The ordinal forest (OF) method allows ordinal regression with highdimensional and lowdimensional 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 (permutationbased) 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 noninfluential 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/s003570189302x>.
Package details 


Author  Roman Hornung 
Maintainer  Roman Hornung <hornung@ibe.med.unimuenchen.de> 
License  GPL2 
Version  2.42 
Package repository  View on CRAN 
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