The matrix is not square. it is J x k where J >> k.(e.g., say J = 32 items and k = 5 dimensions). Only the upper k rows are lower triangular. Each row corresponds to the slope elements for a single item. Also we want to apply priors separately for each item - i.e., separately for each row. In essence, the problem is that I am fitting a 1 dimensional IRT model for item 1, and 2 dimensional IRT model for item 2, ..., a k-1 dimensional model for item k-1, and a k dimensional IRT model for the rest of the J - (k - 1) items. This is the same thing as fitting a k dimensional model for all items but fixing the appropriate slopes to be equal to zero. This would avoid do loops and conditional if statements.
What I am trying to conceptualize is the simplest way of specifying such a model in Stan.