README.md

tEDM tEDM website: https://stscl.github.io/tEDM/

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T*emporal Empirical Dynamic M*odeling

Overview

The tEDM package provides a suite of tools for exploring and quantifying causality in time series using Empirical Dynamic Modeling (EDM). It is particularly designed to detect, differentiate, and reconstruct causal dynamics in systems where traditional assumptions of linearity and stationarity may not hold.

The package implements four fundamental EDM-based methods:

Refer to the package documentation https://stscl.github.io/tEDM/ for more detailed information.

Installation

install.packages("tEDM", dep = TRUE)
install.packages("tEDM",
                 repos = c("https://stscl.r-universe.dev",
                           "https://cloud.r-project.org"),
                 dep = TRUE)
if (!requireNamespace("devtools")) {
    install.packages("devtools")
}
devtools::install_github("stscl/tEDM",
                         #build_vignettes = TRUE,
                         dep = TRUE)

Reference

Sugihara, G., May, R., Ye, H., Hsieh, C., Deyle, E., Fogarty, M., Munch, S., 2012. Detecting Causality in Complex Ecosystems. Science 338, 496–500. https://doi.org/10.1126/science.1227079.

Leng, S., Ma, H., Kurths, J., Lai, Y.-C., Lin, W., Aihara, K., Chen, L., 2020. Partial cross mapping eliminates indirect causal influences. Nature Communications 11. https://doi.org/10.1038/s41467-020-16238-0.

Tao, P., Wang, Q., Shi, J., Hao, X., Liu, X., Min, B., Zhang, Y., Li, C., Cui, H., Chen, L., 2023. Detecting dynamical causality by intersection cardinal concavity. Fundamental Research. https://doi.org/10.1016/j.fmre.2023.01.007.

Clark, A.T., Ye, H., Isbell, F., Deyle, E.R., Cowles, J., Tilman, G.D., Sugihara, G., 2015. Spatial convergent cross mapping to detect causal relationships from short time series. Ecology 96, 1174–1181. https://doi.org/10.1890/14-1479.1.



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tEDM documentation built on Aug. 25, 2025, 5:12 p.m.