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An R package for exhaustive Granger causality testing with tidyverse integration.
grangersearch provides a simple interface for performing
Granger causality tests on time series data. The package wraps the
vars infrastructure while providing a streamlined interface
for exploratory causal analysis.
Key features include:
|>, %>%) and non-standard
evaluationtidy() and
glance() methods for structured outputInstall from GitHub:
# install.packages("remotes")
remotes::install_github("nkorf/grangersearch")library(grangersearch)
# Basic pairwise test
data(Canada, package = "vars")
result <- Canada |> granger_causality_test(e, U, lag = 2)
print(result)
# Get tidy results
tidy(result)
# Exhaustive search across multiple variables
search_results <- Canada |> granger_search(lag = 2)
plot(search_results) # Causality matrix visualization
# Lag selection analysis
lag_analysis <- Canada |> granger_lag_select(e, U, lag = 1:8)
plot(lag_analysis)| Function | Description |
|---|---|
granger_causality_test() |
Test Granger causality between two time series |
granger_search() |
Exhaustive pairwise search across multiple variables |
granger_lag_select() |
Analyze results across different lag orders |
tidy() / glance() |
Broom-style tidying of results |
Granger Causality Test
======================
Observations: 84, Lag order: 2, Significance level: 0.050
e -> U: e Granger-causes U (p = 0.0000)
U -> e: U does not Granger-cause e (p = 0.2983)
If you use this package, please cite:
Korfiatis, N. (2025). grangersearch: An R Package for Exhaustive Granger Causality Testing with Tidyverse Integration. arXiv preprint. https://arxiv.org/abs/XXXX.XXXXX
Nikolaos Korfiatis Department of Informatics, Ionian University Corfu, Greece nkorf@ionio.gr
MIT
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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