tEDM: Temporal Empirical Dynamic Modeling
Inferring causation from time series data through empirical dynamic modeling (EDM), with methods such as convergent cross mapping from Sugihara et al. (2012) <doi:10.1126/science.1227079>, partial cross mapping as outlined in Leng et al. (2020) <doi:10.1038/s41467-020-16238-0>, and cross mapping cardinality as described in Tao et al. (2023) <doi:10.1016/j.fmre.2023.01.007>.
| Version: |
1.1 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
dplyr, ggplot2, methods, Rcpp |
| LinkingTo: |
Rcpp, RcppThread, RcppArmadillo |
| Suggests: |
RcppThread, RcppArmadillo, readr, plot3D, spEDM, knitr, rmarkdown, purrr, tidyr, cowplot |
| Published: |
2025-08-25 |
| DOI: |
10.32614/CRAN.package.tEDM |
| Author: |
Wenbo Lv  [aut,
cre, cph] |
| Maintainer: |
Wenbo Lv <lyu.geosocial at gmail.com> |
| BugReports: |
https://github.com/stscl/tEDM/issues |
| License: |
GPL-3 |
| URL: |
https://stscl.github.io/tEDM/, https://github.com/stscl/tEDM |
| NeedsCompilation: |
yes |
| Materials: |
README, NEWS |
| CRAN checks: |
tEDM results |
Documentation:
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