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) ), and fair-cut forest (Cortes (2019) ), 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. 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.


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Reference manual

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install.packages("isotree")

0.3.0 by David Cortes, a month ago


https://github.com/david-cortes/isotree


Report a bug at https://github.com/david-cortes/isotree/issues


Browse source code at https://github.com/cran/isotree


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


See at CRAN