//! Probability, descriptive and robust benchmark statistics, deterministic
//! clustering, streaming quantiles, finite Markov and hidden-state sequence
//! inference, and fairness helpers for number-domain data.
//!
//! Descriptive statistics and disparate-impact helpers also expose Claim
//! surfaces. The Claim values carry their subject, predicate, and evidence table
//! as inspectable runtime data, so callers can browse both the computed metric
//! and the inputs used to justify it. [`fit_markov`] keeps the finite vocabulary,
//! exact counts, additive smoothing, held-out likelihood, deterministic
//! serialization, and corpus provenance inspectable instead of hiding learned
//! weights. [`QuantileSketch`] makes rank error and retained memory explicit;
//! [`forward_backward`], [`viterbi`], and [`fit_hmm`] keep normalization,
//! convergence, bounded work, numerical repair, and termination evidence.
//! [`fit_kmeans`] and [`fit_gmm`] add seeded initialization, bounded convergence,
//! regularized covariance, singular-component policy, and model-selection
//! evidence without taking ownership of sequence alignment.
//! [`exact_binary_interval`], paired and clustered bootstrap, sealed
//! [`RegisteredLookSequence`] contracts, and [`fit_isotonic`] provide the
//! bounded mathematical owner for sequential study decisions. They reuse
//! [`BootstrapControl`] and keep confidence, work, cluster independence,
//! censoring, and replay evidence explicit.
pub use *;
/// Cookbook recipes for this lib, embedded at build time.
pub static RECIPES: EmbeddedDir =
include!;