aorsf: Accelerated Oblique Random Forests

Fit, interpret, and compute predictions with oblique random forests. Includes support for partial dependence, variable importance, passing customized functions for variable importance and identification of linear combinations of features. Methods for the oblique random survival forest are described in Jaeger et al., (2023) doi:10.1080/10618600.2023.2231048 .

See also

Useful links:

• https://github.com/ropensci/aorsf

• https://docs.ropensci.org/aorsf/

• Report bugs at https://github.com/ropensci/aorsf/issues/

Author

Maintainer: Byron Jaeger bjaeger@wakehealth.edu (ORCID)

Other contributors:

• Nicholas Pajewski [contributor]

• Sawyer Welden swelden@wakehealth.edu [contributor]

• Christopher Jackson chris.jackson@mrc-bsu.cam.ac.uk [reviewer]

• Marvin Wright [reviewer]

• Lukas Burk [reviewer]