ordinalForest: Ordinal Forests: Prediction and Class Width Inference 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 and at the same time estimate the relative widths of the classes of the ordinal target variable. Using a (permutation-based) variable importance measure it is moreover possible to rank the importances of the covariates. OF will be presented in an upcoming technical report by Hornung et al.. The main functions of the package are: ordfor() (construction of OF), predict.ordfor() (prediction of the target variable values of new observations), and plot.ordfor() (visualization of the estimated relative widths of the classes of the ordinal target variable).

Version: 1.0
Imports: ranger, combinat, ggplot2
Published: 2017-04-13
Author: Roman Hornung
Maintainer: Roman Hornung <hornung at ibe.med.uni-muenchen.de>
License: GPL-2
NeedsCompilation: no
CRAN checks: ordinalForest results


Reference manual: ordinalForest.pdf
Package source: ordinalForest_1.0.tar.gz
Windows binaries: r-devel: ordinalForest_1.0.zip, r-release: ordinalForest_1.0.zip, r-oldrel: ordinalForest_1.0.zip
OS X El Capitan binaries: r-release: ordinalForest_1.0.tgz
OS X Mavericks binaries: r-oldrel: ordinalForest_1.0.tgz


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