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Contributing to fairscope

Thanks for your interest. fairscope is built incrementally, one module per phase, with regression tests as the credibility anchor.

Development setup

git clone https://github.com/Rajveer-code/fairscope
cd fairscope
python -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -e ".[dev]"
pre-commit install
pytest

Standards

  • Every function: full type hints; NumPy-style docstring with a runnable Examples block; input validation with clear errors; no silent failures; deterministic given a seed (expose random_state on anything stochastic).
  • Tests are mandatory. Where an authoritative reference value exists (e.g. DeLong 1988, statsmodels multitest), test against it — not against a paper. Where testing against a paper, assert direction + approximate magnitude within a stated tolerance and document any discrepancy. Committed data fixtures are small subsamples, labeled as such in the docstring; they do not reproduce a full published run.
  • Formatting/linting: black + ruff (run automatically by pre-commit).
  • Coverage: enforced overall package floor ≥70% (--cov-fail-under=70 in CI); target ≥85% on core/.

Honesty rules (non-negotiable)

  1. No invented mathematics — every method ports a published paper and cites it.
  2. No fabricated user counts, stars, downloads, or adoption claims anywhere.
  3. If a paper is ambiguous about a parameter, open an issue and ask — do not invent it.
  4. If a method does not match what a paper actually contains, name the function for what it generically does and flag the mismatch.

Commits

Small, logical, self-contained commits — never batch a whole phase into one commit. Each commit message should describe a real design decision or fix.