Repository navigation
Add optional MeanRankTransformer for scikit-learn pipelines, cloning, cross-validation and named outputs. Named input columns are checked; changing the feature definition requires refitting. The base NumPy/SciPy functions remain unchanged.
25 package tests pass on Python 3.10, 3.12 and 3.14. Includes a synthetic pipeline example.
Install the wheel with python -m pip install hammer_pathways-0.2.0-py3-none-any.whl. For the adapter, then run python -m pip install "hammer-pathways[sklearn]==0.2.0" in the same environment. Verify the wheel with SHA256SUMS.
No models or datasets are included. This is feature engineering, not a clinical model or a guarantee against leakage. Gene-set selection and evaluation design remain the researcher's responsibility. Not published on PyPI.