Ordinal Forests: Prediction and Variable Ranking with Ordinal Target Variables

Ordinal forests (OF) are a method for ordinal regression with high-dimensional and low-dimensional data that is able to predict the values of the ordinal target variable for new observations based on a training dataset. Using a (permutation-based) variable importance measure it is moreover possible to rank the covariates with respect to their importances in the prediction of the values of the ordinal target variable. OF will be presented in an upcoming technical report by Hornung et al.. The main functions of the package are: ordfor() (construction of OF) and predict.ordfor() (prediction of the target variable values of new observations).


Reference manual

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2.0 by Roman Hornung, 2 months ago

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

Authors: Roman Hornung

Documentation:   PDF Manual  

GPL-2 license

Imports Rcpp, combinat, ggplot2

Linking to Rcpp

See at CRAN