For a research question, I would like to test if at least some people in my dataset have a (true) random slope that is negative, while the fixed effect is positive. So for most people, x and y are positively associated and I want to test if for some people, the association is in fact negative.

I wonder if I can test this question using multilevel models in brms, for instance by constraining negative values of the random slope priors to zero (or to zero minus the estimated fixed effect) and comparing the model to an unconstrained model? If the second model fits the data better, this could indicate that at least some random slopes have values below zero.

Does anyone know if this can be implemented using brms and if so, how could I compare the constrained and unconstrained models?