| # Random effects in AD Model Builder ### Do you find standard statistical packages too restrictive? ADMB-RE provides great flexibility for use of random effects in nonlinear models
News
List of publications where ADMB-RE has been used.
glmmADMB: R-package for fitting mixed models to overdispersed and zero inflated count data. `glmmADMB` is implemented using ADMB-RE, but it is free!
Examples
Nonlinear mixed models (`nlme` type) Frailty models in survival analysis Semiparametric regression Stochastic volatility models
Fields of application
Statistical modelling Financial time series Fisheries assessments Pharmacokinetics
More information
Example collection User manual User forum A note for ADMB users
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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.

Updated August 2006

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