Communicates with EWSNet (https://ewsnet.github.io), a deep learning framework for modelling and anticipating regime shifts in dynamical systems, and returns the model's prediction for the inputted univariate time series.

## Usage

```
ewsnet_predict(
x,
scaling = TRUE,
ensemble = 25,
envname,
weights_path = default_weights_path()
)
```

## Arguments

- x
A numeric vector of values to be tested.

- scaling
Boolean. If

`TRUE`

, the time series will be scaled between 1 and 2 and scaled EWSNet model weights will be used. This is the recommended setting.- ensemble
A numeric value stating the number of models to average over. Options range from 1 to 25.

- envname
A string naming the Python environment prepared by

`ewsnet_init()`

.- weights_path
A string naming the path to model weights installed by

`ewsnet_reset()`

.

## Value

A dataframe of EWSNet predictions. Values represent the estimated probability that the quoted event will occur.

## Examples

```
#A dummy dataset of a hedgerow bird population
#monitored over 50 years.
abundance_data <- data.frame(time = seq(1:50),
abundance = rnorm(50,mean = 20))
#Activate python environment (only necessary
#on first opening of R session).
if (FALSE) {
ewsnet_init(envname = "EWSNET_env")
}
#Generate EWSNet predictions.
if (FALSE) {
pred <- ewsnet_predict(
abundance_data$abundance,
scaling = TRUE,
ensemble = 15,
envname = "EWSNET_env")
}
```