Information-Theoretic Measures for Revealing Variable Interactions
infoxtr is an R package for analyzing variable interactions using information-theoretic measures. Originally tailored for time series, its methods extend seamlessly to spatial cross-sectional data. Powered by a pure C++ engine with a lightweight R interface, the package also exposes its headers for direct integration into other R packages.
Refer to the package documentation https://stscl.github.io/infoxtr/ for more detailed information.
- Install from CRAN with:
install.packages("infoxtr", dep = TRUE)- Install binary version from R-universe with:
install.packages("infoxtr",
repos = c("https://stscl.r-universe.dev",
"https://cloud.r-project.org"),
dep = TRUE)- Install from source code on GitHub with:
if (!requireNamespace("devtools")) {
install.packages("devtools")
}
devtools::install_github("stscl/infoxtr",
build_vignettes = TRUE,
dep = TRUE)Schreiber, T., 2000. Measuring Information Transfer. Physical Review Letters 85, 461–464. https://doi.org/10.1103/physrevlett.85.461.
Kraskov, A., Stogbauer, H., Grassberger, P., 2004. Estimating mutual information. Physical Review E 69. https://doi.org/10.1103/physreve.69.066138.
Martinez-Sanchez, A., Arranz, G., Lozano-Duran, A., 2024. Decomposing causality into its synergistic, unique, and redundant components. Nature Communications 15. https://doi.org/10.1038/s41467-024-53373-4.
Zhang, X., Chen, L., 2025. Quantifying interventional causality by knockoff operation. Science Advances 11. https://doi.org/10.1126/sciadv.adu6464.
Varley, T.F., 2025. Information theory for complex systems scientists: What, why, and how. Physics Reports 1148, 1–55. https://doi.org/10.1016/j.physrep.2025.09.007.