Isolation-Based Outlier Detection

Fast and multi-threaded implementation of isolation forest (Liu, Ting, Zhou (2008) ), extended isolation forest (Hariri, Kind, Brunner (2018) ), SCiForest (Liu, Ting, Zhou (2010) ), fair-cut forest (Cortes (2021) ), robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) <>), and customizable variations of them, for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) ), and imputation of missing values (Cortes (2019) ), based on random or guided decision tree splitting, and providing different metrics for scoring anomalies based on isolation depth or density (Cortes (2021) ). 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.


Reference manual

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0.5.5 by David Cortes, 15 days ago

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Authors: David Cortes [aut, cre, cph] , Thibaut Goetghebuer-Planchon [cph] (Copyright holder of included robinmap library) , David Blackman [cph] (Copyright holder of original xoshiro code) , Sebastiano Vigna [cph] (Copyright holder of original xoshiro code)

Documentation:   PDF Manual  

Task views: Missing Data

BSD_2_clause + file LICENSE license

Imports Rcpp

Suggests MASS, outliertree, jsonlite

Enhances Matrix, SparseM

Linking to Rcpp

Imported by dsos, itsdm.

See at CRAN