer: David Cortes <david.cortes.rivera at>
License: BSD_2_clause + file LICENSE
NeedsCompilation: yes
In views: MissingData
CRAN checks: isotree results


Reference manual: isotree.pdf
Package source: isotree_0.1.18.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: isotree_0.1.18.tgz, r-oldrel: isotree_0.1.18.tgz
Old sources: isotree archive


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