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ROADMAP.md — PyStatsV1 Development Roadmap

This roadmap describes the planned evolution of PyStatsV1 from v0.17 through v1.0.
It is intentionally modular, so contributors can pick up self‑contained tasks.


🌱 v0.17.0 — Onboarding & First Issues (CURRENT MILESTONE)

Goals:

  • Add full onboarding:
    • CONTRIBUTING.md
    • CODE_OF_CONDUCT.md
    • SECURITY.md
    • SUPPORT.md
    • Issue + PR templates
  • Add documentation index (CHAPTERS.md, ROADMAP.md)
  • Publish first “Good First Issues”

New contributor‑friendly tasks:

  • Ch14 “Explain Mode”
  • Ch15 additional reliability metrics
  • Epidemiology RR‑with‑strata simulator + analyzer skeleton

Status: 🔵 In progress


🌿 v0.18.0 — Explainable Statistics

Goals:

  • Add “Explain Mode” to existing chapters:
    • Step‑by‑step t-test calculations
    • Show algebra behind Cronbach’s Alpha
    • Show ICC derivation table
  • Add optional verbose tracing mode across scripts

Status: 🟡 Planned


🌾 v0.19.0 — Epidemiology & Risk Ratios

New case study:

“Risk Ratio With Stratification”

Includes:

  • Simulator for a 2×K strata study (e.g., gender, age groups)
  • Analyzer computing:
    • Crude RR
    • Mantel–Haenszel pooled RR
    • Confidence intervals
    • Optional Woolf test for homogeneity
  • Visualizations (forest plot)

Status: 🟡 Planned


🌾 v0.20.0 — Power & Sample Size Tools

Add utilities such as:

  • Power for two‑sample t-test
  • Power for paired design
  • Confidence interval planning
  • Monte‑Carlo power exploration

Status: 🟡 Planned


🌾 v0.21.0 — Regression & Model Diagnostics

Case studies:

  • Linear regression
  • Residual analysis
  • Influential points
  • Model comparison
  • Bootstrapped intervals

Status: 🟡 Planned


🌻 v0.22.0 — GLMs & Count Data

Case study modules:

  • Poisson regression
  • Negative binomial
  • Logistic regression
  • Overdispersion diagnostics

Status: 🟡 Planned


🌻 v0.23.0 — Bayesian Mirrors

Re‑implement selected chapters using:

  • PyMC
  • Bambi

Focus:

  • Bayesian equivalents of Ch14 + Ch15
  • Posterior predictive checks
  • Visualization of priors and posteriors

Status: 🔵 Exploration


🌞 v1.0.0 — Stable Educational Release

Criteria:

  • Fully documented (tutorial + examples)
  • CI‑green on Windows & Linux
  • Reproducible simulations
  • All chapters pass Lint, Test, Smoke
  • Clear contributor pathways
  • Zero known major issues

If you'd like to contribute to any milestone, visit:
👉 Issues: https://github.com/PyStatsV1/PyStatsV1/issues
👉 Contributing Guide: CONTRIBUTING.md