# All examples

All examples currently available in the website

- Math
- Various undocumented techniques and tricks useful for developing ADMB programs
- MCMCMC
- 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.
- Miscellaneous
- 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
- Parameterization
- 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.
- PK/DK
- Pharmacokinetics (PK) & Pharmacodynamics(DK)

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