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>.

Version: 0.1.4
Depends: R (≥ 3.6)
Imports: collapse, data.table, lifecycle, R6, Rcpp, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: covr, ggplot2, glmnet, knitr, rmarkdown, survival, SurvMetrics, testthat (≥ 3.0.0), tibble, units
Published: 2024-05-03
Author: Byron Jaeger ORCID iD [aut, cre], Nicholas Pajewski [ctb], Sawyer Welden [ctb], Christopher Jackson [rev], Marvin Wright [rev], Lukas Burk [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
Tips to speed up computation
Out-of-bag predictions and evaluation
PD and ICE curves with ORSF


Package source: aorsf_0.1.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): aorsf_0.1.4.tgz, r-oldrel (arm64): aorsf_0.1.4.tgz, r-release (x86_64): aorsf_0.1.4.tgz, r-oldrel (x86_64): aorsf_0.1.4.tgz
Old sources: aorsf archive

Reverse dependencies:

Reverse imports: glmnetr
Reverse suggests: censored


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