survival-models
CommunityBayesian survival models with censoring support.
Authorchoxos
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Bayesian survival analysis models provide probabilistic hazard and time-to-event modeling with censoring considerations, enabling robust inference from incomplete follow-up data.
Core Features & Use Cases
- Supports Exponential, Weibull, Log-Normal, and Piecewise Exponential hazard models with covariates.
- Includes frailty modeling and generated quantities for interpretation of hazard ratios and survival probabilities.
- Suitable for clinical biostatistics, reliability analysis, and research requiring time-to-event analysis with censoring.
- Real-world use: estimate effects of covariates on survival time and predict survival curves under different scenarios.
Quick Start
Run a basic exponential survival model on your dataset using Stan or JAGS templates.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: survival-models Download link: https://github.com/choxos/BiostatAgent/archive/main.zip#survival-models Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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