Receiver Operating Characteristic (ROC)-Guided Classification and Survival Tree
Overview
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.
- Resubmission; previous version was archived
From NEWS.md in the release
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- 8includes a package overview page
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- 80%4 run in a plain check · 1 never run
- Exported functions with examples
- 67%
- Documented parameters
- 100%
- Return-value docs
- 67%
- Help pages citing references
- 13%
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- RR 4.6.0 released · 2026-04-24
- archivedRemoved from CRAN2025-09-17email to the maintainer is undeliverable
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- unarchivedReturned to CRAN2020-08-01
- 1.1.1
- RR 4.0.0 released · 2020-04-24
- archivedRemoved from CRAN2019-11-27installation failure was not corrected despite reminder
- 1.1.0
- RR 3.6.0 released · 2019-04-26
- 1.0.02019-03-24
- RR 3.5.0 released · 2018-04-23
Package metadata
- First published
- 2019-03-24
- Total releases
- 3 / 7 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 3.8 KB / 1 file
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