aorsf: Accelerated Oblique Random Survival Forests

Fit, interpret, and make predictions with oblique random survival forests. Oblique decision trees are notoriously slow compared to their axis based counterparts, but 'aorsf' runs as fast or faster than axis-based decision tree algorithms for right-censored time-to-event outcomes. Methods to accelerate and interpret the oblique random survival forest are described in Jaeger et al., (2022) arXiv:2208.01129.

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 [email protected] (ORCID)

Other contributors:

• Nicholas Pajewski [contributor]

• Sawyer Welden [email protected] [contributor]

• Christopher Jackson [email protected] [reviewer]

• Marvin Wright [reviewer]

• Lukas Burk [reviewer]