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cff-version: 1.2.0
message: 'To cite package "ordinalForest" in publications use:'
type: software
license: GPL-2.0-only
title: 'ordinalForest: Ordinal Forests: Prediction and Variable Ranking with Ordinal
  Target Variables'
version: 2.4-4
doi: 10.32614/CRAN.package.ordinalForest
abstract: '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. <https://doi.org/10.1007/s00357-018-9302-x>.'
authors:
- family-names: Hornung
  given-names: Roman
  email: hornung@ibe.med.uni-muenchen.de
repository: https://CRAN.R-project.org/package=ordinalForest
date-released: '2024-10-29'
contact:
- family-names: Hornung
  given-names: Roman
  email: hornung@ibe.med.uni-muenchen.de