Using `reduce_sum` with `ordered_logistic`

Modeling
13

Trying to vectorize this without reduce_sum first following the examples from the user guide here 23.8 Vectorization | Stan User’s Guide and here 1.13 Multivariate priors for hierarchical models | Stan User’s Guide.

For ordered_logistic_lpmf(ints k | vector eta, vectors c), its clear how to create the vector for the first argument. But for the second I’m a bit at a loss because the vectors are of different length (items have different numbers of response categories). So while theta[jj[n]] * beta[ii[n]] returns a real for every n, segment(alpha, pos_alpha[ii[n]], m[ii[n]]) returns a vector, the size of which can vary by n.

 {
    vector[N] theta_beta_v;
    ?[N] alpha_v;	// should be vectors of different size but ragged arrays are not supported
    for (n in 1:N) {
      theta_beta_v[n] = theta[jj[n]] * beta[ii[n]];
      ? = segment(alpha, pos_alpha[ii[n]], m[ii[n]]);
    }
    target += ordered_logistic_lpmf(r | theta_beta_v, alpha_v); 
 }

I think ordered_logistic_glm_lpmf could be used once this has been figured out.