CRAN/E | EWSmethods

EWSmethods

Forecasting Tipping Points at the Community Level

Installation

About

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.

Citation EWSmethods citation info
github.com/duncanobrien/EWSmethods
duncanobrien.github.io/EWSmethods/
Bug report File report

Key Metrics

Version 1.2.5
R ≥ 3.5
Published 2024-01-11 112 days ago
Needs compilation? no
License MIT
License File
CRAN checks EWSmethods results

Downloads

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Maintainer

Maintainer

Duncan O'Brien

duncan.a.obrien@gmail.com

Authors

Duncan O'Brien

aut / cre / cph

Smita Deb

aut

Sahil Sidheekh

aut

Narayanan Krishnan

aut

Partha Dutta

aut

Christopher Clements

aut

Material

README
NEWS
Reference manual
Package source

Vignettes

Performing Early Warning Signal Assessments

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

EWSmethods archive

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