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Oblique random survival forests incorporate linear combinations of input variables into random survival forests (Ishwaran, 2008 <doi:10.1214/08-AOAS169>). Regularized Cox proportional hazard models (Simon, 2016 <doi:10.18637/jss.v039.i05>) are used to identify optimal linear combinations of input variables.
Version: | 0.1.2 |
Depends: | R (≥ 3.5.0) |
Imports: | Rcpp, pec, data.table, stats, missForest, purrr, glmnet, survival, dplyr, rlang, prodlim, ggthemes, tidyr, ggplot2, scales |
LinkingTo: | Rcpp, RcppArmadillo |
Published: | 2022-08-28 |
DOI: | 10.32614/CRAN.package.obliqueRSF |
Author: | Byron Jaeger [aut, cre] |
Maintainer: | Byron Jaeger <bjaeger at wakehealth.edu> |
License: | GPL-3 |
NeedsCompilation: | yes |
Materials: | README |
CRAN checks: | obliqueRSF results |
Reference manual: | obliqueRSF.pdf |
Package source: | obliqueRSF_0.1.2.tar.gz |
Windows binaries: | r-devel: obliqueRSF_0.1.2.zip, r-release: obliqueRSF_0.1.2.zip, r-oldrel: obliqueRSF_0.1.2.zip |
macOS binaries: | r-release (arm64): obliqueRSF_0.1.2.tgz, r-oldrel (arm64): obliqueRSF_0.1.2.tgz, r-release (x86_64): obliqueRSF_0.1.2.tgz, r-oldrel (x86_64): obliqueRSF_0.1.2.tgz |
Old sources: | obliqueRSF archive |
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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.
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