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).

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 (2017).
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. (2017) Ordinal Forests. Tech. Rep. 212, Department of Statistics,
University of Munich.

Rcpp (>= 0.11.2), combinat, ggplot2, nnet

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 (2019).
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. (2019) Ordinal Forests. Journal of Classification,
<doi:10.1007/s00357-018-9302-x>.