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Use cases include variable importance, factor\nattribution, and feature selection in time-series regression.\nThe package supports rolling and expanding windows, weights,\nand handling of missing values via 'min_obs', 'complete_obs',\nand 'na_restore' arguments. The implementation uses the online\nand offline algorithms from the 'roll' package to compute\nrolling and expanding cross-products efficiently with\nparallelism across columns and windows provided by\n'RcppParallel'.", "License": "GPL (>= 2)", "URL": "https://github.com/jasonjfoster/rollshap", "BugReports": "https://github.com/jasonjfoster/rollshap/issues", "SystemRequirements": "GNU make", "Encoding": "UTF-8", "Config/roxygen2/old_usage": "TRUE", "Config/roxygen2/version": "8.0.0", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-29 10:15:10 UTC", "User": "root" }, "Author": "Jason Foster [aut, cre]", "Maintainer": "Jason Foster ", "Config/pak/sysreqs": "make", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-05-21 09:10:02 UTC", "RemoteUrl": "https://github.com/cran/rollshap", "RemoteRef": "HEAD", "RemoteSha": "ccb6c1bed9d606384b1435bd0d3ee491fd15f0a0", "_user": "cran", "_type": "src", "_file": "rollshap_1.0.1.tar.gz", "_fileid": 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