I’m doing a project that uses brms (logit) for a Bayesian multilevel model, which has three levels (time, people, and company), and both people and company have random intercept and random slopes. For this particular setting, we have a prior that the correlation between the intercept and slope should be at least weakly positive, but since the default in brms is LKJ and the correlation will be symmetrical no matter what shape is defined. Can anyone help me understand:

- what is the rationale for brms to use LKJ prior?
- what can I do if I want to impose a prior distribution for correlation to be >0?

I’ve been searching for answers for a while but haven’t found any conclusion yet. Any hint is appreciated!