/**
 * Quantile / IQR utilities for the extreme-value explainer.
 *
 * FROZEN for the executor agent: do not modify scoring logic here.
 * See docs/explain-data-worklog.md §1.
 */
/** Type-7 (linear interpolation) quantile of a pre-sorted ascending array. */
export declare function quantileSorted(sorted: number[], p: number): number;
export interface IIqrOutlierResult {
    q1: number;
    q3: number;
    iqr: number;
    lowerFence: number;
    upperFence: number;
    /** indices into the ORIGINAL (unsorted) values array */
    outlierIndices: number[];
}
/** Tukey's fences: values beyond q1/q3 ± 1.5·IQR. */
export declare function detectIqrOutliers(values: number[], k?: number): IIqrOutlierResult;
export type ISensitiveAggregator = 'sum' | 'mean' | 'stdev' | 'variance';
export declare function computeAggregate(values: number[], agg: ISensitiveAggregator): number;
export interface IOutlierImpact {
    /** aggregate over all values */
    aggregateAll: number;
    /** aggregate with outlier values removed */
    aggregateWithout: number;
    /** relative change in the aggregate caused by the outliers, in [0, ∞) */
    impact: number;
}
/**
 * How much do the outlier rows distort the aggregate the user is looking at?
 * `impact` is relative to the full aggregate; when the full aggregate is ~0,
 * the change is scaled by the values' dispersion instead so the ratio stays
 * meaningful.
 */
export declare function outlierImpact(values: number[], outlierIndices: number[], agg: ISensitiveAggregator): IOutlierImpact;