PyStatsV1 — Applied Statistics (R ↔ Python)

PyStatsV1 provides plain, transparent Python scripts that mirror classical R textbook analyses, making it easy for students, tutors, and practitioners to:

• run statistical analyses from the command line,
• generate synthetic data for teaching,
• produce figures and JSON summaries,
• and compare outputs across R/Python.

The project follows a chapter-based structure — each chapter includes a simulator, an analyzer, Makefile targets, and CI smoke tests.

Who is this for?

PyStatsV1 is designed for:

• Students who want to run textbook-style analyses in real Python code.
• Instructors / TAs who need reproducible demos and synthetic data for lectures, labs, or assignments.
• Practitioners who prefer plain scripts and command-line tools over large frameworks.
• R users who want a clear, line-by-line bridge from R examples into Python.

---

🚀 Quick Start

1. Create and activate a virtual environment

macOS / Linux

python -m venv .venv && source .venv/bin/activate
python -m pip install -U pip
pip install -r requirements.txt

Windows (Git Bash or PowerShell)

# Git Bash first; PowerShell as fallback
python -m venv .venv; source .venv/Scripts/activate 2>/dev/null || .venv\Scripts\Activate.ps1
python -m pip install -U pip
pip install -r requirements.txt

---

📊 Chapter Scripts

Chapter 1 — Introduction

python -m scripts.ch01_introduction

Chapter 13 — Within-subjects & Mixed Models

make ch13-ci   # tiny CI smoke
make ch13      # full demo

Chapter 14 — Tutoring A/B Test (two-sample t-test)

make ch14-ci
make ch14

Chapter 15 — Reliability (Cronbach’s α, ICC, Bland–Altman)

make ch15-ci
make ch15

For an overview of what each chapter contains:

• CHAPTERS.md — coverage, commands, and outputs
• ROADMAP.md — planned chapters (e.g., Ch16 Epidemiology RR)

---

📚 Project Docs & Policies

PyStatsV1 is structured with a core set of documentation:

• CONTRIBUTING.md — environment setup, development workflow, Makefile usage, PR process.
• CODE_OF_CONDUCT.md — community expectations & enforcement.
• CHAPTERS.md — high-level description of all implemented chapters.
• ROADMAP.md — the future of the project: upcoming chapters & milestones.
• SECURITY.md — how to privately report vulnerabilities.
• SUPPORT.md — how to get help or ask questions.
• Case Study Template: docs/case_study_template.md — structure for building new chapter teaching documentation.

If you want to contribute, start with CONTRIBUTING.md and check issues labeled good first issue or help wanted.

---

🤝 Contribute in 5 minutes

Want to help but not sure where to start?

1. Browse issues labeled good first issue or help wanted.

2. Pick one small thing (typo, doc improvement, tiny refactor, or a missing test).

3. Fork & clone the repo.

4. Create and activate a virtual environment, then:

   pip install -r requirements.txt
   make lint
   make test

5. Make your change, and ensure make lint and make test both pass.

6. Open a Pull Request and briefly describe:

   • what you changed,
   • how you tested it,
   • which chapter(s) it touches, if any.

Maintainer promise: we’ll give constructive feedback and help first-time contributors land their PRs.

---

🗺️ Roadmap snapshot

High-level upcoming work (see ROADMAP.md for details):

• ✅ v0.17.0 — Onboarding and issue templates
• ⏳ Next steps:
  • Additional regression chapters (logistic, Poisson, etc.)
  • Power and sample size simulations
  • Epidemiology-focused examples (risk ratios, odds ratios)
  • More teaching case studies using docs/case_study_template.md

If you’d like to champion a specific chapter or topic, open an issue and we can design it together.

---

🧪 Development Workflow

From the project root:

make lint    # ruff check
make test    # pytest

To run chapter smoke tests:

make ch13-ci
make ch14-ci
make ch15-ci

All synthetic data is written to:

• data/synthetic/
• outputs/<chapter>/

…and ignored by Git.

---

🔀 Pull Requests

Every pull request should:

• pass make lint and make test,
• avoid committing generated outputs,
• follow the structure described in CONTRIBUTING.md.

GitHub provides:

• 🐛 Bug report template
• 💡 Feature request template
• 📘 Good first issue template
• 🔀 Pull request template

---

🔒 Security

If you believe you’ve found a security issue, do not open a public GitHub issue.
Follow the private disclosure process described in SECURITY.md.

---

💬 Community & support

• Questions?
  Open a GitHub issue with the question label.

• Using PyStatsV1 in a course?
  We’d love to hear about it — open an issue titled Course report: <institution> or mention it in your PR description.

• Feature ideas / chapter requests?
  Open an issue with the enhancement or chapter-idea label.

As the project grows, we plan to enable GitHub Discussions and possibly a lightweight chat space for instructors and contributors.

---

📜 License

MIT © 2025 Nicholas Elliott Karlson