statpkg-template

Cookiecutter template for JOSS-ready Python statistical packages — closing the CRAN publication pipeline gap.

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Motivation

R's CRAN ecosystem publishes 100–180 new statistical packages per month, the majority implementing methods directly from peer-reviewed papers. Python has no equivalent publication pipeline: PyPI has no review process, no enforced documentation standards, and no discoverability layer comparable to CRAN Task Views.

statpkg-template provides a standardized scaffold that makes publishing a Python statistical package — from paper implementation to PyPI + JOSS — as frictionless as authoring an R package for CRAN.

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What It Generates

my-stat-package/
├── src/my_stat_package/   # Main source (uv/src layout)
├── tests/                 # pytest + hypothesis property tests
├── docs/                  # MkDocs + mkdocstrings
├── paper/                 # JOSS paper.md + paper.bib
├── benchmarks/            # ASV or pytest-benchmark
├── validation/            # Reference implementation comparisons (rpy2 or manual)
├── .github/workflows/     # CI (lint, test, matrix), release, docs
├── pyproject.toml         # Complete: ruff, mypy, pytest config
├── README.md
├── ARCHITECTURE.md
├── CHANGELOG.md
├── CONTRIBUTING.md
├── LICENSE                # Apache-2.0 default
└── TODO.md

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Key Features

• JOSS-ready: includes paper/paper.md template with all required sections
• CRAN Task View mapping: pyproject.toml classifiers map to CRAN Task View categories for cross-ecosystem discoverability
• Validation-first: validation/ directory with rpy2-based comparison scaffold
• uv-native: full uv workflow, no setup.py, no requirements.txt
• Hypothesis integration: property-based test scaffold for statistical correctness
• SemVer-gated: pre-commit hooks enforce conventional commits → auto-changelog

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License

Apache-2.0 — see LICENSE.