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