ordinalForest (version 1.0)
Ordinal Forests: Prediction and Class Width Inference with
Ordinal Target Variables
Description
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).