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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "rollshap" in publications use:'
type: software
license: GPL-2.0-or-later
title: 'rollshap: Rolling Shapley Values'
version: 1.0.1
abstract: 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, <https://doi.org/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'.
authors:
- family-names: Foster
  given-names: Jason
  email: jason.j.foster@gmail.com
repository: https://cran.r-universe.dev
repository-code: https://github.com/jasonjfoster/rollshap
commit: ccb6c1bed9d606384b1435bd0d3ee491fd15f0a0
url: https://github.com/jasonjfoster/rollshap
date-released: '2026-05-21'
contact:
- family-names: Foster
  given-names: Jason
  email: jason.j.foster@gmail.com