0.1.0 - 2026-06-30
- Added descriptive statistics for one-dimensional finite samples.
- Added Normal and Student's t distribution helpers.
- Added one-sample, paired, Welch, and pooled t-tests.
- Added average-rank utilities.
- Added asymptotic, exact, and auto Mann-Whitney U tests.
- Added JSONL fixtures generated from pinned NumPy, SciPy, and Statsmodels references.
- Added upstream-derived fixture coverage notes.
Added
nan_policy: :raise | :propagate | :omitfor current public statistics APIs.- Added t-test confidence intervals and optional Cohen's d / Hedges' g effect sizes.
- Added Mann-Whitney common-language and rank-biserial effect sizes.
- Added
effect_size.cliffs_deltaas an alias of Mann-Whitney rank-biserial. - Added dataframe-style
columns:andpairs:wrappers for t-tests and Mann-Whitney U tests, including optionalExplorer.DataFramesupport when Explorer is loaded by the caller. - Added opt-in tensor extraction for dataframe columns with
input: :tensorand opt-in Nx reduction paths withbackend: :tensor. - Added the
Statwise.VisualizationAPI for semantic chart construction, Vega-Lite export, optional Livebook/Kino display, themes, faceting, and composition. - Added statistical result annotations for categorical plots, including t-test and Mann-Whitney comparison brackets, computed per-facet tests, and p-value/statistic/effect-size labels.
- Added runnable Livebook galleries for visualization features and statistical test selection, variants, dataframe-style inputs, and plot annotations.
- Added a Python-reference benchmark harness for comparing Statwise against pinned NumPy, SciPy, and Statsmodels calls.
- Added benchmark JSON output and baseline comparison support for regression checks.