Calculates the cross-correlation between two functional vectors using `tf::tf_crosscor()`

.
Note that it only operates on regular data and that the cross-correlation assumes that each column
has the same domain.

To apply this `PipeOp`

to irregualr data, convert it to a regular grid first using `PipeOpFDAInterpol`

.
If you need to change the domain of the columns, use `PipeOpFDAScaleRange`

.

## Parameters

The parameters are the parameters inherited from `PipeOpTaskPreprocSimple`

,
as well as the following parameters:

`arg`

::`numeric()`

Grid to use for the cross-correlation.

## Super classes

`mlr3pipelines::PipeOp`

-> `mlr3pipelines::PipeOpTaskPreproc`

-> `mlr3pipelines::PipeOpTaskPreprocSimple`

-> `PipeOpFDACor`

## Methods

## Inherited methods

### Method `new()`

Initializes a new instance of this Class.

#### Usage

`PipeOpFDACor$new(id = "fda.cor", param_vals = list())`

#### Arguments

`id`

(

`character(1)`

)

Identifier of resulting object, default`"fda.cor"`

.`param_vals`

(named

`list`

)

List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default`list()`

.

## Examples

```
set.seed(1234L)
dt = data.table(y = 1:100, x1 = tf::tf_rgp(100L), x2 = tf::tf_rgp(100L))
task = as_task_regr(dt, target = "y")
po_cor = po("fda.cor")
task_cor = po_cor$train(list(task))[[1L]]
task_cor
#> <TaskRegr:dt> (100 x 2)
#> * Target: y
#> * Properties: -
#> * Features (1):
#> - dbl (1): x1_x2_cor
```