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Author:
None
Summary:
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.
Latest version:
0.7.0
Required dependencies:
joblib
|
numba
|
numpy
|
pandas
|
scikit-learn
|
scipy
Optional dependencies:
cupy-cuda12x
|
hypothesis
|
marimo
|
matplotlib
|
mkdocs-material
|
mkdocstrings
|
nvidia-cuda-nvrtc-cu12
|
nvidia-cuda-runtime-cu12
|
pre-commit
|
pyarrow
|
pytest
|
pytest-cov
|
rpy2
|
ruff
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