`KernelSmooth_cfg`

is a configuration class for non-parametric local-linear
regression to construct a smooth representation of the relationship between
two variables. This is typically used for displaying a surface of the conditional
average treatment effect over a continuous covariate.

Kernel smoothing is handled by the `nprobust`

package.

## Public fields

`model_class`

The class of the model, required for all classes
which inherit from `Model_cfg`

.

`neval`

The number of points at which to evaluate the local
regression. More points will provide a smoother line at the cost
of somewhat higher computation.

`eval_min_quantile`

Minimum quantile at which to evaluate the smoother.

## Methods

### Method `new()`

Create a new `KernelSmooth_cfg`

object with specified number of evaluation points.

#### Arguments

`neval`

The number of points at which to evaluate the local
regression. More points will provide a smoother line at the cost
of somewhat higher computation.

`eval_min_quantile`

Minimum quantile at which to evaluate the smoother.
A value of zero will do no clipping. Clipping is performed from both the
top and the bottom of the empirical distribution. A value of alpha would
evaluate over [alpha, 1 - alpha].

#### Returns

A new `KernelSmooth_cfg`

object.

### Method `clone()`

The objects of this class are cloneable with this method.

#### Usage

`KernelSmooth_cfg$clone(deep = FALSE)`

#### Arguments

`deep`

Whether to make a deep clone.

## Examples

```
## ------------------------------------------------
## Method `KernelSmooth_cfg$new`
## ------------------------------------------------
KernelSmooth_cfg$new(neval = 100)
#> <KernelSmooth_cfg>
#> Inherits from: <Model_cfg>
#> Public:
#> clone: function (deep = FALSE)
#> eval_min_quantile: 0.05
#> initialize: function (neval = 100, eval_min_quantile = 0.05)
#> model_class: KernelSmooth
#> neval: 100
```