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.
|Imports:||Rcpp (≥ 0.11.3), icensmis, parallel|
|Author:||Hui Xu and Raji Balasubramanian|
|Maintainer:||Hui Xu <huix at schoolph.umass.edu>|
|License:||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]|
|CRAN checks:||icRSF results|
|Windows binaries:||r-devel: icRSF_1.0.zip, r-release: icRSF_1.0.zip, r-oldrel: icRSF_1.0.zip|
|OS X El Capitan binaries:||r-release: icRSF_1.0.tgz|
|OS X Mavericks binaries:||r-oldrel: icRSF_1.0.tgz|
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