Installing and using glmmADMB in R 
Under windows:
 Download glmmADMB.zip.
 On the "Packages" menu in R, choose "Install package(s) from local zip file..."
Under linux:
 Download glmmADMB_0.3.tar.gz.
 Consult the
R Installation and Administration manual on how to install the package.
Using the package in R:
 library("glmmADMB") to load the package into R
 help("glmm.admb") and see example at the bottom of the help page.
Note that the ADMBRE executables create temporary files (sometimes large), so you should start R in a
specially dedicated directory.
 
Source code 
Source code: glmmADMB includes two binaries ("nbmm" and "bvprobit"). On request from
Rusers we make the (AD Model Builder) source code for these available here:
nbmm.tpl and bvprobit.tpl. (In order
to compile the tplfiles you need to buy AD Model Builder.)
 
ADMB User Forum 
Questions relating to the Rpackage should be posted to the
ADMB user forum
under the topic "ADMB NBMM for R"
 

Zeroinflation and overdispersion currently receive much attention in the statistical literature, e.g:
For count responses, the situation of excess zeros (relative to what standard models allow) often
occurs in biomedical and sociological applications. Modeling repeated measures of zeroinflated count
data presents special challenges. This is because in addition to the problem of extra zeros,
the correlation between measurements upon the same subject at different occasions needs to be taken into
account.
Min and Agresti (2005),
Statistical modelling
The Rpackage glmmADMB provides a GLMM framework (in
the spirit of glmmPQL and GLMM) with:
 Negative binomial or Poisson responses.
 Zeroinflation, e.g. a mixture of a Poisson or negative binomial distribution and a point mass at zero.
In addition it is possible to fit data with Bernoulli response (0 or 1):
 Logistic or probit link function
glmmADMB is developed using ADMBRE, but the full unrestricted
Rpackage is made freely available and does not require ADMBRE to run with user supplied data.
Likelihood approximation
By default glmm.admb() uses the Laplace approximation, which is beleived to be superior to
the PQL method used by other mixed model routines in R. Hence, the likelihood values returned
by glmm.admb() can be used construct the AIC criterion for model comparison, and to perform likelihood
ratio tests. For situations where the Laplace approximation is not accurate enough, importance sampling
is an option of glmm.admb().
Beyond the standard GLMM framework
ADMBRE provides a full
programming language for random effects modeling. The code for glmmADMB is
nbmm.tpl. Using ADMBRE it is easy to modify nbmm.tpl to nonstandard
situations, such as:
 zeroinflation with P(zero) depending on covariates.
 Distributions of different types: e.g. response (X,Y) with X Bernoulli and Y Poisson.
 Crossed random effects.
Details and examples of how to build ADMBRE programs can be found here: user manual.
