# All examples

All examples currently available in the website

- "Maximum likelihood estimation and inference" by Russell Millar
- A discrete valued time series model; Polio data
- Illustrate how a time series of count data can be modelled as a GLMM with a Poisson response
- A discrete valued time series model; Polio data
- Illustrate how a time series of count data can be modelled as a GLMM with a Poisson response
- A fisheries model with random effects
- Catch-age model from Schnute and Richards (1995) with annual recruitments as random effects.
- A standard CJS model
- Fitting Cormack-Jolly-Seber (CJS) Models to Capture-Recapture Data using R2admb
- Adjoint code
- Why to write adjoint code and alternative approaches to do it.
- Baranov catch equation
- Example by Steven Martell of writing adjoint code in ADMB to numerically solve the Baranov catch equation. A simple simulation model is used to generate simulated catch and relative abundance data with observation error only. The assessment model is then conditioned on the simulated catch data.
- BCB bowheads
- Abundance estimation of BCB bowhead whales
- Beta-binomial model
- Binomial response with random effects having beta distribution. Comparison to Winbugs and h-GLM
- By field of application
- Categorical data
- CJS Individual Heterogeneity
- Mixed Effects Cormack-Jolly-Seber Models for Analysis of Capture-Recapture Data
- CJS Models
- Cormack-Jolly-Seber (CJS) models in different variations
- Comparison of approaches
- Three different implementations of the same model with separable spatial covariance function in a fully Gaussian situation: i) Plain ADMB (non-random effect) ii) Geostatistical formulation iii) Hybrid approach.
- Count data
- Poisson, negative binomial counts in various variants
- Covariance Calculations
- A short document with accompanying R code that details (1) the functions used to bound parameters, (2) the method for calculating a bounded covariance matrix, and (3) what is stored in the binary admodel.hes and admodel.cov files and how the user can utilize this information to gain more control over an MCMC chain.
- Covariance in RE models
- Covariance matrices
- How to parameterize a covariance matrix
- Delta smelt life cycle model
- A state-space multistage model to evaluate population impacts in the presence of density dependence.
- Diet and heart disease
- Continuous and discrete observation sharing being influenced by a latent random variable