EWSmethods: an R package to forecast tipping points at the community level using early warning signals, resilience measures, and machine learning models

Early warning signals (EWSs) represent a potentially universal tool for identifying whether a system is approaching a tipping point, and have been applied in fields including ecology, epidemiology, economics, and physics. This potential universality has led to the development of a suite of computational approaches aimed at improving the reliability of these methods. Classic methods based on univariate data have a long history of use, but recent theoretical advances have expanded EWSs to multivariate datasets, particularly relevant given advancements in remote sensing. More recently, novel machine learning approaches have been developed but have not been made accessible in the R () environment. Here, we present EWSmethods - an R package () that provides a unified syntax and interpretation of the most popular and cutting edge EWSs methods applicable to both univariate and multivariate time series. EWSmethods provides two primary functions for univariate and multivariate systems respectively, with two forms of calculation available for each: classical rolling window time series analysis, and the more robust expanding window. It also provides an interface to the Python machine learning model EWSNet which predicts the probability of a sudden tipping point or a smooth transition, the first of its form available to R () users. This note details the rationale for this open-source package and delivers an introduction to its functionality for assessing resilience. We have also provided vignettes and an external website to act as further tutorials and FAQs.

Automated summary

Early warning signals (EWSs) are a potentially universal tool for identifying system tipping points, and have been applied in various fields. Recent theoretical advances have expanded EWSs to multivariate datasets, and novel machine learning approaches have also been developed. This R package provides a unified syntax for analyzing both univariate and multivariate time series data, and includes access to a Python machine learning model for predicting tipping points.