These functions are slightly different APIs for `partykit::ctree()`

and
`partykit::cforest()`

that have several important arguments as top-level
arguments (as opposed to being specified in `partykit::ctree_control()`

).

## Usage

```
ctree_train(
formula,
data,
weights = NULL,
minsplit = 20L,
maxdepth = Inf,
teststat = "quadratic",
testtype = "Bonferroni",
mincriterion = 0.95,
...
)
cforest_train(
formula,
data,
weights = NULL,
minsplit = 20L,
maxdepth = Inf,
teststat = "quadratic",
testtype = "Univariate",
mincriterion = 0,
mtry = ceiling(sqrt(ncol(data) - 1)),
ntree = 500L,
...
)
```

## Arguments

- formula
A symbolic description of the model to be fit.

- data
A data frame containing the variables in the model.

- weights
A vector of weights whose length is the same as

`nrow(data)`

. For`partykit::ctree()`

models, these are required to be non-negative integers while for`partykit::cforest()`

they can be non-negative integers or doubles.- minsplit
The minimum sum of weights in a node in order to be considered for splitting.

- maxdepth
maximum depth of the tree. The default

`maxdepth = Inf`

means that no restrictions are applied to tree sizes.- teststat
A character specifying the type of the test statistic to be applied.

- testtype
A character specifying how to compute the distribution of the test statistic.

- mincriterion
The value of the test statistic (for

`testtype == "Teststatistic"`

), or 1 - p-value (for other values of`testtype`

) that must be exceeded in order to implement a split.- ...
Other options to pass to

`partykit::ctree()`

or`partykit::cforest()`

.- mtry
Number of input variables randomly sampled as candidates at each node for random forest like algorithms. The default

`mtry = Inf`

means that no random selection takes place.- ntree
Number of trees to grow in a forest.

## Examples

```
if (rlang::is_installed(c("modeldata", "partykit"))) {
data(bivariate, package = "modeldata")
ctree_train(Class ~ ., data = bivariate_train)
ctree_train(Class ~ ., data = bivariate_train, maxdepth = 1)
}
#>
#> Model formula:
#> Class ~ A + B
#>
#> Fitted party:
#> [1] root
#> | [2] B <= 56.77622: Two (n = 100, err = 34.0%)
#> | [3] B > 56.77622: One (n = 909, err = 33.8%)
#>
#> Number of inner nodes: 1
#> Number of terminal nodes: 2
```