Have you tried the
brms package? Its
brm function supports multinomial logistic models and category-specific variables as well. Not sure if it will do what you want, though. Something like:
mod <- brm (choice ~ agentVar1 + agentVar2 + cse (choiceVar1),
prior=c (set_prior ("student_t(3, 0, 5)", class="b")))
might be what you're after. The
cse is a category-specific effect, which I think might be your alternative-specific variables. I think.
brms is Bayesian and does sampling. The Bayesian part may require reasonable priors to converge. (Usually you can get started with its defaults, but some kinds of regressions -- I think categorical is one of them -- are pickier. I copied/pasted a prior I used for a different categorical task that could get you started.) Bayesian statistics requires more thought up front (priors) and more checking of convergence, but it offers a lot of goodness in return. So if you're totally unfamiliar with it, this could be a big leap that doesn't make sense right now, but it's an option to consider.
The formula automatically compiles down to the Stan language, which compiles down to C++, which compiles to an executable that is run. So you need to have a C++ compiler on your system for it to work. (Probably included if you're running Linux, install the free Developer Tools under MacOS, not sure about Windows.)
brm is unbelievably flexible and supports everything from censoring and truncation to random effects and smoothers. So it pretty much supports any kind of regression you can imagine.
I'm just a very satisfied user of
brms, and thought it might solve your current issue and be useful in the future as well.