Package: ordinalForest 2.4-4

Roman Hornung

ordinalForest: Ordinal Forests: Prediction and Variable Ranking with Ordinal Target Variables

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>.

Authors:Roman Hornung [aut, cre]

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# Install 'ordinalForest' in R:
install.packages('ordinalForest', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • hearth - Data on Coronary Artery Disease

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.09 score 4 stars 31 scripts 397 downloads 5 exports 14 dependencies

Last updated 2 days agofrom:af4a30f389. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 30 2024
R-4.5-linux-x86_64OKOct 30 2024

Exports:ordforperff_customperff_equalperff_oneclassperff_proportional

Dependencies:bootCircStatscombinatdotCall64dtwfieldsmapsMASSnnetproxyRcppspamverificationviridisLite