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:	0.1.6
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:	2025-12-11
DOI:	10.32614/CRAN.package.aorsf
Author:	Byron Jaeger [aut, cre], Nicholas Pajewski [ctb], Sawyer Welden [ctb], Christopher Jackson [rev], Marvin Wright [rev], Lukas Burk [rev]
Maintainer:	Byron Jaeger <byron.jaeger at gmail.com>
BugReports:	https://github.com/ropensci/aorsf/issues/
License:	MIT + file LICENSE
URL:	https://github.com/ropensci/aorsf, https://docs.ropensci.org/aorsf/
NeedsCompilation:	yes
Citation:	aorsf citation info
Materials:	README, NEWS
CRAN checks:	aorsf results

Documentation:

Reference manual:	aorsf.html , aorsf.pdf
Vignettes:	Introduction to aorsf (source, R code) Tips to speed up computation (source, R code) Out-of-bag predictions and evaluation (source, R code) PD and ICE curves with ORSF (source, R code)

Downloads:

Package source:	aorsf_0.1.6.tar.gz
Windows binaries:	r-devel: aorsf_0.1.6.zip, r-release: aorsf_0.1.6.zip, r-oldrel: aorsf_0.1.6.zip
macOS binaries:	r-release (arm64): aorsf_0.1.6.tgz, r-oldrel (arm64): aorsf_0.1.6.tgz, r-release (x86_64): aorsf_0.1.6.tgz, r-oldrel (x86_64): aorsf_0.1.6.tgz
Old sources:	aorsf archive

Reverse dependencies:

Reverse imports:	glmnetr
Reverse suggests:	bonsai, censored, fastml, filtro

Linking:

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