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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)
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)
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.
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()

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