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>.
|Maintainer||Roman Hornung <[email protected]>|
|Package repository||View on CRAN|
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