The `engine_glmnet`

engine does not support priors in a
typical sense, however it is possible to specify so called penalty factors as
well as lower and upper limits on all variables in the model.

The default penalty multiplier is `1`

for each coefficient X covariate,
i.e. coefficients are penalized equally and then informed by an intersection
of any absence information with the covariates. In contrast a variable with
penalty.factor equal to `0`

is not penalized at all.

In addition, it is possible to specifiy a lower and upper limit for specific
coefficients, which constrain them to a certain range. By default those
ranges are set to `-Inf`

and `Inf`

respectively, but can be reset
to a specific value range by altering `"lims"`

(see examples).

For a regularized regression that supports a few more options on the priors,
check out the Bayesian `engine_breg`

.

## Arguments

- variable
A

`character`

variable passed on to the prior object.- hyper
A

`numeric`

value between`0`

and`1`

that state the penalization factor. By default this is set to`0`

, implying the`"variable"`

provided is not regularized at all.- lims
A

`numeric`

`vector`

of the lower and upper limits for each coefficient (Default:`c(-Inf, Inf)`

).- ...
Variables passed on to prior object.

## See also

Other prior:
`BARTPrior()`

,
`BARTPriors()`

,
`BREGPrior()`

,
`BREGPriors()`

,
`GDBPrior()`

,
`GDBPriors()`

,
`GLMNETPriors()`

,
`INLAPrior()`

,
`INLAPriors()`

,
`STANPrior()`

,
`STANPriors()`

,
`XGBPrior()`

,
`XGBPriors()`

,
`add_priors()`

,
`get_priors()`

,
`priors()`

,
`rm_priors()`

## Examples

```
if (FALSE) {
# Retain variable
p1 <- GLMNETPrior(variable = "forest", hyper = 0)
p1
# Smaller chance to be regularized
p2 <- GLMNETPrior(variable = "forest", hyper = 0.2, lims = c(0, Inf))
p2
}
```