Installation

Use Python 3.11 or newer.

Install from a source checkout

git clone https://github.com/tandpds/marketbayesmeta.git
cd marketbayesmeta

python -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[dev]"

Confirm that model dependencies import from the repository virtual environment:

which python
python - <<'PY'
import pymc
import numpy
print("pymc", pymc.__version__)
print("numpy", numpy.__version__)
PY

If PyMC fails to import, recreate .venv before running model fits. Tracker and config validation are still useful without sampling, but analysis runs require a working PyMC/ArviZ stack.

Avoid mixed base conda/user-site environments for release validation. Either activate .venv or prefix checks with:

PATH=.venv/bin:$PATH make check-statistical

Developer checks

make check
make check-statistical

make check is required before finalising code or documentation changes. make check-statistical runs sampling-based contract tests and should pass in the environment used for model work.

Next steps

• Quick Start
• Run an Analysis
• Configuration Reference

Quick Start

The quickest way to see the package workflow is to run the synthetic example config.

source .venv/bin/activate
python runme.py examples/config.yaml

The example config points to examples/example_tracker.csv and writes outputs to:

output/example_geo_sales_uplift/

The example is intentionally small. It should complete and write the full artefact set, but it is not expected to be reportable because readiness is directional.

Check the config

marketbayesmeta-check-config examples/config.yaml

Run the config

marketbayesmeta-run examples/config.yaml

Review the result

Start with:

• analysis_report.md
• run_status.json
• readiness.csv
• diagnostics.csv
• prior_diagnostics.csv
• effect_preparation.csv
• ppc.csv

The relevant question is not just whether the run completed. It is whether run_status.reportable is true after reviewing diagnostics, readiness, uncertainty provenance, and sensitivity outputs.