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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>.

Versions across snapshots

VersionRepositoryFileSize
1.6.8 rolling linux/jammy R-4.5 SurvMA_1.6.8.tar.gz 77.8 KiB
1.6.8 rolling linux/noble R-4.5 SurvMA_1.6.8.tar.gz 77.8 KiB
1.6.8 rolling source/ R- SurvMA_1.6.8.tar.gz 27.7 KiB
1.6.8 latest linux/jammy R-4.5 SurvMA_1.6.8.tar.gz 77.8 KiB
1.6.8 latest linux/noble R-4.5 SurvMA_1.6.8.tar.gz 77.8 KiB
1.6.8 latest source/ R- SurvMA_1.6.8.tar.gz 27.7 KiB
1.6.8 2026-04-26 source/ R- SurvMA_1.6.8.tar.gz 27.7 KiB
1.6.8 2026-04-23 source/ R- SurvMA_1.6.8.tar.gz 27.7 KiB
1.6.8 2026-04-09 windows/windows R-4.5 SurvMA_1.6.8.zip 80.8 KiB
1.6.8 2025-04-20 source/ R- SurvMA_1.6.8.tar.gz 27.7 KiB

Dependencies (latest)

Imports