icRSF: A Modified Random Survival Forest Algorithm
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.
||Rcpp (≥ 0.11.3), icensmis, parallel, stats
||Hui Xu and Raji Balasubramanian
||Hui Xu <huix at schoolph.umass.edu>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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