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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 |
| 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.
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