{"ID":2822929,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.01604","arxiv_id":"2601.01604","title":"grangersearch: An R Package for Exhaustive Granger Causality Testing with Tidyverse Integration","abstract":"This paper introduces grangersearch, an R package for performing exhaustive Granger causality searches on multiple time series. The package provides: (1) exhaustive pairwise search across multiple variables, (2) automatic lag order optimization with visualization, (3) tidyverse-compatible syntax with pipe operators and non-standard evaluation, and (4) integration with the broom ecosystem through tidy() and glance() methods. The package wraps the vars infrastructure while providing a simple interface for exploratory causal analysis. We describe the statistical methodology, demonstrate the package through worked examples, and discuss practical considerations for applied researchers.","short_abstract":"This paper introduces grangersearch, an R package for performing exhaustive Granger causality searches on multiple time series. The package provides: (1) exhaustive pairwise search across multiple variables, (2) automatic lag order optimization with visualization, (3) tidyverse-compatible syntax with pipe operators and...","url_abs":"https://arxiv.org/abs/2601.01604","url_pdf":"https://arxiv.org/pdf/2601.01604v1","authors":"[\"Nikolaos Korfiatis\"]","published":"2026-01-04T17:06:18Z","proceeding":"stat.CO","tasks":"[\"stat.CO\",\"stat.ML\"]","methods":"[\"LoRA\"]","has_code":false}