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 |
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