Package: bibat
Bibat: Batteries-Included Bayesian Analysis TemplateBibat 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.DocumentationCheck out bibat's documentation athttps://bibat.readthedocs.io.In particular, you may find it useful to have a look at [this vignette](https:// bibat.readthedocs.io/en/latest/_static/report.html) that demonstrates, step by
step, how to use bibat to implement a complex statistical analysis.How to use bibatTo start a Bayesian statistical analysis project using bibat, first installcopier, for example like this:$pipxinstallcopierNow choose a directory name for your analysis, for examplemy_cool_project,
and copy bibat's example project there:$copiercopygh:teddygroves/bibatmy_cool_projectThis will trigger an interactive questionnaire and then create a brand
new, custom, batteries-included, Bayesian analysis project in the directorymy_cool_project. Seebibat's documentationfor
what to do next.If you want to use bibat's Python code separately from the template, you can
install it to your python environment as follows:$pipinstallbibatTo install bibat with development dependencies:$pipinstallbibat'[development]'DependenciesBibat requires Python version 3.11 or greater.Bibat's Python dependencies can be found in itspyproject.toml file.Bibat's Python dependencies:arvizcmdstanpycopiernumpypandaspanderapydanticscikit-learnstaniotomlxarrayzarrBibat's development dependencies (install these by runningpip install bibat'[development]'):blackpre-commitcodecovpytestpytest-covtoxsphinxsphinx-clickrufffuroProjects created by bibat have Python dependencies listed in their [pyproject.toml] file](https://github.com/teddygroves/bibat/blob/main/template/pyproject.toml.jinja). The additional ones are as follows:bibatjupyterIn addition, the following Python packages may be installed, depending on how
the user answers bibat's questionnaire:sphinxBibat projects also depend oncmdstan, the command line
interface to Stan. Bibat projects include code that installs cmdstan when you
run the commandmake analysisfrom the root of the target project. To only install dependencies, you can also run the commandmake env.If bibat fails to install cmdstan, please raise an issue! The relevant
parts of thecmdstanandcmdstanpydocumentation might also be useful.Target project dependencies: QuartoBibat supports automatic generation of documentation using either Sphinx orQuarto. Whereas bibat will install Sphinx
automatically, Quarto must be installed manually: see thequarto
documentationfor instructions.Citation informationIf you would like to cite bibat using bibtex please use the following format:@software{bibat,doi={10.5281/zenodo.7775328},url={https://github.com/teddygroves/bibat},year={2023},author={TeddyGroves},title={Bibat:batteries-includedBayesiananalysistemplate},}