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All examples
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All examples currently available in the website
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<a href="text-books/maximum-likelihood-estimation-and-inference-by-russell-millar.html" class="contenttype-folder state-published url">"Maximum likelihood estimation and inference" by Russell Millar</a>
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<a href="glmm-generalized-linear-mixed-models/count-data/a-discrete-valued-time-series-model.html" class="contenttype-folder state-published url">A discrete valued time series model; Polio data</a>
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<span class="description">Illustrate how a time series of count data can be modelled as a GLMM with a Poisson response</span>
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<a href="state-space-models/a-discrete-valued-time-series-model.html" class="contenttype-folder state-published url">A discrete valued time series model; Polio data</a>
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<span class="description">Illustrate how a time series of count data can be modelled as a GLMM with a Poisson response</span>
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<a href="fisheries/a-fisheries-model-with-random-effects-1.html" class="contenttype-folder state-published url">A fisheries model with random effects</a>
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<span class="description">Catch-age model from Schnute and Richards (1995) with annual recruitments as random effects.</span>
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<a href="mark-recapture/cormack-jolly-seber-models/fitting-cormack-jolly-seber-models-to-capture-recapture-data-using-r2admb.html" class="contenttype-folder state-published url">A standard CJS model</a>
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<span class="description">Fitting Cormack-Jolly-Seber (CJS) Models to Capture-Recapture Data using R2admb</span>
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<a href="admb-tricks/adjoint-code-1.html" class="contenttype-folder state-published url">Adjoint code</a>
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<span class="description">Why to write adjoint code and alternative approaches to do it.</span>
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<a href="fisheries/a-fisheries-model-solving-the-baranov-catch-equation-using-adjoint-code.html" class="contenttype-folder state-published url">Baranov catch equation</a>
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<span class="description">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. </span>
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<a href="glmm-generalized-linear-mixed-models/gaussian-models/bcb-bowheads.html" class="contenttype-folder state-published url">BCB bowheads</a>
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<span class="description">Abundance estimation of BCB bowhead whales</span>
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<a href="glmm-generalized-linear-mixed-models/non-gaussian-random-effects/beta-binomial-model.html" class="contenttype-folder state-published url">Beta-binomial model</a>
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<span class="description">Binomial response with random effects having beta distribution. Comparison to Winbugs and h-GLM</span>
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<a href="by-field-of-application.html" class="contenttype-folder state-published url">By field of application</a>
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<a href="categorical-data.html" class="contenttype-folder state-published url">Categorical data</a>
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<a href="mark-recapture/cormack-jolly-seber-models/cjs-individual-heterogeneity-1.html" class="contenttype-folder state-published url">CJS Individual Heterogeneity </a>
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<span class="description">Mixed Effects Cormack-Jolly-Seber Models for Analysis of Capture-Recapture Data</span>
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<a href="mark-recapture/cormack-jolly-seber-models.html" class="contenttype-folder state-published url"> CJS Models</a>
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<span class="description">Cormack-Jolly-Seber (CJS) models in different variations</span>
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