AD Model Builder (ADMB)

AD Model Builder (ADMB)

AD Model Builder (ADMB) is an open-source statistical software for nonlinear optimization used for fitting statistical models to data. It uses automatic differentiation to calculate gradients efficiently and allows building complex statistical models.
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statistics modeling optimization nonlinear-models automatic-differentiation

AD Model Builder (ADMB): Open-Source Nonlinear Optimization Software

AD Model Builder (ADMB) is an open-source statistical software for nonlinear optimization used for fitting statistical models to data. It uses automatic differentiation to calculate gradients efficiently and allows building complex statistical models.

What is AD Model Builder (ADMB)?

AD Model Builder (ADMB) is an open-source statistical software designed for nonlinear optimization and fitting statistical models to data. It utilizes automatic differentiation techniques to efficiently calculate parameter gradients, which allows fitting complex models with many parameters.

Some key features of ADMB include:

  • Fully automatic differentiation for fast, accurate gradient calculations
  • Template-based model specification for defining statistical models
  • Good for fitting models with many parameters
  • Specialized for fisheries stock assessment and ecological modeling
  • Implements maximum likelihood and other statistical estimation methods
  • Command line programs and APIs for scripting model fitting tasks
  • Good documentation and active user community support

ADMB makes it easier to implement complex statistical models with many parameters. The automatic differentiation and templating system speed up model testing and fitting considerably. It strikes a balance between the flexibility of general statistical programming languages and the convenience of dedicated model fitting tools.

AD Model Builder (ADMB) Features

Features

  1. Statistical modeling and analysis
  2. Nonlinear optimization
  3. Automatic differentiation
  4. Template-based modeling language
  5. Built-in diagnostics
  6. Parameter estimation
  7. Hierarchical mixed effects models

Pricing

  • Open Source

Pros

Efficient and accurate gradient calculations

Flexible and expressive modeling language

Open source with active development community

Integrates well with R and other tools

Cons

Steep learning curve

Limited documentation and training resources

Less flexible than general purpose programming languages


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