# Simple generic skewness transform

I would like to add skewness to an ad-hoc distribution (defined essentially by an _lp user-defined function).

Is there a simple / “canonical” / generally used transformation

f(y; \lambda) = x

which would make y \in \mathbb{R} “more skewed” than x \in \mathbb{R} in some direction depending on its sign? With a nice clean log derivative (Jacobian) so it is easy to program in Stan (hey, I can dream ;P).

It does not need to be mean-preserving, ie E[x] \ne E[y] is fine, the distribution already has something that controls the mean that will just adjust accordingly in the posterior.