Function to include prior information as split probability for the Bayesian additive regression tree model added via engine_bart.

Priors for engine_bart have to be specified as transition probabilities of
variables which are internally used to generate splits in the regression
tree. Specifying a prior can thus help to 'enforce' a split with a given
variable. These can be numeric and coded as values between `0`

and
`1`

.

## Usage

```
BARTPrior(variable, hyper = 0.75, ...)
# S4 method for character
BARTPrior(variable, hyper = 0.75, ...)
```

## Note

Even if a given variable is included as split in the regression or classification tree, this does not necessarily mean that the prediction changes if the value is non-informative (as the split can occur early on). It does however affect any variable importance estimates calculated from the model.

## References

Chipman, H., George, E., and McCulloch, R. (2009) BART: Bayesian Additive Regression Trees.

Chipman, H., George, E., and McCulloch R. (2006) Bayesian Ensemble Learning. Advances in Neural Information Processing Systems 19, Scholkopf, Platt and Hoffman, Eds., MIT Press, Cambridge, MA, 265-272.

## See also

Other prior:
`BARTPriors()`

,
`BREGPrior()`

,
`BREGPriors()`

,
`GDBPrior()`

,
`GDBPriors()`

,
`GLMNETPrior()`

,
`GLMNETPriors()`

,
`INLAPrior()`

,
`INLAPriors()`

,
`STANPrior()`

,
`STANPriors()`

,
`XGBPrior()`

,
`XGBPriors()`

,
`add_priors()`

,
`get_priors()`

,
`priors()`

,
`rm_priors()`