R: Forecasting Tipping Points at the Community Level

# Forecasting Tipping Points at the Community Level

## 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