The Bayesian regression approach to a sum of complementary trees
is to shrink the said fit of each tree through a regularization prior. BART
models provide non-linear highly flexible estimation and have been shown to
compare favourable among machine learning algorithms (Dorie et al. 2019).
Default prior preference is for trees to be small (few terminal nodes) and
shrinkage towards `0`

.

This package requires the `"dbarts"`

R-package to be installed. Many
of the functionalities of this engine have been inspired by the
`"embarcadero"`

R-package. Users are therefore advised to cite if they
make heavy use of BART.

## Arguments

- x
`distribution()`

(i.e.`BiodiversityDistribution`

) object.- iter
A

`numeric`

estimate of the number of trees to be used in the sum-of-trees formulation (Default:`1000`

).- nburn
A

`numeric`

estimate of the burn in samples (Default:`250`

).- chains
A number of the number of chains to be used (Default:

`4`

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

`"link"`

or`"response"`

(Default:`"response"`

).- ...
Other options.

## Value

An Engine.

## Details

Prior distributions can furthermore be set for:

probability that a tree stops at a node of a given depth (Not yet implemented)

probability that a given variable is chosen for a splitting rule

probability of splitting that variable at a particular value (Not yet implemented)

## References

Carlson, CJ. embarcadero: Species distribution modelling with Bayesian additive regression trees in r. Methods Ecol Evol. 2020; 11: 850– 858. https://doi.org/10.1111/2041-210X.13389

Dorie, V., Hill, J., Shalit, U., Scott, M., & Cervone, D. (2019). Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition. Statistical Science, 34(1), 43-68.

Vincent Dorie (2020). dbarts: Discrete Bayesian Additive Regression Trees Sampler. R package version 0.9-19. https://CRAN.R-project.org/package=dbarts

## See also

Other engine:
`engine_breg()`

,
`engine_gdb()`

,
`engine_glmnet()`

,
`engine_inlabru()`

,
`engine_inla()`

,
`engine_stan()`

,
`engine_xgboost()`

## Examples

```
if (FALSE) {
# Add BART as an engine
x <- distribution(background) |> engine_bart(iter = 100)
}
```