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A tool for Approximate Bayesian Computation

Project Description

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

# Approximate Bayes rocks!

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

## Features

* Model comparison via approximate Bayes factors
+ rejection
+ random forest
* Parameter inference
+ rejection
+ MCMC
* Cross-validation

## Installation

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

```bash
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):

```bash
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:

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

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

```bash
python -m pip install #name_of_the_whl_file
```

Repeat the same steps for the Numpy wheel:

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


Now, open a terminal and type:

```bash
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/project_files)).
Currently, we provide:

* Two-sample t-test
* Levene-Test

### Contributors

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

Release History

This version
History Node

2.0.1

Download Files

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Filename, Size & Hash SHA256 Hash Help File Type Python Version Upload Date
abrox-2.0.1.tar.gz
(1.1 MB) Copy SHA256 Hash SHA256
Source None Feb 1, 2018

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