Because of the Master thesis I am supervising, I had a look into options for implementing SDT models and found out that brms already supports basically all link functions that are relevant for this: mostly probit for SDT with normal noise and cloglog for SDT with Gumbel noise. Additional options would be cauchit or logit for SDT models with Cauchy or Logistic noise. But I don't think these are really relevant. These links are implement for both bernoulli or binomial distributions for single threshold SDT models, as well as for the cumulative distribution needed for the multiple threshold/confidence SDT models.

I was thinking about implementing two SDT models for 1) the single threshold case and 2) the multiple threshold case and in addition to the response variable let the users specify which noise distribution they want to assume (i.e. "normal" or "gumbel"). For the multiple threshold models, there would be an additional option for unequal variance models, providing the option to predict a "discrimination" parameter.

Would you mind, if I take a shot at these implementations? Or do you want to do this? Just because originally, we discussed that you would take the lead in this.

Apart from that I think these models should support all of the brms functionality, given that the distributions and link functions are natively supported by brms.