EWSmethods: Forecasting Tipping Points at the Community Level (original) (raw)

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
URL:	https://github.com/duncanobrien/EWSmethods,https://duncanobrien.github.io/EWSmethods/
NeedsCompilation:	no
Citation:	EWSmethods citation info
Materials:	README, NEWS
CRAN checks:	EWSmethods results

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