rocTree: Receiver Operating Characteristic (ROC)-Guided Classification and Survival Tree

Receiver Operating Characteristic (ROC)-guided survival trees and forests algorithms are implemented, providing a unified framework for tree-structured analysis with censored survival outcomes. A time-invariant partition scheme on the survivor population was considered to incorporate time-dependent covariates. Motivated by ideas of randomized tests, generalized time-dependent ROC curves were used to evaluate the performance of survival trees and establish the optimality of the target hazard function. The optimality of the target hazard function motivates us to use a weighted average of the time-dependent area under the curve (AUC) on a set of time points to evaluate the prediction performance of survival trees and to guide splitting and pruning. A detailed description of the implemented methods can be found in Sun et al. (2019) <arXiv:1809.05627>.

Version: 1.0.0
Depends: R (≥ 3.4.0)
Imports: DiagrammeR (≥ 1.0.0), parallel, data.tree (≥ 0.7.5), graphics, stats, survival (≥ 2.38), methods, tibble, dplyr, ggplot2, MASS, flexsurv
Published: 2019-03-24
Author: Yifei Sun [aut], Mei-Cheng Wang [aut], Sy Han Chiou [aut, cre]
Maintainer: Sy Han Chiou <schiou at>
License: GPL (≥ 3)
NeedsCompilation: yes
Materials: NEWS
CRAN checks: rocTree results


Reference manual: rocTree.pdf
Package source: rocTree_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: rocTree_1.0.0.tgz, r-oldrel: rocTree_1.0.0.tgz


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