Temporal Empirical Dynamic Modeling R package tEDM version 1.3 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 introduced by Leng et al. (2020) <doi:10.1038/s41467-020-16238-0>, and cross mapping cardinality described in Tao et al. (2023) <doi:10.1016/j.fmre.2023.01.007>, following a systematic description proposed in Lyu et al. (2026) <doi:10.1016/j.compenvurbsys.2026.102435>. Software 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 Wenbo Lyu <lyu.geosocial@gmail.com> Comprehensive R Archive Network (CRAN) Wenbo Lyu [aut, cre, cph] (ORCID: <https://orcid.org/0009-0002-6003-3800>) GPL-3 2026-03-30 application/tgz https://CRAN.R-project.org/package=tEDM doi:10.32614/CRAN.package.tEDM