This engine allows the estimation of linear coefficients using
either ridge, lasso or elastic net regressions techniques. Backbone of this
engine is the glmnet R-package which is commonly used in SDMs,
including the popular `'maxnet'`

(e.g. Maxent) package. Ultimately this
engine is an equivalent of engine_breg, but in a "frequentist" setting. If
user aim to emulate a model that most closely resembles maxent within the
ibis.iSDM modelling framework, then this package is the best way of doing so.
Compared to the `'maxnet'`

R-package, a number of efficiency settings
are implemented in particular for cross-validation of alpha and lambda
values.

Limited amount of prior information can be specified for this engine,
specifically via offsets or as `GLMNETPrior`

, which allow to specify priors
as regularization constants.

## Arguments

- x
`distribution()`

(i.e.`BiodiversityDistribution`

) object.- alpha
A

`numeric`

giving the elasticnet mixing parameter, which has to be between`0`

and`1`

.`alpha=1`

is the lasso penalty, and`alpha=0`

the ridge penalty (Default:`0`

).- nlambda
A

`numeric`

giving the number of lambda values to be used (Default:`100`

).- lambda
A

`numeric`

with a user supplied estimate of lambda. Usually best to let this parameter be determined deterministically (Default:`NULL`

).- type
The mode used for creating posterior predictions. Either making

`"link"`

or`"response"`

(Default:`"response"`

).- ...
Other parameters passed on to glmnet.

## Value

An Engine.

## Details

Regularized regressions are effectively GLMs that are fitted with
ridge, lasso or elastic-net regularization. Which of them is chosen is
critical dependent on the alpha value: * For `alpha`

equal to `0`

a ridge regularization is used. Ridge regularization has the property that it
doesn't remove variables entirely, but instead sets their coefficients to
`0`

. * For `alpha`

equal to `1`

a lasso regularization is
used. Lassos tend to remove those coefficients fully from the final model
that do not improve the loss function. * For `alpha`

values between
`0`

and `1`

a elastic-net regularization is used, which is
essentially a combination of the two. The optimal lambda parameter can be
determined via cross-validation. For this option set `"varsel"`

in
`train()`

to `"reg"`

.

## References

Jerome Friedman, Trevor Hastie, Robert Tibshirani (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22. URL https://www.jstatsoft.org/v33/i01/.

Renner, I.W., Elith, J., Baddeley, A., Fithian, W., Hastie, T., Phillips, S.J., Popovic, G. and Warton, D.I., 2015. Point process models for presence‐only analysis. Methods in Ecology and Evolution, 6(4), pp.366-379.

Fithian, W. & Hastie, T. (2013) Finite-sample equivalence in statistical models for presence-only data. The Annals of Applied Statistics 7, 1917–1939

## See also

Other engine:
`engine_bart()`

,
`engine_breg()`

,
`engine_gdb()`

,
`engine_glm()`

,
`engine_inla()`

,
`engine_inlabru()`

,
`engine_scampr()`

,
`engine_stan()`

,
`engine_xgboost()`

## Examples

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