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rocTree
View on CRAN: Click
here
Download and install rocTree package within the R console
Install from CRAN:
install.packages("rocTree")
Install from Github:
library("remotes")
install_github("cran/rocTree") Install by package version:
library("remotes")
install_version("rocTree", "1.1.1") Attach the package and use:
library("rocTree")
Maintained by
Sy Han Chiou
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-03-24
Latest Update: 2020-08-01
Description:
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) .
How to cite:
Sy Han Chiou (2019). rocTree: Receiver Operating Characteristic (ROC)-Guided Classification and Survival Tree. R package version 1.1.1, https://cran.r-project.org/web/packages/rocTree. Accessed 25 Jun. 2026.
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Complete documentation for rocTree
Functions, R codes and Examples using
the rocTree R package
Some associated functions: export_Surv . plot.rocTree . predict.rocTree . print.rocTree . rocTree-package . rocTree . simDat . simu .
Some associated R codes: RcppExports.R . Surv.R . data.R . plot.predict.rocTree.R . plot.rocTree.R . predict.rocTree.R . print.rocTree.R . rocTree.R . rocTree_pkg.R . simu.R . util.R . Full rocTree package functions and examples
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