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News
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glmmADMB: R-package for
fitting mixed models to overdispersed and zero inflated count data. glmmADMB is implemented using ADMB-RE,
but it is free!
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Buy ADMB-RE
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Contact us at orders@otter-rsch.com
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Free evaluation version of ADMB-RE
can be downloaded here
Features
- Nested and crossed random effects
- Exact marginal likelihood by importance sampling
- Seamless switch between maximum likelihood and MCMC based inference
Technical details
- Model specification in C++ like language
- Hyper-parameters (variance components etc.) estimated by maximum likelihood
- Marginal likelihood evaluated by the Laplace approximation or importance sampling
- ADMB-RE calculates exact derivatives using Automatic Differentiation
- All the useful features of ordinary AD Model Builder are available
Why choose ADMB-RE?
- Flexibility: In principle you can implement any random effect you can think of
- Convenience: Computational details are transparent. Your only responsibility is to formulate the loglikelihood
- Computational efficiency: ADMB-RE is up to 50 times faster than winBUGS
- Robustness: With exact derivatives you can fit highly nonlinear models
- Convergence diagnostic: The gradient of the likelihood function provides a clear
convergence diagnostic, while with MCMC judging convergence is difficult.
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