Wrapper around PipeOpCrankCompositor to simplify Graph creation.

## Usage

```
pipeline_crankcompositor(
learner,
method = c("sum_haz", "mean", "median", "mode"),
which = NULL,
response = FALSE,
overwrite = FALSE,
graph_learner = FALSE
)
```

## Arguments

- learner
`[mlr3::Learner]|[mlr3pipelines::PipeOp]|[mlr3pipelines::Graph]`

Either a`Learner`

which will be wrapped in mlr3pipelines::PipeOpLearner, a`PipeOp`

which will be wrapped in mlr3pipelines::Graph or a`Graph`

itself. Underlying`Learner`

should be LearnerSurv.- method
`character(1)`

One of`sum_haz`

(default),`mean`

,`mode`

, or`median`

; abbreviations allowed. Used to determine how`crank`

is estimated from the predicted`distr`

.- which
`integer(1)`

If`method = "mode"`

then specifies which mode to use if multi-modal, default is the first.- response
`logical(1)`

If`TRUE`

then the`response`

predict type is also estimated with the same values as`crank`

.- overwrite
`logical(1)`

If`TRUE`

then existing`response`

and`crank`

predict types are overwritten.- graph_learner
`logical(1)`

If`TRUE`

returns wraps the Graph as a GraphLearner otherwise (default) returns as a`Graph`

.

## Examples

```
if (FALSE) {
if (requireNamespace("mlr3pipelines", quietly = TRUE)) {
library("mlr3")
library("mlr3pipelines")
task = tsk("rats")
pipe = ppl(
"crankcompositor",
learner = lrn("surv.coxph"),
method = "sum_haz"
)
pipe$train(task)
pipe$predict(task)
}
}
```