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: 1.2.5
Depends: R (≥ 3.5)
Imports: curl, dplyr (≥ 1.0.6), egg, ggplot2, gtools, forecast, foreach, infotheo, mAr, moments, rEDM (≥ 1.15.0), reticulate, scales, tidyr, zoo
Suggests: devtools, doParallel, knitr, fs, parallel, rmarkdown, testthat (≥ 3.0.0)
Published: 2024-01-11
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


Reference manual: EWSmethods.pdf
Vignettes: Performing Early Warning Signal Assessments


Package source: EWSmethods_1.2.5.tar.gz
Windows binaries: r-devel: EWSmethods_1.2.5.zip, r-release: EWSmethods_1.2.5.zip, r-oldrel: EWSmethods_1.2.5.zip
macOS binaries: r-release (arm64): EWSmethods_1.2.5.tgz, r-oldrel (arm64): EWSmethods_1.2.5.tgz, r-release (x86_64): EWSmethods_1.2.5.tgz
Old sources: EWSmethods archive


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