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rollshap  

Rolling Shapley Values
View on CRAN: Click here


Download and install rollshap package within the R console
Install from CRAN:
install.packages("rollshap")

Install from Github:
library("remotes")
install_github("cran/rollshap")

Install by package version:
library("remotes")
install_version("rollshap", "1.0.1")



Attach the package and use:
library("rollshap")
Maintained by
Jason Foster
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-05-21
Latest Update: 2026-05-21
Description:
Analytical computation of rolling and expanding Shapley values for time-series data. The 'rollshap' package decomposes the coefficient of determination (R-squared) of a linear regression into nonnegative contributions from each explanatory variable using the Shapley value from cooperative game theory (Shapley, 1953, <doi:10.1515/9781400881970-018>). For each window, the exact Shapley value is computed by fitting all subsets of the explanatory variables and averaging the marginal contribution to R-squared across all orderings, which returns an order-invariant attribution that sums to the full-model R-squared. Use cases include variable importance, factor attribution, and feature selection in time-series regression. The package supports rolling and expanding windows, weights, and handling of missing values via 'min_obs', 'complete_obs', and 'na_restore' arguments. The implementation uses the online and offline algorithms from the 'roll' package to compute rolling and expanding cross-products efficiently with parallelism across columns and windows provided by 'RcppParallel'.
How to cite:
Jason Foster (2026). rollshap: Rolling Shapley Values. R package version 1.0.1, https://cran.r-project.org/web/packages/rollshap. Accessed 25 Oct. 2026.
Previous versions and publish date:
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Complete documentation for rollshap
Functions, R codes and Examples using the rollshap R package
Full rollshap package functions and examples
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