Definition of the initial number of boosting iterations, step size and other hyper-parameters for boosting algorithms.

```
boost_control(mstop = 100, nu = 0.1,
risk = c("inbag", "oobag", "none"), stopintern = FALSE,
center = TRUE, trace = FALSE)
```

mstop

an integer giving the number of initial boosting iterations.
If `mstop = 0`

, the offset model is returned.

nu

a double (between 0 and 1) defining the step size or shrinkage parameter.
The default is probably too large for many applications
with `family = Poisson()`

and a smaller value is better.

risk

a character indicating how the empirical risk should be
computed for each boosting iteration. `inbag`

leads to
risks computed for the learning sample (i.e., all non-zero weights),
`oobag`

to risks based on the out-of-bag (all observations with
zero weights) and `none`

to no risk computations at all.

stopintern

a logical that defines if the boosting algorithm stops internally when the out-of-bag risk in one iteration is larger than the out-of-bag risk in the iteration before. Can also be a positive number giving the risk difference that needs to be exceeded.

center

deprecated. A logical indicating if the numerical covariates should be mean
centered before fitting. Only implemented for
`glmboost`

. In `blackboost`

centering is not needed. In `gamboost`

centering is only needed if `bols`

base-learners are specified without intercept. In this
case centering of the covariates is essential and should be done
manually (at the moment). Will be removed in favour of
a corresponding argument in `glmboost`

in the future
(and gives a warning).

trace

a logical triggering printout of status information during the fitting process.

An object of class `boost_control`

, a list.

Objects returned by this function specify hyper-parameters of the
boosting algorithms implemented in `glmboost`

,
`gamboost`

and `blackboost`

(via the `control`

argument).