Title: Ordinal Forests: Prediction and Class Width Inference with
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

Title: Ordinal Forests: Prediction and Variable Ranking with Ordinal
Imports: Rcpp (>= 0.11.2), combinat, ggplot2
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
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
The main functions of the package are: ordfor() (construction of OF) and predict.ordfor()
(prediction of the target variable values of new observations).

Description: 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
Hornung R. (2017) Ordinal Forests. Tech. Rep. 212, Department of Statistics,