Setting Different Priors on Category-Specific Coefficients

I’m fitting an adjacent categories ordinal model with category-specific effects, and I’d like to set different priors on the per-level coefficients. For a reproducible example, consider the following model:

dat <- read.dta(“”)
dat$apply = as.integer(dat$apply)
fit1 = brm(bf(apply~cs(gpa)), family = acat(), data = dat, chains = 2)

GPA has two coefficients, as expected, but prior_summary(fit1) shows only one possible prior location for GPA, which sets the priors for both of the coefficients. How would I go about setting, for example, a uniform(-0.001,0.001) prior on gpa[1] (essentially setting it to 0), while leaving gpa[2] with more traditional prior?

  • Operating System: Ubuntu 16.04
  • brms Version: 2.7.0

Currently, brms does not support varying priors over category specific effects, but please feel free to open an issue on so that I may eventually implement it.