ordinalForest: Ordinal Forests: Prediction and Variable Ranking with Ordinal Target Variables

The ordinal forest (OF) method allows ordinal regression with high-dimensional and low-dimensional data. After having constructed an OF prediction rule using a training dataset, it can be used to predict the values of the ordinal target variable for new observations. Moreover, by means of the (permutation-based) variable importance measure of OF, it is also possible to rank the covariates with respect to their importances in the prediction of the values of the ordinal target variable. OF is presented in Hornung (2020). NOTE: Starting with package version 2.4, it is also possible to obtain class probability predictions in addition to the class point predictions, where the variable importance values are also obtained based on the class probabilities. The main functions of the package are: ordfor() (construction of OF) and predict.ordfor() (prediction of the target variable values of new observations). References: Hornung R. (2020) Ordinal Forests. Journal of Classification 37, 4–17. <doi:10.1007/s00357-018-9302-x>.

Version: 2.4-1
Imports: Rcpp (≥ 0.11.2), combinat, nnet, verification
LinkingTo: Rcpp
Published: 2020-07-22
Author: Roman Hornung
Maintainer: Roman Hornung <hornung at ibe.med.uni-muenchen.de>
License: GPL-2
NeedsCompilation: yes
CRAN checks: ordinalForest results


Reference manual: ordinalForest.pdf
Package source: ordinalForest_2.4-1.tar.gz
Windows binaries: r-devel: ordinalForest_2.4-1.zip, r-release: ordinalForest_2.4-1.zip, r-oldrel: ordinalForest_2.4-1.zip
macOS binaries: r-release: ordinalForest_2.4-1.tgz, r-oldrel: ordinalForest_2.4-1.tgz
Old sources: ordinalForest archive


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