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A tool for Approximate Bayesian Computation

Project Description

![Logo](abrox/gui/icons/readme_logo.png)

# ABrox

ABrox is a python package for Approximate Bayesian Computation accompanied by a user-friendly graphical interface.

In the current version, we use the ABC rejection algorithm with a local regression adjustment for the case of parameter inference, and local logistic (multinomial) regression for model comparison.

## Features

  • Model comparison via approximate Bayes factors
  • Parameter inference

## Installation

ABrox can be installed via pip. Simply open a terminal and type:

` pip install abrox `

It might take a few seconds since there are several dependencies that you might have to install as well.

### MacPorts

If you installed Python via MacPorts, the abrox-gui command after installation of abrox does not work. You can alternatively start the GUI via (assuming Python version 3.5):

` cd /Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/abrox/gui/ python3.5 main.py `

### Windows

Assuming Python is already installed, first install Visual Studio Build Tools from:

  1. [here](http://landinghub.visualstudio.com/visual-cpp-build-tools)

Now visit the following page to install the Scipy wheel:

  1. [here](http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy)

After the installation, open a console in the download directory and type:

` python -m pip install #name_of_the_whl_file `

Repeat the same steps for the Numpy wheel:

  1. [here](http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy)

Now, open a terminal and type:

` python -m pip install abrox `

You are now ready to use ABrox!

## ABrox using the GUI

After ABrox has been installed, you can start the user interface by typing abrox-gui. We provide several templates in order to get more familiar with the GUI.

## ABrox using Python

If you are more comfortable with plain Python, you can run your project once from the GUI and continue working with the Python-file that has been generated in the output folder.

## Templates

We provide a few example project files so you can see how ABrox works ([here](https://github.com/mertensu/ABrox/tree/master/templates)). Currently, we provide:

  • Two-sample t-test
  • Levene-Test
  • Multinomial Processing tree (comparison)

### Contributors

[Ulf Mertens](http://www.psychologie.uni-heidelberg.de/ae/meth/team/mertens/)
and Stefan Radev
Release History

Release History

This version
History Node

1.0.0

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
abrox-1.0.0.tar.gz (845.2 kB) Copy SHA256 Checksum SHA256 Source Oct 30, 2017

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