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 [aut, cre], Nicholas Pajewski [ctb], Sawyer Welden [ctb], Christopher Jackson [rev] |
Maintainer: | Byron Jaeger <bjaeger at wakehealth.edu> |
BugReports: | https://github.com/bcjaeger/aorsf/issues/ |
License: | MIT + file LICENSE |
URL: | https://github.com/bcjaeger/aorsf/, https://bcjaeger.github.io/aorsf/ |
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: aorsf_0.0.2.zip, r-release: aorsf_0.0.2.zip, r-oldrel: aorsf_0.0.2.zip |
macOS binaries: | r-release (arm64): aorsf_0.0.1.tgz, r-oldrel (arm64): aorsf_0.0.1.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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