Rcpp (>= 0.11.2), combinat, nnet, verification
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>.

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 importance 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. Moreover, the variable importance values
can also be based on the class probability predictions. Preliminary results indicate that
this might lead to a better discrimination between influential and non-influential covariates.
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>.