Forecasting Tipping Points at the Community Level R package EWSmethods version 1.3.3 Rolling and expanding window approaches to assessing abundance based early warning signals, non-equilibrium resilience measures, and machine learning. See Dakos et al. (2012) <doi:10.1371/journal.pone.0041010>, Deb et al. (2022) <doi:10.1098/rsos.211475>, Drake and Griffen (2010) <doi:10.1038/nature09389>, Ushio et al. (2018) <doi:10.1038/nature25504> and Weinans et al. (2021) <doi:10.1038/s41598-021-87839-y> for methodological details. Graphical presentation of the outputs are also provided for clear and publishable figures. Visit the 'EWSmethods' website for more information, and tutorials. Software Depends: R (>= 4.4) Imports: curl, egg, ggplot2, gtools, forecast, foreach, infotheo, mAr, moments, rEDM (>= 1.15.0), reticulate, scales Suggests: data.table, devtools, doParallel, knitr, fs, parallel, rmarkdown, testthat (>= 3.0.0) Duncan O'Brien <duncan.a.obrien@gmail.com> Comprehensive R Archive Network (CRAN) Duncan O'Brien [aut, cre, cph] (ORCID: <https://orcid.org/0000-0002-3420-5210>), Smita Deb [aut] (ORCID: <https://orcid.org/0000-0001-7037-7055>), Sahil Sidheekh [aut], Narayanan Krishnan [aut], Partha Dutta [aut] (ORCID: <https://orcid.org/0000-0001-6067-1023>), Christopher Clements [aut] (ORCID: <https://orcid.org/0000-0001-5677-5401>) MIT + file LICENSE (https://CRAN.R-project.org/package=EWSmethods/LICENSE) 2026-03-30 application/tgz https://CRAN.R-project.org/package=EWSmethods doi:10.32614/CRAN.package.EWSmethods