Perform a arcsinh(x) transformation

```
arcsinh_x(x, standardize = TRUE, ...)
# S3 method for arcsinh_x
predict(object, newdata = NULL, inverse = FALSE, ...)
# S3 method for arcsinh_x
print(x, ...)
```

## Arguments

- x
A vector to normalize with with x

- standardize
If TRUE, the transformed values are also centered and
scaled, such that the transformation attempts a standard normal

- ...
additional arguments

- object
an object of class 'arcsinh_x'

- newdata
a vector of data to be (potentially reverse) transformed

- inverse
if TRUE, performs reverse transformation

## Value

A list of class `arcsinh_x`

with elements

- x.t
transformed
original data

- x
original data

- mean
mean after transformation but prior to standardization

- sd
sd after transformation but prior to standardization

- n
number of nonmissing observations

- norm_stat
Pearson's P / degrees of freedom

- standardize
was the transformation standardized

The `predict`

function returns the numeric value of the transformation
performed on new data, and allows for the inverse transformation as well.

## Details

`arcsinh_x`

performs an arcsinh transformation in the context of
bestNormalize, such that it creates a transformation that can be estimated
and applied to new data via the `predict`

function.

The function is explicitly: log(x + sqrt(x^2 + 1))

## Examples

```
x <- rgamma(100, 1, 1)
arcsinh_x_obj <- arcsinh_x(x)
arcsinh_x_obj
#> Standardized asinh(x) Transformation with 100 nonmissing obs.:
#> Relevant statistics:
#> - mean (before standardization) = 0.7198804
#> - sd (before standardization) = 0.5443389
p <- predict(arcsinh_x_obj)
x2 <- predict(arcsinh_x_obj, newdata = p, inverse = TRUE)
all.equal(x2, x)
#> [1] TRUE
```