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
Author:	Nikolai Sellereite [aut], Martin Jullum [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

Linking:

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