SurvMA: Model Averaging Prediction of Personalized Survival Probabilities (original) (raw)
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 |
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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 [aut, cre], Jie Ding [aut], Xiaoguang Wang [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 |
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