What’s New in 0.13 / 0.14

0.14 continues on data: stats::reliability (Cronbach’s α, KR-20, split-half, item analysis, κ confidence intervals and tests, Cochran’s Q, Somers’ D / Goodman–Kruskal γ, χ² residuals, Pearson inference), stats::regression (exact OLS/WLS with the full inference table, logistic regression by IRLS), stats::survival (Kaplan–Meier with Greenwood variances, Nelson–Aalen, log-rank — exact), stats::sequential (Wald’s SPRT for screening as answers arrive), stats::information (KL, JS, mutual information, exactly), stats::multivariate (multivariate normal, covariance/correlation matrices, PCA) and stats::order (order statistics as distributions). See the guide’s later sections.

Statistics on data. 0.12 made distributions compose; 0.13 turns to the data people actually collect — many raters answering many items — and answers the questions asked of it exactly. See Analysing Rater and Response Data for the walk-through.

• Inter-rater agreement (stats::agreement): percent agreement, Cohen’s κ (plain and weighted), Scott’s π, Fleiss’ κ, Gwet’s AC1, Krippendorff’s α (nominal / ordinal / interval / ratio, with missing data), the six ICC forms, Kendall’s W — every one an exact rational, matching statsmodels / the krippendorff package to the last digit.
• Label aggregation (stats::aggregation): majority and weighted votes, Dawid–Skene EM, Bradley–Terry, per-rater accuracy / precision / recall / F₁, exact Clopper–Pearson and Wilson intervals, gold-question screening.
• Hypothesis tests (stats::hypothesis): exact binomial, Fisher, McNemar and sign tests (p-values as rationals); t (Student, Welch, paired), z, one-way ANOVA; Mann–Whitney (exact null distribution or asymptotic), Wilcoxon, Kruskal–Wallis, Friedman, Spearman, Kendall, KS; χ² and G tests; effect sizes; Bonferroni / Holm / BH / BY; bootstrap and permutation; power and sample size. Statistics are exact expressions and p-values exact expressions through the symbolic StudentT / χ² / F CDFs.
• Estimation (stats::estimation): MLE and method of moments for the standard families, log-likelihood / AIC / BIC, conjugate Bayesian posteriors (Beta–Binomial, Gamma–Poisson, Normal, Dirichlet) with credible intervals and exact predictive tables.
• Markov chains (stats::markov) on exact QMatrix transition matrices: stationary distributions, classes and periods, absorption probabilities and times, hitting probabilities and times.
• Descriptive statistics (stats::data), exact: moments, quantiles (both conventions), ranks, rank correlations, robust summaries and outlier screens.
• Distribution::quantile_f64: numeric inverse CDF for every family, through the exact CDF when it cannot be compiled.