Bibat is a Python package providing a flexible interactive template for Bayesian statistical analysis projects.
It aims to make it easier to create software projects that implement a Bayesian workflow that scales to arbitrarily many inter-related statistical models, data transformations, inferences and computations. Bibat also aims to promote software quality by providing a modular, automated and reproducible project that takes advantage of and integrates together the most up to date statistical software.
Bibat comes with "batteries included" in the sense that it creates a working example project, which the user can adapt so that it implements their desired analysis. We believe this style of template makes for better usability and easier testing of Bayesian workflow projects compared with the alternative approach of providing an incomplete skeleton project.
Check out bibat's documentation at https://bibat.readthedocs.io.
In particular, you may find it useful to have a look at this vignette that demonstrates, step by step, how to use bibat to implement a complex statistical analysis.
You can try out bibat like this (make sure you are in a Python environment where you would like to install bibat and its dependencies):
$ pip install bibat
$ bibat
After following the wizard's instructions, you should now have a new directory implementing a simple statistical analysis. To try it out, run the following command from the root of the new directory:
$ make analysis
To install the latest version from github:
$ pip install git+https://github.com/teddygroves/bibat.git@main