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 
[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:
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