BayesianEstimationTool (BET)

BayesianEstimationTool fits population-level parameters and individual-specific latent variables with NumPyro inference methods.

Documentation

Start here:

• Manual (Markdown) — model contracts, likelihoods, masking, JAX modeling, inference, persistence, and testing.
• Manual (PDF) — printable manual. If GitHub cannot preview the PDF, download it to view locally.
• Cheatsheet (Markdown) — compact API reference and common workflows.
• Cheatsheet (PDF) — one-page printable reference. If GitHub cannot preview the PDF, download it to view locally.

Manual.md is the authoritative manual. Its PDF is rendered directly from that file using GitHub-like styling and print pagination. The cheatsheet is a separate compact, Python-generated reference. To regenerate locally, install requests, playwright, and reportlab, install Playwright Chromium (python -m playwright install chromium), and set GITHUB_TOKEN to a token permitted to call GitHub's Markdown API for this repository. The manual generator uses GitHub's Markdown rendering API and Playwright; the cheatsheet generator uses ReportLab:

python docs/generators/markdown_to_pdf.py docs/Manual.md
python docs/generators/cheatsheet.py

The GitHub Actions test workflow regenerates the PDFs (and the Python-authored cheatsheet Markdown), checks that the cheatsheet is exactly one PDF page, uploads the outputs as a bet-docs artifact, and runs the test suite.

Installation

BET requires Python 3.10 or newer. Install from PyPI:

python -m pip install BayesianEstimationTool

Alternatively, install the latest source directly from GitHub:

python -m pip install "git+https://github.com/rafaelrbravo/BayesianEstimationTool.git"

You can also download a wheel (.whl) or source archive (.tar.gz) from GitHub Releases and install it with python -m pip install PATH_TO_DOWNLOADED_FILE.

Verify installation:

python -c "import BayesianEstimationTool as bet; print(bet.Estimator)"

For editable development from a source checkout, run python -m pip install -e . in the repository root. The conda recipe in conda-recipe/meta.yaml is for local builds; BET is not yet published on conda-forge.

See the manual for the minimal model and training examples.

Bundled examples and PDF documentation

After installing BET, copy its bundled runnable examples and PDF manual/cheatsheet to an editable folder in the current directory (no GitHub download needed):

python -m BayesianEstimationTool.ExampleInstaller

To select a different parent directory:

python -m BayesianEstimationTool.ExampleInstaller ~/Documents

This creates BET-Examples/ with the six Python examples and a Documentation/ subfolder containing Manual.pdf and Cheatsheet.pdf. Run a script with python BET-Examples/1_SimpleExampleNuts.py. The installer refuses to overwrite an existing BET-Examples/ directory. The installer is available in package builds that include bundled examples and documentation.

How to cite

If you use BET in your research, please cite:

Bravo, R., Tercan, B., Lima, E., Aguilar, B., & Yankeelov, T. (2026). BayesianEstimationTool (Version 1.0.2) [Computer software]. https://doi.org/10.5281/zenodo.23431103

This DOI identifies all versions of BET. GitHub also provides a Cite this repository option using CITATION.cff.