I agree with you, Ben. I don’t think we should recommend “hacking your prior to push a pocket of the parameter space out of the typical set.” Maybe in the documentation we can say something like this:
"When there are divergences, this can be an opportunity for you to revisit your model. Often we have found that users have prior information available to them that they have not included in their model. If you have such prior information, you can add this to your model now, and, in addition to the benefits in statistical efficiency, this might alleviate the computational difficulties as well. For example, if you are nearly certain that a parameter will be less than 1 in absolute value, you could assign it a normal(0, 1) prior distribution, which can pull the posterior away from regions of parameter space which are substantively irrelevant but could cause computational problems.
We are not saying that adding prior information will necessarily resolve the computational problems revealed by divergences or poor mixing, nor are we recommending the use of a prior solely for the purpose of stabilizing computation."