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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: 0.1.5
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: 2024-05-30
DOI: 10.32614/CRAN.package.aorsf
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 wakehealth.edu>
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.pdf
Vignettes: Introduction to aorsf
Tips to speed up computation
Out-of-bag predictions and evaluation
PD and ICE curves with ORSF

Downloads:

Package source: aorsf_0.1.5.tar.gz
Windows binaries: r-devel: aorsf_0.1.5.zip, r-release: aorsf_0.1.5.zip, r-oldrel: aorsf_0.1.5.zip
macOS binaries: r-release (arm64): aorsf_0.1.5.tgz, r-oldrel (arm64): aorsf_0.1.5.tgz, r-release (x86_64): aorsf_0.1.5.tgz, r-oldrel (x86_64): aorsf_0.1.5.tgz
Old sources: aorsf archive

Reverse dependencies:

Reverse imports: glmnetr
Reverse suggests: bonsai, censored

Linking:

Please use the canonical form https://CRAN.R-project.org/package=aorsf to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.