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.
Version: 1.3.1 Depends: R (≥ 4.4) Imports: curl, egg, ggplot2, gtools, forecast, foreach, infotheo, mAr, moments, rEDM (≥ 1.15.0), reticulate, scales, zoo Suggests: devtools, doParallel, knitr, fs, parallel, rmarkdown, testthat (≥ 3.0.0) Published: 2024-05-15 DOI: 10.32614/CRAN.package.EWSmethods Author: Duncan O'Brien [aut, cre, cph], Smita Deb [aut], Sahil Sidheekh [aut], Narayanan Krishnan [aut], Partha Dutta [aut], Christopher Clements [aut] Maintainer: Duncan O'Brien <duncan.a.obrien at gmail.com> BugReports: https://github.com/duncanobrien/EWSmethods/issues License: MIT + file LICENSE URL: https://github.com/duncanobrien/EWSmethods, https://duncanobrien.github.io/EWSmethods/ NeedsCompilation: no Citation: EWSmethods citation info Materials: README NEWS CRAN checks: EWSmethods results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=EWSmethods to link to this page.
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