Installation

pyLBA requires Python 3.10 or newer. It uses NumPy and pandas for data, PyMC and PyTensor for Bayesian estimation, and ArviZ for posterior summaries.

Install from GitHub

python -m pip install git+https://github.com/rgs2151/pyLBA.git

Verify the installation:

import pyLBA
print(pyLBA.__version__)

Editable installation

For local development or running the example notebook:

git clone https://github.com/rgs2151/pyLBA.git
cd pyLBA
python -m venv .venv
source .venv/bin/activate
python -m pip install -e '.[dev,docs]'

On Windows, activate the environment with .venv\Scripts\activate instead.

Build the documentation

From the repository root:

python -m pip install -e '.[docs]'
python -m sphinx -W --keep-going -b html docs docs/_build/html
python -m http.server 8000 --directory docs/_build/html

Open http://localhost:8000 to browse the generated site. Documentation builds render the guides and API reference without running simulations or MCMC.