SurvMA
1.6.8Model Averaging Prediction of Personalized Survival Probabilities
Overview
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.
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- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Code & Tests
- Cyclomatic complexity
- 3.0 median / 20 max
- Documented parameters
- 81%
Test coverage
Line coverage
–
Expression
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Tests / Examples
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Functions
14 2 exported
Complexity
5.6 avg / 20 max
Call network
14 nodes / 12 edges
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People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.6.8Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2024-09-23
- Total releases
- 1 / 2 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 13 KB / 3 files
- Download size
- 27 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet