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) .


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install.packages("orf")

0.1.2 by Gabriel Okasa, 5 days ago


https://github.com/okasag/orf


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