Forecasting Tipping Points at the Community Level [R package EWSmethods version 1.3.1]

EWSmethods: Forecasting Tipping Points at the Community Level

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年05月15日

DOI: 10.32614/CRAN.package.EWSmethods

Author: Duncan O'Brien ORCID iD [aut, cre, cph], Smita Deb ORCID iD [aut], Sahil Sidheekh [aut], Narayanan Krishnan [aut], Partha Dutta ORCID iD [aut], Christopher Clements ORCID iD [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