All examples
http://www.admb-project.org/examples/copy_of_new-examples
All examples currently available in the website"Maximum likelihood estimation and inference" by Russell Millar
http://www.admb-project.org/examples/text-books/maximum-likelihood-estimation-and-inference-by-russell-millar
No publisher2013-11-21T15:51:23ZFolderA discrete valued time series model; Polio data
http://www.admb-project.org/examples/glmm-generalized-linear-mixed-models/count-data/a-discrete-valued-time-series-model
Illustrate how a time series of count data can be modelled as a GLMM with a Poisson responseNo publisherRandom effectMedical/Biometrics2010-04-01T03:30:29ZFolderA discrete valued time series model; Polio data
http://www.admb-project.org/examples/state-space-models/a-discrete-valued-time-series-model
Illustrate how a time series of count data can be modelled as a GLMM with a Poisson responseNo publisherRandom effectMedical/Biometrics2010-04-01T03:30:29ZFolderA fisheries model with random effects
http://www.admb-project.org/examples/fisheries/a-fisheries-model-with-random-effects-1
Catch-age model from Schnute and Richards (1995) with annual recruitments as random effects.No publisher2013-01-12T02:04:27ZFolderA standard CJS model
http://www.admb-project.org/examples/mark-recapture/cormack-jolly-seber-models/fitting-cormack-jolly-seber-models-to-capture-recapture-data-using-r2admb
Fitting Cormack-Jolly-Seber (CJS) Models to Capture-Recapture Data using R2admbNo publisher2012-11-16T05:40:00ZFolderAdjoint code
http://www.admb-project.org/examples/admb-tricks/adjoint-code-1
Why to write adjoint code and alternative approaches to do it.No publisher2013-01-12T01:45:00ZFolderBaranov catch equation
http://www.admb-project.org/examples/fisheries/a-fisheries-model-solving-the-baranov-catch-equation-using-adjoint-code
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. No publisher2013-01-14T20:05:03ZFolderBCB bowheads
http://www.admb-project.org/examples/glmm-generalized-linear-mixed-models/gaussian-models/bcb-bowheads
Abundance estimation of BCB bowhead whalesNo publisher2012-12-30T05:21:32ZFolderBeta-binomial model
http://www.admb-project.org/examples/glmm-generalized-linear-mixed-models/non-gaussian-random-effects/beta-binomial-model
Binomial response with random effects having beta distribution. Comparison to Winbugs and h-GLMNo publisherWinbugsRandom effect-gh (Gauss Hermite integration)h-GLM2012-11-16T05:05:00ZFolderBy field of application
http://www.admb-project.org/examples/by-field-of-application
No publisher2009-12-15T06:54:28ZFolderCategorical data
http://www.admb-project.org/examples/categorical-data
No publisher2009-12-15T06:54:28ZFolderCJS Individual Heterogeneity
http://www.admb-project.org/examples/mark-recapture/cormack-jolly-seber-models/cjs-individual-heterogeneity-1
Mixed Effects Cormack-Jolly-Seber Models for Analysis of Capture-Recapture DataNo publisher2012-12-03T16:20:00ZFolder CJS Models
http://www.admb-project.org/examples/mark-recapture/cormack-jolly-seber-models
Cormack-Jolly-Seber (CJS) models in different variationsNo publisher2012-11-16T05:40:00ZFolderComparison of approaches
http://www.admb-project.org/examples/spatial-models/separable-different-implementation
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. No publisher2011-12-07T02:09:09ZFolderCount data
http://www.admb-project.org/examples/glmm-generalized-linear-mixed-models/count-data
Poisson, negative binomial counts in various variantsNo publisher2010-11-24T01:57:00ZFolder