SurvMA: Model Averaging Prediction of Personalized Survival Probabilities

Provide model averaging-based approaches that can be used to predict personalized survival probabilities. The key underlying idea is to approximate the conditional survival function using a weighted average of multiple candidate models. Two scenarios of candidate models are allowed: (Scenario 1) partial linear Cox model and (Scenario 2) time-varying coefficient Cox model. A reference of the underlying methods is Li and Wang (2023) <doi:10.1016/j.csda.2023.107759>.

Version: 1.6.8
Depends: R (≥ 3.5.0)
Imports: survival, maxLik, pec, quadprog, splines, methods
Published: 2024-09-23
DOI: 10.32614/CRAN.package.SurvMA
Author: Mengyu Li ORCID iD [aut, cre], Jie Ding ORCID iD [aut], Xiaoguang Wang ORCID iD [aut]
Maintainer: Mengyu Li <mylilucky at 163.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: <https://github.com/Stat-WangXG/SurvMA>
NeedsCompilation: no
CRAN checks: SurvMA results

Documentation:

Reference manual: SurvMA.pdf

Downloads:

Package source: SurvMA_1.6.8.tar.gz
Windows binaries: r-devel: SurvMA_1.6.8.zip, r-release: SurvMA_1.6.8.zip, r-oldrel: SurvMA_1.6.8.zip
macOS binaries: r-release (arm64): SurvMA_1.6.8.tgz, r-oldrel (arm64): SurvMA_1.6.8.tgz, r-release (x86_64): SurvMA_1.6.8.tgz, r-oldrel (x86_64): SurvMA_1.6.8.tgz

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