Adding expert recommended scores in regularized horseshoe priors

Short summary of the problem:
I am trying to use brms package for the zero-inflated negative family in the Bayesian framework. My aim is to apply the shrinkage method through a horseshoe prior.

To know the importance of variables, I invite experts from different countries and conducted an online survey for recommending whether a predictor candidate is significant (based on the Likert scale). Thus, obtain a weighted score corresponding to each of them.

I want to include these weighted scores as prior information in the model with the horseshoe prior. I tried different ways to solve it but didn’t succeed yet.

The function for defining horseshoe prior in the model:
horseshoe(df = 1, scale_global = 1, df_global = 1, scale_slab = 2, df_slab = 4,
par_ratio = NULL, autoscale = TRUE)

Additional Information:

  • Operating System: Windows
  • brms Version: 2.18.0

The functional form of regularized horseshoe prior defined in the stan code is :

tau: global shrinkage parameter,
c>0: fixed or assume some prior distribution
lambda_j: local shrikage parameters

I guess, we can modify the stan code by assigning c with expert scores. In my case, expert scores are in the range of 1 to 5.