This approach is described in Yao et al. (2018) doi:10.1214/17-BA1091 . The general method minimizes (or maximizes) some quantity across models. For simple models with normal error, this may be the root mean squared error (RMSE), but other approaches include the log score. We adopt the latter here, where log scores are used to generate the stacking of predictive distributions

## Arguments

- model_list
A list of models fit with

`sdmTMB_cv()`

to generate estimates of predictive densities. You will want to set the seed to the same value before fitting each model or manually construct the fold IDs so that they are the same across models.- include_folds
An optional numeric vector specifying which folds to include in the calculations. For example, if 5 folds are used for k-fold cross validation, and the first 4 are needed to generate these weights,

`include_folds = 1:4`

.

## References

Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. 2018. Using Stacking to Average Bayesian Predictive Distributions (with Discussion). Bayesian Analysis 13(3): 917–1007. International Society for Bayesian Analysis. doi:10.1214/17-BA1091

## Examples

```
# Set parallel processing if desired. See 'Details' in ?sdmTMB_cv
# Depth as quadratic:
set.seed(1)
m_cv_1 <- sdmTMB_cv(
density ~ 0 + depth_scaled + depth_scaled2,
data = pcod_2011, mesh = pcod_mesh_2011,
family = tweedie(link = "log"), k_folds = 2
)
#> Running fits with `future.apply()`.
#> Set a parallel `future::plan()` to use parallel processing.
# Depth as linear:
set.seed(1)
m_cv_2 <- sdmTMB_cv(
density ~ 0 + depth_scaled,
data = pcod_2011, mesh = pcod_mesh_2011,
family = tweedie(link = "log"), k_folds = 2
)
#> Running fits with `future.apply()`.
#> Set a parallel `future::plan()` to use parallel processing.
# Only an intercept:
set.seed(1)
m_cv_3 <- sdmTMB_cv(
density ~ 1,
data = pcod_2011, mesh = pcod_mesh_2011,
family = tweedie(link = "log"), k_folds = 2
)
#> Running fits with `future.apply()`.
#> Set a parallel `future::plan()` to use parallel processing.
models <- list(m_cv_1, m_cv_2, m_cv_3)
weights <- sdmTMB_stacking(models)
weights
#> [1] 0.9038042 0.0182349 0.0779609
```