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Implements a modification to the Random Survival Forests algorithm for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic.
| Version: | 1.2 |
| Imports: | Rcpp (≥ 0.11.3), icensmis, parallel, stats |
| LinkingTo: | Rcpp |
| Published: | 2018-02-27 |
| DOI: | 10.32614/CRAN.package.icRSF |
| Author: | Hui Xu and Raji Balasubramanian |
| Maintainer: | Hui Xu <huix at schoolph.umass.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| In views: | Survival |
| CRAN checks: | icRSF results |
| Reference manual: | icRSF.pdf |
| Package source: | icRSF_1.2.tar.gz |
| Windows binaries: | r-devel: icRSF_1.2.zip, r-release: icRSF_1.2.zip, r-oldrel: icRSF_1.2.zip |
| macOS binaries: | r-release (arm64): icRSF_1.2.tgz, r-oldrel (arm64): icRSF_1.2.tgz, r-release (x86_64): icRSF_1.2.tgz, r-oldrel (x86_64): icRSF_1.2.tgz |
| Old sources: | icRSF archive |
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