scikit-survival and dev-survivors
Scikit-survival is a mature, production-ready survival analysis framework integrated with scikit-learn's ecosystem, while dev-survivors is an experimental interpretability-focused library—they are **competitors** offering alternative approaches to the same problem domain, though scikit-survival is vastly more established and widely adopted.
About scikit-survival
sebp/scikit-survival
Survival analysis built on top of scikit-learn
Implements specialized models that account for censored data—where event outcomes are partially unknown—enabling time-to-event predictions in clinical and reliability domains. Integrates seamlessly with scikit-learn's preprocessing, cross-validation, and pipeline infrastructure while supporting both uncensored and right-censored observations. Provides multiple survival model variants optimized through convex solvers (ECOS, OSQP) with dependencies on NumPy, SciPy, and pandas for numerical computation.
About dev-survivors
iuliivasilev/dev-survivors
Stay Alive. A Reliable and Interpretable Survival Analysis Library
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