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shapr: Prediction Explanation with Dependence-Aware Shapley Values

Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements the method described in Aas, Jullum and Løland (2019) <doi:10.48550/arXiv.1903.10464>, which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values.

Version: 0.2.2
Depends: R (≥ 3.5.0)
Imports: stats, data.table, Rcpp (≥ 0.12.15), condMVNorm, mvnfast, Matrix
LinkingTo: RcppArmadillo, Rcpp
Suggests: ranger, xgboost, mgcv, testthat, knitr, rmarkdown, roxygen2, MASS, ggplot2, caret, gbm, party, partykit
Published: 2023-05-04
DOI: 10.32614/CRAN.package.shapr
Author: Nikolai Sellereite ORCID iD [aut], Martin Jullum ORCID iD [cre, aut], Annabelle Redelmeier [aut], Anders Løland [ctb], Jens Christian Wahl [ctb], Camilla Lingjærde [ctb], Norsk Regnesentral [cph, fnd]
Maintainer: Martin Jullum <Martin.Jullum at nr.no>
BugReports: https://github.com/NorskRegnesentral/shapr/issues
License: MIT + file LICENSE
URL: https://norskregnesentral.github.io/shapr/, https://github.com/NorskRegnesentral/shapr
NeedsCompilation: yes
Language: en-US
Materials: README NEWS
In views: MachineLearning
CRAN checks: shapr results

Documentation:

Reference manual: shapr.pdf
Vignettes: 'shapr': Explaining individual machine learning predictions with Shapley values

Downloads:

Package source: shapr_0.2.2.tar.gz
Windows binaries: r-devel: shapr_0.2.2.zip, r-release: shapr_0.2.2.zip, r-oldrel: shapr_0.2.2.zip
macOS binaries: r-release (arm64): shapr_0.2.2.tgz, r-oldrel (arm64): shapr_0.2.2.tgz, r-release (x86_64): shapr_0.2.2.tgz, r-oldrel (x86_64): shapr_0.2.2.tgz
Old sources: shapr archive

Reverse dependencies:

Reverse imports: PPtreeregViz, SEMdeep

Linking:

Please use the canonical form https://CRAN.R-project.org/package=shapr to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.