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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.
Getting started
Browse package contents
Package details |
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| Author | Duncan O'Brien [aut, cre, cph] (<https://orcid.org/0000-0002-3420-5210>), Smita Deb [aut] (<https://orcid.org/0000-0001-7037-7055>), Sahil Sidheekh [aut], Narayanan Krishnan [aut], Partha Dutta [aut] (<https://orcid.org/0000-0001-6067-1023>), Christopher Clements [aut] (<https://orcid.org/0000-0001-5677-5401>) |
| Maintainer | Duncan O'Brien <duncan.a.obrien@gmail.com> |
| License | MIT + file LICENSE |
| Version | 1.3.1 |
| URL | https://github.com/duncanobrien/EWSmethods https://duncanobrien.github.io/EWSmethods/ |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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