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awesome-survival-analysis
Resources for Survival Analysis
https://github.com/einatboro/awesome-survival-analysis
Last synced: 4 days ago
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Packages
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Python Packages
- lifelines
- scikit-survival - learn.
- PySurvival - validation, and prediction.
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R Packages
- survival - Meier curves, and Cox models.
- Cox model predictions
- Concordance in Survival Analysis
- dynpred
- pec
- Landmarking
- rstanarm
- JM - to-Event Data.
- JMbayes - to-event data, employing Bayesian methods with MCMC techniques
- randomForestSRC - SRC).
- LTRCforests - Term, Right-Censored longitudinal data using random forests, suitable for censored data in medical and reliability studies.
- rms
- survminer - ready outputs.
- survivalAnalysis - Level Interface for Survival Analysis and Associated Plots
- SurvMetrics
- icensBKL - corrected estimation for interval-censored data
- SmoothHazard - censored data
- bayesSurv
- survivalmodels - dependent covariates, multiple types of censoring, and complex survival models.
- survPresmooth
- cenROC - dependent receiver operating characteristic (ROC) curves with right-censored event time data.
- CRAN Task View: Survival Analysis - to-event data.
- gwasurvivr
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Julia Packages
- Survival.jl - Meier Estimator, Nelson-Aalen Estimator, Cox Proportional Hazards Model)
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Vignettes
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Julia Packages
- Competing Risks in Survival Analysis - state models and competing risks from the `survival` package.
- How to use the Landmarking package
- Joint Modeling using rstanarm
- Time-dependent covariates in survival analysis - dependent covariates in the context of survival analysis.
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Tutorials
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Julia Packages
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Papers
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Julia Packages
- Prognostic Factor Analysis using Survival Data
- Dynamic Predictions using Joint Modeling and Landmarking - Dependent Covariates in Survival Analysis using Joint Modeling and Landmarking.
- Concordance for Survival Time Data - dependent covariates, including methods for dealing with ties in predictor and event times.
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