/**
 * Ridge regression kernel for the model-based explainers (B/C class).
 *
 * Operates on mark-level aggregates (tens to hundreds of rows, ≤ ~20
 * features), so closed-form solutions and brute-force leave-one-out are
 * cheap. No external dependencies.
 *
 * FROZEN for the executor agent: do not modify scoring logic here.
 * See docs/explain-data-worklog.md §1.
 */
export interface IRidgeModel {
    beta: number[];
    intercept: number;
}
/** Gaussian elimination with partial pivoting; A is modified-safe (copied). */
export declare function solveLinearSystem(A: number[][], b: number[]): number[];
/**
 * Closed-form ridge fit with centered features and target (the intercept is
 * not penalized). λ = 0 reduces to OLS.
 */
export declare function fitRidge(X: number[][], y: number[], lambda: number): IRidgeModel;
export declare function predictRidge(model: IRidgeModel, X: number[][]): number[];
/**
 * Leave-one-out cross-validated R². Negative values mean the model predicts
 * held-out marks worse than the mean — an honest overfitting signal that a
 * training-set R² would hide (per-mark dummies fit perfectly in-sample).
 */
export declare function looCvR2(X: number[][], y: number[], lambda: number): number;
export declare const DEFAULT_LAMBDA_GRID: number[];
/** pick λ maximizing LOO-CV R² over a fixed grid — deterministic */
export declare function selectLambda(X: number[][], y: number[], grid?: number[]): {
    lambda: number;
    cvR2: number;
};
/** standardize a column to zero mean / unit variance; constant columns → all zeros */
export declare function standardize(values: number[]): number[];
/**
 * Deterministic PRNG (mulberry32). `Math.random` is forbidden in the explain
 * module: the same view state must always produce the same explanations.
 */
export declare function mulberry32(seed: number): () => number;
/** Fisher–Yates shuffle with an injected PRNG */
export declare function shuffled<T>(items: T[], rand: () => number): T[];
/**
 * One-sided permutation p-value with add-one smoothing:
 * P(score ≥ real | null). Guards the "lucky prediction" failure mode.
 */
export declare function permutationPValue(realScore: number, permutedScores: number[]): number;