A set of objects are contained here to easily facilitate the use of outcome transformations for modeling. For example, if there is a large amount of variability in the resampling results for the Kappa statistics, which lies between -1 and 1, assuming normality may produce posterior estimates outside of the natural bound. One way to solve this is to use a link function or assume a prior that is appropriately bounded. Another approach is to transform the outcome values prior to modeling using a Gaussian prior and reverse-transforming the posterior estimates prior to visualization and summarization. These object can help facilitate this last approach.

## Format

An object of class `list`

of length 2.

An object of class `list`

of length 2.

An object of class `list`

of length 2.

An object of class `list`

of length 2.

An object of class `list`

of length 2.

## Details

The `logit_trans`

object is useful for model
performance statistics bounds in zero and one, such as accuracy
or the area under the ROC curve.

`ln_trans`

and `inv_trans`

can be useful when the statistics
are right-skewed and strictly positive.

`Fisher_trans`

was originally used for correlation statistics
but can be used here for an metrics falling between -1 and 1,
such as Kappa.