What transform does is transferring a (vector) of value in R into something that are in the domain that defined by the distribution.
In fact, the transformation in PyMC3 is defined in this direction only. It is different than package like TFP, where transformation is defined in both direction and you can apply both forward and inverse transformation.
I’m sorry: I don’t follow this argument. I’m asking for the transforms to be applied in sample_posterior_predictive and sample_prior_predictive. This is only applying the transform forward, as you say, so why would it be wrong to do this?
It seems like the argument for doing this is clear: a programmer that builds a model with transformed variables, and then asks PyMC3 to generate samples from that model, will expect to see transformed variables (e.g., no truncated normal samples below the lower bound).
With all due respect, I can’t see how any argument could be made for the current behavior.