Sam Brilleman

I have a similar issue to the above, but related to a `PGView` referencing a newly added column in an sqlalchemy table, and both trying to be handled in the...

Will running: ``` f1$stan_summary[, "sd"] ``` essentially get you what you want? @jgabry or @bgoodri might be able to explain why running: ``` count_data

To fix this, I think we can just skip the shift/centering of the intercept prior when `prior_PD = TRUE`? Would you agree @ermeel86?

> Good catch Sam! Actually, wasn't my sharp eye. Rather, somebody emailed me asking why the outputs weren't consistent! ha 😨 > I admit that this is somewhat more implicit...

> Then how are you drawing from the prior predictive distribution of the same model whose posterior distribution is being estimated when `prior_PD = FALSE`? Good point, I guess you...

Hi @anddis - thanks for reporting this! It is a little odd. Not sure when someone will have time to look into the details of this, but I will tag...

Hi both - sorry for the really slow reply. @ermeel86 - I don't know off the top of my head, and I've had a poke around in the code and...

@jgabry @liuxiangyu127 Seems reasonable, but should we maybe add an argument to make this optional? It might dramatically inflate the size of the return object, so probably only want to...

Yeah it may well be. I don't think `posterior_epred()` existed back when `posterior_survfit()` came into existence. So I will read the more recent docs and see.

Had a look and there are some differences. `posterior_epred` is kinda like `posterior_survfit(type = "haz")` but as soon as you start looking for `posterior_survfit(type = "surv")` you need a transformation...