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All examples currently available in the website
Various undocumented techniques and tricks useful for developing ADMB programs
This presents generalized code for conducting Metropolis Coupled MCMC using ADMB called within R
Mean and variance
Both mean and variance vary smoothly as functions of a covariate
MGRF: simple CAR model
CAR model for the Scottish Lip Cancer Data
Mineralization of terbuthylazine
A simple nonlinear least-squares problem, with normally distributed residuals and no random effects or latent variables. Example from the NCEAS non-linear modelling working group.
Stuff that is hard to categorize, but still is useful
Mixed response
Models with responses of different types
Negative Binomial Fir Fecundity
Negative binomial serially correlated counts
Compares a negative binomial response to Poisson responses for the polio data
Non Gaussian random effects
ADMB allows non-Gaussian random effects via transformation of a normal variate
Occupancy model
Comparison of ADMB and WinBUGS modelling approach for simple occupancy model. This is also a comparison of Bayesian and frequentist modelling.
One-compartment open model
Fit mixed effects model to the classical "phenopharbital" model
Orange trees
Ordered categorical responses
Ordered categorical responses with application to SOCATT data
Owl nestling negotiation
Zero-inflated generalized linear mixed model example from the NCEAS non-linear modelling working group.
Parameter scaling
Shows how to scale parameters so that they become of the same magnitude
Examples of how to (and not to) parameterize mathematical functions and statistical models
Pella-Tomlinson basic model
Pella-Tomlinson by Arni Magnusson with user interface and formatted MCMC output. Repeats and extends the analysis of Polacheck et al. (1993).
Pella-Tomlinson from ADMB manual
Pella-Tomlinson example by Dave Fournier from the ADMB manual. Demonstrates several innovative modelling approaches: 6 month time step, time-varying K and q.
Pharmacokinetics (PK) & Pharmacodynamics(DK)

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