Wrapper of Python Library 'shap'
Overview
Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) arXiv:1705.07874 The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'.
idparam forplot.individual_variable_effectnow works as intended. Thanks (#27)- Removed duplicated functions from
DALEX shappernow importsDALEX
From NEWS.md in the release
Install
Health
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Szymon Maksymiuk <sz.maksymiuk@gmail.com>’ The Description field contains Lundberg, S., et al., (2016) <arXiv:1705.07874> The SHAP method is used Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
- NOTE11 OK, 2 NOTE2026-06-09
- WARNING11 OK, 1 NOTE, 1 WARNING2026-06-08
- NOTE9 OK, 5 NOTE2026-03-10
Documentation
- Help pages
- 5
- Examples that run
- 75%3 run in a plain check · 1 never run
- Exported functions with examples
- 67%
- Documented parameters
- 83%
- Return-value docs
- 67%
- Help pages citing references
- 0%
Downloads
Repository
- PRs merged (all time)
- 69%
- Median days to close, 50 most recently updated closed issues
- 82
- Median days to close, 50 most recently updated closed PRs
- 0
Stars over time
Forks over time
Issues over time
PRs over time
6 development-tooling and community-health practices detected across 5 families in the upstream repository
Checks run against github.com/modeloriented/shapper on 2026-10-21.
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Dependencies
Installing shapper adds 39 packages (15 with compiled code, 25 MB of source tarballs). Heaviest: ggplot2 6.0 MB, ingredients 2.4 MB, Rcpp 1.8 MB.
Code & Tests
People & History
Previously maintained by Alicja Gosiewska (0.1.0).
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 0.1.3Latest
- RR 4.0.0 released · 2020-04-24
- 0.1.22019-08-02 · diff ↗
- 0.1.1
- RR 3.6.0 released · 2019-04-26
- 0.1.02019-03-02
- RR 3.5.0 released · 2018-04-23
Package metadata
- First published
- 2019-03-02
- Total releases
- 4 / 7 yrs
- License
- GPL
- Download size
- 39 KB
Cite
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