Likelihood Function for Multinomial Logistic Regression and its partial derivatives

Thanks for the book suggestion. I'll check it out tomorrow when the library
opens up. 

Yes, it is a multilevel model, but its likelihood function is the sum of the
likelihood functions for the individual levels (i.e. a simple multinomial
logits) and some other terms (the priors). It is, essentially, the
hierarchical priors model of Finkel and Manning (HLT, 2009). 

So to compute the above composite likelihood function I need just the simple
multinomial logit likelihood for each of the separate pools (there are just
two levels - the lower consisting of 4 pools, the upper of just one, i.e.
it's a hierarchical relationship). 

So I am really surprised that I could not find R package containing a
function that computes the simple multinomial logit regression for a given
set of parameter values (rather than simply supplying the final fit).

   Hi: John Fox's CAR book has some very nice examples of how the
multinomial
   likelihood  is  estimated  computationally speaking. But you mentioned
   multilevel earlier which sounds more complex ?

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