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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: |
1.0 |
| 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: |
2025-07-15 |
| 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:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=tEDM
to link to this page.
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.