The paper pointed out the following options to fit GLMM using R:

- glmer
- glmmML
- glmm (from the repeated package)
- glmmADMB
- MCMCglmm
- glmmBUGS
- glmmPQL
- BUGS (through R2WinBUGS)
- glmmAK

And I would like to add one more, npmlreg.

I am not aware of the glmmAK package before. From the first glance, it seems to be very promising in the sense that it seems to allow non-Gaussian random effect in a Bayesian framework, something similar to what npmlreg does with ML method.

============== edited on March 3 ====================

DPpackage is another package that can estimate GLMM in a Bayesian framework.

============== edited on March 5 ====================

ASReml/ASReml-R is another choice. It is not free software, but it does seem to have some unique strengths. Maybe I should download a demo copy and try it myself.

============== edited on March 29 ===================

hglm is another possibility.

I am not aware of the glmmAK package before. From the first glance, it seems to be very promising in the sense that it seems to allow non-Gaussian random effect in a Bayesian framework, something similar to what npmlreg does with ML method.

============== edited on March 3 ====================

DPpackage is another package that can estimate GLMM in a Bayesian framework.

============== edited on March 5 ====================

ASReml/ASReml-R is another choice. It is not free software, but it does seem to have some unique strengths. Maybe I should download a demo copy and try it myself.

============== edited on March 29 ===================

hglm is another possibility.

## 2 comments:

So, based on your experience, which package would you suggest using?

They each has unique advantages and weaknesses. In my work, I use glmer ("lme4" package) and allvc ("npmlreg" package) a lot; and I am starting to get in the world of Bayesian analysis, and MCMCglmm is a real life saver.

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