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

Receiver Operating Characteristic (ROC)-guided survival trees and ensemble 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/survival 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: 1.1.1
Depends: R (≥ 3.5.0)
Imports: DiagrammeR (≥ 1.0.0), data.tree (≥ 0.7.5), graphics, stats, survival (≥ 2.38), ggplot2, MASS, flexsurv, Rcpp
LinkingTo: Rcpp, RcppArmadillo
Published: 2020-08-01
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_1.1.1.tar.gz
Windows binaries: r-devel:, r-devel-UCRT:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): rocTree_1.1.1.tgz, r-release (x86_64): rocTree_1.1.1.tgz, r-oldrel: rocTree_1.1.1.tgz
Old sources: rocTree archive


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