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comprisk

Python toolkit for competing risks: forest (RSF) + (penalized) Fine-Gray subdistribution regression + Aalen-Johansen cumulative incidence + Gray's K-sample test + cause-specific Cox. Scales to n=10⁶ in ~1 min, 10–22× faster than randomForestSRC on real EHR data, scikit-learn-compatible.

Ecosystem
pypi.org
Latest Release
0.8.0
2 months ago
Versions
6
Downloads
2,884 last month
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    Readme
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    Links
    Registry pypi.org
    Source Repository
    Docs Documentation
    JSON API View JSON
    CodeMeta codemeta.json
    Package Details
    PURL pkg:pypi/comprisk
    spec
    License Apache-2.0
    First Release 6 months ago
    Last Synced about 1 month ago
    Keywords
    biostatistics competing-risks machine-learning random-forest survival-analysis numba python random-survival-forest scikit-learn
    Repository
    Stars 9 on GitHub
    Forks 0 on GitHub
    repos.ecosyste.ms