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

CRANCRAN ReleaseCRAN ChecksDownloads_allDownloads_monthLicenseR-CMD-checkLifecycle: stableR-universe

TemporalEmpiricalDynamicModeling

Overview

ThetEDM package provides a suite of tools for exploringand quantifying causality in time series using Empirical DynamicModeling (EDM). It is particularly designed to detect, differentiate,and reconstruct causal dynamics in systems where traditional assumptionsof linearity and stationarity may not hold.

The package implements four fundamental EDM-based methods:

Refer to the package documentationhttps://stscl.github.io/tEDM/ for more detailedinformation.

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 byintersection 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 detectcausal relationships from short time series. Ecology 96, 1174–1181.https://doi.org/10.1890/14-1479.1.

 


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