Forecasting Tipping Points at the Community Level
Python Removal
Path to Model Weights
Deseason Seasonal Time Series
Detrend Time Series
Construct an Embedded Timeseries
EWSNet Finetune
EWSNet Initialisation
EWSNet Predict
Reset EWSNet Model Weights
Calculate Fisher Information
Information Imbalance
Information Gain
Multivariate S-map Inferred Jacobian
Multivariate Jacobian Index Estimated From Multivariate Autocorrelatio...
Multivariate Early Warning Signal Assessment
Multivariate S-map Jacobian index function
Multivariate Variance Index function
Significance Testing of Rolling Window Early Warning Signals
Plot an EWSmethods object
Information Imbalance Across Alphas
Univariate S-map Inferred Jacobian
Univariate Jacobian Index Estimated From Univariate Autocorrelation Ma...
Univariate Early Warning Signal Assessment
Univariate S-map Jacobian index function
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.
Useful links