GLMs: Negative Binomial family in R?

Greetings R Users!

I have a data set of count responses for which I have made repeated
observations on the experimental units (stream reaches) over two air
photo dates, hence the mixed effect.  I have been using Dr. Jim
Lindsey's GLMM function found in his"repeated" measures package with
the "poisson" family.

My problem though is that I don't think the poisson distribution is
the right one to discribe my data which is overdispersed; the variance
is greater than the mean.  I have read that the "negative binomial"
regression models can account for some of the differences among
observations by adding in a error term that independent of the the
covariates.
glm.nb() from package MASS fits negative binomial GLMs.
I haven't yet come across a mixed effects model that can use the
"negative binomial" distribution.
For known theta, you can plug negative.binomial(theta) into glmmPQL()
for example. (Both functions are also available in MASS.) I'm not sure
whether there is also code available for unknown theta.
If any of you know of such a function - I will certainly look forward
to hearing from you!  Additionally, if any of you have insight on
zero-inflated data, and testing for this, I'd be interested in your
comments too.  I'll post a summary of your responses to this list.
Look at package zicounts for zero-inflated Poisson and NB models. For
these models, there is also code available at
  http://pscl.stanford.edu/content.html
which also hosts code for hurdle models.

hth,
Z
Best Regards,
Nadele Flynn, M.Sc. candidate.
University of Alberta

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GLMs: Negative Binomial family in R?

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