scikit-survival and survhive
Scikit-survival provides the foundational statistical and machine learning survival analysis implementations that survhive wraps with convenience abstractions and deep learning extensions, making them complements rather than direct competitors.
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 survhive
compbiomed-unito/survhive
Convenient, opinionated wrapper around some (deep) survival models
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