Efficient MCMC algorithm for linear regression models that makes use of 'spike-and-slab' priors for some modest regularization on the amount of posterior probability for a subset of the coefficients.

## Usage

```
engine_breg(
x,
iter = 10000,
nthread = getOption("ibis.nthread"),
type = "response",
...
)
```

## Arguments

- x
`distribution()`

(i.e.`BiodiversityDistribution`

) object.- iter
`numeric`

on the number of MCMC iterations to run (Default:`10000`

).- nthread
`numeric`

on the number of CPU-threads to use for data augmentation.- type
The mode used for creating posterior predictions. Either making

`"link"`

or`"response"`

(Default:`"response"`

).- ...
Other none specified parameters passed on to the model.

## Value

An Engine.

## Details

This engine provides efficient Bayesian predictions through the
Boom R-package. However note that not all link and models functions are
supported and certain functionalities such as offsets are generally not
available. This engines allows the estimation of linear and non-linear
effects via the `"only_linear"`

option specified in train.

## References

Nguyen, K., Le, T., Nguyen, V., Nguyen, T., & Phung, D. (2016, November). Multiple kernel learning with data augmentation. In Asian Conference on Machine Learning (pp. 49-64). PMLR.

Steven L. Scott (2021). BoomSpikeSlab: MCMC for Spike and Slab Regression. R package version 1.2.4. https://CRAN.R-project.org/package=BoomSpikeSlab

## See also

Other engine:
`engine_bart()`

,
`engine_gdb()`

,
`engine_glmnet()`

,
`engine_inlabru()`

,
`engine_inla()`

,
`engine_stan()`

,
`engine_xgboost()`

## Examples

```
if (FALSE) {
# Add BREG as an engine
x <- distribution(background) |> engine_breg(iter = 1000)
}
```