Changelog

All notable changes to HypoTestX are documented here. Versions follow Semantic Versioning.

v1.0.6 — Released (current)

Routing transparency and documentation release.

  • warn_fallback parameter — analyze() now accepts warn_fallback=True (default). Emits a UserWarning when the built-in regex fallback is used, directing users to a real LLM backend for better accuracy. Suppress with warn_fallback=False.

  • routing_confidence field — HypoResult now exposes routing_confidence (float 0–1; 1.0 for LLM, 0.6 for fallback) and routing_source ("llm" or "fallback").

  • Fallback warning in summary() — when routing_source == "fallback", result.summary() appends a ⚠ notice advising the user to verify the selected test.

  • routing_source field on RoutingResult — the internal dataclass now carries routing_source alongside confidence.

  • ReadTheDocs documentation — full Sphinx/furo documentation site with quickstart, user guide, API reference, and contributing guide. Published at https://hypotestx.readthedocs.io

  • pyproject.toml docs extras updated to sphinx>=7.0, furo>=2024.1.29, myst-parser>=2.0.

  • README: fixed roadmap versioning (linear v0.1.0 → v0.2.0 → v1.0.0 sequence); added “Why HypoTestX?” comparison table; added routing_confidence and routing_source to the HypoResult reference.

v0.1.0 — Released

Initial public release.

  • Complete parametric test suite: one-sample t-test, two-sample t-test, Welch’s t-test, paired t-test, one-way ANOVA

  • Non-parametric tests: Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis

  • Categorical tests: chi-square (independence + goodness-of-fit), Fisher’s exact

  • Correlation tests: Pearson, Spearman, point-biserial

  • Pure Python math core: distributions, special functions, numerical integration — zero mandatory dependencies

  • Assumption checking: Shapiro-Wilk, Levene, Bartlett, Jarque-Bera

  • Power analysis: sample size calculation, post-hoc power for two-sample t-tests

  • Bootstrap and permutation tests

  • APA-style reporting via apa_report()

  • Structured HypoResult with statistic, p-value, effect size, CI, interpretation

v0.2.0 — Released

LLM-powered natural-language interface.

  • analyze() function — single entry-point natural-language interface

  • Plug-in LLM backend system — swap any backend with one keyword arg

    • FallbackBackend — built-in regex router (zero deps, offline, always works)

    • OllamaBackend — local Ollama (free, offline)

    • GeminiBackend — Google Gemini free tier (1 500 req/day)

    • OpenAICompatBackend — OpenAI, Groq, Together AI, Mistral, Perplexity

    • HuggingFaceBackend — HuggingFace Inference API + local transformers

    • CallableBackend — wraps any callable(messages) -> str

    • LLMBackend ABC — subclass to integrate any custom LLM

  • RoutingResult structured dataclass for intent extraction

  • SchemaInfo DataFrame summary passed to LLMs as context

  • Regex fallback pattern table covering all test types

  • 483 tests passing

v1.0.0 — Released

Production-ready release with domain packages, reporting, and visualization.

  • Domain-specific packages: hypotestx.domains — clinical/medical, A/B testing, finance, survey analysis

  • HTML / PDF reporting: generate_report(), export_html(), export_pdf() (PDF requires weasyprint)

  • Visualization: result.plot(), plot_result(), plot_distributions(), plot_p_value() — requires optional matplotlib

  • Azure OpenAI backend: backend="azure" with correct deployment URL, api-key header, and api_version parameter

  • Routing confidence warnings: routing_confidence and routing_source fields on HypoResult; UserWarning emitted when fallback is used

  • Routing validation: explicit column checks per test type with actionable error messages before dispatch

  • Structured logging: logging.getLogger("hypotestx") throughout

  • Duck-typed backends: any object exposing .route() accepted by get_backend()

  • Division-by-zero fix: Welch/Student t-tests guard against zero-variance groups

  • 532 tests passing

v1.1.0 — Planned

  • Two-way ANOVA and repeated-measures ANOVA

  • Regression-based tests (linear, logistic)

  • Automatic assumption-driven test selection

  • Streaming LLM responses for verbose mode

  • analyze() result explains why a test was chosen

v1.2.0 — Planned

  • Bayesian alternatives (Bayesian t-test, Bayes factor)

  • Time series stationarity and change-point tests

  • Meta-analysis tools

  • Interactive Jupyter widgets for results