Wrapper around PipeOpDistrCompositor to simplify Graph creation.

## 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.- estimator
`character(1)`

One of`kaplan`

(default) or`nelson`

, corresponding to the Kaplan-Meier and Nelson-Aalen estimators respectively. Used to estimate the baseline survival distribution.- form
`character(1)`

One of`aft`

(default),`ph`

, or`po`

, corresponding to accelerated failure time, proportional hazards, and proportional odds respectively. Used to determine the form of the composed survival distribution.- overwrite
`logical(1)`

If`FALSE`

(default) then if the`learner`

already has a`distr`

, the compositor does nothing. If`TRUE`

then the`distr`

is overwritten by the compositor if already present, which may be required for changing the prediction`distr`

from one model form to another.- graph_learner
`logical(1)`

If`TRUE`

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

.- ...
`ANY`

For use with`distrcompositor`

, now deprecated.

## Examples

```
if (FALSE) {
if (requireNamespace("mlr3pipelines", quietly = TRUE) &&
requireNamespace("rpart", quietly = TRUE)) {
library("mlr3")
library("mlr3pipelines")
task = tsk("rats")
pipe = ppl(
"distrcompositor",
learner = lrn("surv.rpart"),
estimator = "kaplan",
form = "ph"
)
pipe$train(task)
pipe$predict(task)
}
}
```