A tool for Approximate Bayesian Computation
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.
- Model comparison via approximate Bayes factors
- Parameter inference
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.
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 `
Assuming Python is already installed, first install Visual Studio Build Tools from:
Now visit the following page to install the Scipy wheel:
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:
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.
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
- Multinomial Processing tree (comparison)
- [Ulf Mertens](http://www.psychologie.uni-heidelberg.de/ae/meth/team/mertens/)
- and Stefan Radev