smoothstate
Fast, dependency-light smoothing of state probabilities for Python, built for Polars.
smoothstate provides focused smoothing primitives for model-evaluation workflows. It was created to support the smooth calibration machinery in rtichoke without requiring a general-purpose survival-modeling stack at runtime.
The package currently includes:
smooth_binary_state()for smoothing binary state probabilities over a continuous predictor.smooth_state_cox()for time-dependent smoothing with a complementary log-log transformed probability, a 3-knot restricted cubic spline, and a secondary Cox model.
The Cox implementation is continuously validated against Python lifelines and R survival::coxph(). In the current GitHub Actions benchmark it reproduces the reference curves to numerical precision while running roughly 26–39× faster than lifelines for this deliberately narrow workload.
Installation
uv add smoothstate
Until the first PyPI release is published, install directly from GitHub:
uv add git+https://github.com/uriahf/smoothstate
Documentation
See the Great Docs site for the user guide, implementation details, benchmarks, and API reference.
1 maintainer
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