Ordered Random Forests

An implementation of the Ordered Forest estimator as developed in Lechner & Okasa (2019) . The Ordered Forest flexibly estimates the conditional probabilities of models with ordered categorical outcomes (so-called ordered choice models). Additionally to common machine learning algorithms the 'orf' package provides functions for estimating marginal effects as well as statistical inference thereof and thus provides similar output as in standard econometric models for ordered choice. The core forest algorithm relies on the fast C++ forest implementation from the 'ranger' package (Wright & Ziegler, 2017) .


Reference manual

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0.1.3 by Gabriel Okasa, a year ago


Report a bug at https://github.com/okasag/orf/issues

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

Authors: Gabriel Okasa [aut, cre] , Michael Lechner [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports ggplot2, ranger, Rcpp, stats, utils, xtable

Suggests knitr, rmarkdown, testthat

Linking to Rcpp

See at CRAN