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Temporal Empirical Dynamic Modeling
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:
Convergent Cross Mapping (CCM) – for detecting nonlinear causal relationships in time series.
Partial Cross Mapping (PCM) – for disentangling direct from indirect causal influences.
Cross Mapping Cardinality (CMC) – for identifying time-varying or state-dependent causal linkages.
Multispatial Convergent Cross Mapping (MultispatialCCM) – for reconstructing causal dynamics from replicated time series across multiple spatial locations.
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)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.
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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