following up on this: I couldn't find much about figuring out how well the data are modelled (e.g. posterior predictive checks / residuals checks), which is crucial when trying to use more difficult variables (aka non-gaussian) in BGGM. Am I missing something?

@fusaroli I somehow did not receive a notification for this comment. Apologies, as typically I respond quickly.

And you can certainly model discrete variables, or even Poisson, or some combination. I am not really sure how to directly compare to brms, as in BGGM you would be using the rank likelihood or a semi parametric copula. This would be type = "mixed". I do not think brms can do this.

Also, as for predictive checks, I plan to implement these very soon. I agree they are important.

Strange that I also did not get a notification in email (just checked).

Can you provide some further details as to what you are trying to do ?

I'm new to BGGM and have follow-up question to that: can I model a mixture of binary and continuous variables when using the setting type = "mixed"? And is it then correct to set mixed_type to 1 for the binary variables in my data set?
Thank you in advance!

Yes, you can certainly do that.

And, yes, that is correct. So if you have 10 variables, with the first 5 being binary, then you would have c(1,1,1,1,1,0,0,0,0,0). The zeros denote treat those variables as continuous.