Skip to main content

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

Note that `ABrox`only works with Python 3.

`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

Unfortunately, the installation under Windows is a bit cumbersome. We explain the relevant steps below.

If not already done, install a Python3 version from [here](https://www.python.org/).

Check the version of Python that is installed by typing `python` into the console.

![Python on Windows](abrox/gui/icons/python_windows2.png)

Now, 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. Choose the link that fits
your Python version (see picture above). `cp` should be followed by the actual version (e.g. `cp36`) while
the last part of the link should match the bit-version (e.g. `win32`).

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

After the installation, open a console in the directory the wheel has been downloaded into 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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

abrox-2.0.2.tar.gz (1.1 MB view details)

Uploaded Source

File details

Details for the file abrox-2.0.2.tar.gz.

File metadata

  • Download URL: abrox-2.0.2.tar.gz
  • Upload date:
  • Size: 1.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for abrox-2.0.2.tar.gz
Algorithm Hash digest
SHA256 eb08b25a4a4599ab311ffcde0e64d93b5ad37ce48ad6898862128f081032185b
MD5 995ea0a2fe0d76bfa3b8a75e129464da
BLAKE2b-256 265a9987ebedcf10f814a1ac10fa675cd03218084b39eae7951322b9889a78b7

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page