# All examples

All examples currently available in the website

- Differential equations
- Eilers & Marx parameterization
- Parameterization of the spline from Eilers & Marx (1996)
- Estimation of detection function
- Illustrates the likelihood based estimation of the detection function (perpendicular distance)
- Examples
- Under construction
- Extension: correlated RE's
- Add random effects to all 3 phi's, and attempt to estimate correlations
- Extension: crossed RE's
- Adds a "day effect" following Millar (2004, Aust NZ J. Stat, 46, p. 543-554)
- Fisheries
- Different uses of ADMB in fisheries stock assessments or other fisheries work
- Flexible negative binomial
- Explores non-standard relationships between mean and variance in the NB model
- Function minimizer
- Various tricks and techniques related to the function minimizer to improve convergence
- Gamma distributed myxomatosis using R2admb
- GAMs as mixed models
- Generalized Additive Models
- Gaussian models
- Models where both the response and latent random variable are Gaussian. For such models the covariance matrix of the observations can be worked out analytically, but still the latent variable (random effect) formulation can be beneficial.
- Geostatistical approach
- The approach to spatial modeling where you explicitly model the covariance function/matrix. First used in geology/mining (hence the name). Can be used with both Gaussian and non-Gaussian response for data.
- GLM/GLMM/GAM
- glmmADMB
- Growth models
- Item response theory
- The multilevel Rasch model can be implented using random effects in ADMB. As an example we use data on the responses of 2042 soldiers to a total of 19 items (questions), taken from Doran et al (2007). This illustrates the use of crossed random effects in ADMB. Further, it is shown how the model easily can be generalized in ADMB. These more general models cannot be fitted with standard GLMM software such as "lmer" in R.
- Line transect methods
- Line transect methods are commonly used to estimate animal abundance, and is a special case of distance sampling.
- lmer() comparison
- Application of ADMB to the simulated datasets in Zhang et al. (2011) with emphasis on comparison to the R function lmer()
- Mark-recapture

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