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>.

Version: 0.0.2
Depends: R (≥ 3.6)
Imports: Rcpp, data.table, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: survival, survivalROC, ggplot2, testthat (≥ 3.0.0), knitr, rmarkdown, glmnet, covr, units, tibble
Published: 2022-09-05
Author: Byron Jaeger ORCID iD [aut, cre], Nicholas Pajewski [ctb], Sawyer Welden [ctb], Christopher Jackson [rev]
Maintainer: Byron Jaeger <bjaeger at>
License: MIT + file LICENSE
NeedsCompilation: yes
Citation: aorsf citation info
Materials: README NEWS
CRAN checks: aorsf results


Reference manual: aorsf.pdf
Vignettes: Introduction to aorsf
Out-of-bag predictions and evaluation
PD and ICE curves with ORSF


Package source: aorsf_0.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): aorsf_0.0.2.tgz, r-oldrel (arm64): aorsf_0.0.2.tgz, r-release (x86_64): aorsf_0.0.2.tgz, r-oldrel (x86_64): aorsf_0.0.2.tgz
Old sources: aorsf archive


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