LFO-CV-paper | This repository contains all materials for the paper

Thanks to the link from @dipetkov, I solved this myself. I didn't use the exact methods in the link, but came up with an alternative. You can call stan functions from R to get it to compute log likelihood for your model, even with unseen data (and its very fast!).

First, I put everything in my transformed parameters block into a function in stan's functions block. Then, I created a second function that wraps the first function, and evaluates the log likelihood for given observations and provided parameter estimates (I then removed my generated_quantities block). rstan has a function expose_stan_functions which adds all functions in the stan functions block to the R environment.

You can then call the log likelihood function you made to evaluate your model with any observations (previously seen or unseen), along with a set of parameter estimates.