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.
- Version1.6.8
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- Last release09/23/2024
Team
Mengyu Li
Jie Ding
Show author detailsRolesAuthorXiaoguang Wang
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- Imports4 packages