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Published June 23, 2026 | Version v1
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shapr: Prediction Explanation with Dependence-Aware Shapley Values

Authors/Creators

  • 1. ROR icon Norwegian Computing Center
  • 2. Indicio Technologies AB
  • 3. EDMO icon University of Oslo

Description

The shapr R package implements an enhanced version of the Kernel SHAP method for approximating Shapley values, with a strong focus on conditional Shapley values. The core idea is to remain completely model-agnostic while offering a variety of methods for estimating contribution functions, enabling accurate computation of conditional Shapley values across different feature types, dependencies, and distributions. The package also includes evaluation metrics to compare various approaches. With features like parallelized computations, convergence detection, progress updates, and extensive plotting options, shapr is a highly efficient and user-friendly tool, delivering precise estimates of conditional Shapley values, which are critical for understanding how features truly contribute to predictions.

Files

shapr-master.zip

Files (7.9 MB)

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Additional details

Software

Repository URL
https://github.com/NorskRegnesentral/shapr
Programming language
R
Development Status
Active