scikit-survival and pysurvival
These are competitors offering overlapping survival analysis functionality, with scikit-survival being the more mature and actively maintained option, while pysurvival appears to be unmaintained (evidenced by zero monthly downloads).
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 pysurvival
square/pysurvival
Open source package for Survival Analysis modeling
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