So I have a truncated normal in my model. But how do I do a posterior predictive check with such a model? I know there are two ways to force a truncated distribution in the model block, either by parameter bounds or by explicit truncation wtih the `T[a,b]`

modifier. But the explicit truncation approach does not work for the `_rng`

functions. So the question is: how do I draw samples from truncated distributions using `_rng`

functions in the `generated quantities`

block?

Or is there a simple trick to transform draws from non-truncated distribution to a truncated one, similar to the desugaring of the `T[a,b]`

syntax for sampling statements shown in the docs? The best I have so far is rejection sampling which luckily is not slowing my model down a lot, but it feels hacky.

Thanks for any ideas.