Forecasting Tipping Points at the Community Level

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Documentation for package ‘EWSmethods’ version 1.3.3

• DESCRIPTION file
• User guides, package vignettes and other documentation
• Package NEWS
• Package README

Help Pages

CODrecovery	Three Recovering Cod Populations
conda_clean	Python Removal
default_sewsnet_path	Path to S-EWSNet Model
default_weights_path	Path to Model Weights
deseason_ts	Deseason Seasonal Time Series
detrend_ts	Detrend Time Series
embed_ts	Construct an Embedded Timeseries
ewsnet_finetune	EWSNet Finetune
ewsnet_init	EWSNet Initialisation
ewsnet_predict	EWSNet Predict
ewsnet_reset	Reset EWSNet Model Weights
FI	Calculate Fisher Information
II	Information Imbalance
imbalance_gain	Information Gain
multiAR	Multivariate Jacobian Index Estimated From a Multivariate Autocorrelation Matrix
multiEWS	Multivariate Early Warning Signal Assessment
multiJI	Multivariate S-map Jacobian index function
multi_smap_jacobian	Multivariate S-map Inferred Jacobian
mvi	Multivariate Variance Index function
perm_rollEWS	Significance Testing of Rolling Window Early Warning Signals
plot.EWSmethods	Plot an EWSmethods object
sewsnet_predict	S-EWSNet Predict
sewsnet_reset	Reset S-EWSNet Model
simTransComms	Three Simulated Transitioning Communities.
tuneII	Information Imbalance Across Alphas
uniAR	Univariate Jacobian Index Estimated From an Univariate Autocorrelation Matrix
uniEWS	Univariate Early Warning Signal Assessment
uniJI	Univariate S-map Jacobian index function
uni_smap_jacobian	Univariate S-map Inferred Jacobian