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 methods which accounts for any feature
dependence, and thereby produces more accurate estimates of the true Shapley values.
An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repository.
| Version: |
1.0.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
stats, data.table, Rcpp (≥ 0.12.15), Matrix, future.apply, methods |
| LinkingTo: |
RcppArmadillo, Rcpp |
| Suggests: |
ranger, xgboost, mgcv, testthat (≥ 3.0.0), knitr, rmarkdown, roxygen2, ggplot2, gbm, party, partykit, waldo, progressr, future, ggbeeswarm, vdiffr, forecast, torch, GGally, progress, coro, parsnip, recipes, workflows, tune, dials, yardstick, hardhat, rsample, rlang, cli |
| Published: |
2025-01-16 |
| DOI: |
10.32614/CRAN.package.shapr |
| Author: |
Martin Jullum 
[cre, aut],
Lars Henry Berge Olsen
[aut],
Annabelle Redelmeier [aut],
Jon Lachmann
[aut],
Nikolai Sellereite
[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 |
| Citation: |
shapr citation info |
| Materials: |
README NEWS |
| In views: |
MachineLearning |
| CRAN checks: |
shapr results [issues need fixing before 2025-01-30] |