grangeRsearch

An R package for exhaustive Granger causality testing with tidyverse integration.

Overview

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:

• Exhaustive pairwise search: Automatically discover Granger-causal relationships across multiple variables
• Classic and constrained methods: Traditional VAR-based testing or single-lag constrained formulation
• GC strength measure: Continuous log-variance ratio quantifying causality magnitude
• Automatic lag optimization: Systematic evaluation of multiple lag orders with visualization
• Auto-differencing: Optional first-order differencing for stationarity preprocessing
• Distribution analysis: Analyze GC strength distributions across all variable pairs
• Tidyverse compatibility: Pipe operators (|>, %>%) and non-standard evaluation
• Broom integration: tidy() and glance() methods for structured output
• Visualization: Built-in plotting for causality matrices, distributions, and lag selection

Installation

Install from GitHub:

# install.packages("remotes")
# remotes::install_github("nkorf/grangersearch")
install.packages("grangersearch", repos = c('https://nkorf.r-universe.dev', 'https://cloud.r-project.org'))

Quick Start

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)

# Constrained Granger causality (single lag)
result_constrained <- Canada |>
  granger_causality_test(e, U, lag = 2, type = "constrained")

# With auto-differencing for non-stationary data
result_diff <- Canada |>
  granger_causality_test(e, U, lag = 2, difference = TRUE)

# Exhaustive search across multiple variables
search_results <- Canada |> granger_search(lag = 2)
plot(search_results)  # Causality matrix visualization

# Distribution analysis of GC strength
dist <- Canada |> granger_distribution(lag = 2)
plot(dist)  # Histogram of GC strength values

# Lag selection analysis
lag_analysis <- Canada |> granger_lag_select(e, U, lag = 1:8)
plot(lag_analysis)

Main Functions

Function	Description
granger_causality_test()	Test Granger causality between two time series
granger_search()	Exhaustive pairwise search across multiple variables
granger_distribution()	Analyze GC strength distribution across all pairs
granger_lag_select()	Analyze results across different lag orders
tidy() / glance()	Broom-style tidying of results

Example Output

Granger Causality Test (classic)
================================

Observations: 84, Lag: 2, Significance level: 0.050

e -> U: * e Granger-causes U (p = 0.0000, strength = 0.592)
U -> e:   U does not Granger-cause e (p = 0.2983, strength = 0.030)

CRAN Download Statistics

Citation

If you use this package, please cite:

Korfiatis, N. (2026). grangersearch: An R Package for Exhaustive Granger Causality Testing with Tidyverse Integration. arXiv preprint. https://arxiv.org/abs/2601.01604

Author

Nikolaos Korfiatis Department of Informatics, Faculty of Information Science and Informatics, Plateia Iatrou Tsirigoti 7, Ionian University GR 49100 Corfu, Greece nkorf@ionio.gr

License

MIT