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Overview

Gmacs is a size-based stock assessment modelling framework: an open source program to support the development of integrated assessment models for hard-to-age species, such as crabs. Gmacs is developed using AD Model Builder (ADMB) and so will build on any computer that can run ADMB. ADMB is free, open source, and available for Windows, Linux, MacOS, and Sun/SPARC.

Getting Started

To build and run Gmacs, you'll need a recent version of ADMB (Version 11.1). For Windows users, we reccommend downloading and installing ADMB with the easy-to-use Windows Installer

Easy Option

First-time users and those with little-to-no ADMB experience can use this [handy makefile] to get started.

Advanced Option

Experienced ADMB users can download the Gmacs repository

Developer Option

Those with experience using ADMB and developing their own models might wish to clone the repository using Git. See the Developers page on this wiki for more information.

User-Guide

This Wiki is designed as a user- and developer-guide: it describes how Gmacs can be used to develop size-based stock assessment models and provides details about the source code so that interested users can contribute to development.

The Gmacs framework can make use of a wide range of data sources for both male and female individuals, including survey and fishery indices of abundance and fishery and survey size-composition data. Data must be entered in the form of a text file, model.dat. Control of the model is achieved through the use of three separate input files: the starter.gm file, model.ctl file, and forecast.gm file. Examples of data and control files are presented at the end of this guide.

TODO

• Write introduction to Gmacs.
• Implement and describe a quick-start download procedure.
• Describe the basic set up of files, the executable program, and how to build from source.
• Describe the basic structure of the gmacs.tpl file and how to edit code and contribute to the project.
• Write user guide for each of the input files: data.gm, and control.gm.
• Explain use of the model and associated tools via batch files or a makefile: clean, run, get files etc.
• Describe interpretation of outputs, including how to download, install, and use the R package.