/** Result of {@link chi2Contingency}. */
export interface Chi2ContingencyResult {
    chi2: number;
    pValue: number;
    dof: number;
    expected: number[][];
    cramersV: number;
}
/** Options for {@link chi2Contingency}. */
export interface Chi2ContingencyOptions {
    /** Apply the Yates continuity correction on 2x2 tables. Default true (matches scipy). */
    correction?: boolean;
}
/**
 * Chi-square test of independence on a contingency table.
 *
 * Expected counts `E_ij = rowSum_i * colSum_j / total`. `chi2 = sum((O_ij -
 * E_ij)^2 / E_ij)`, with the Yates continuity correction
 * `(|O_ij - E_ij| - 0.5)^2 / E_ij` applied on 2x2 tables when
 * `opts.correction !== false` (default true, matching
 * `scipy.stats.chi2_contingency`). `dof = (rows - 1) * (cols - 1)`; `pValue =
 * 1 - chiSquaredCDF(chi2, dof)`. `cramersV = sqrt(chi2 / (total *
 * min(rows-1, cols-1)))`.
 *
 * Complementary to (NOT a duplicate of) `chiSquareTest`'s 2D form
 * (`typed/hypothesis.js`): `chiSquareTest` covers 1D goodness-of-fit plus a
 * plain 2D independence test, while `chi2Contingency` is the
 * `scipy.stats.chi2_contingency`-parity contingency test — it adds the Yates
 * continuity correction (2x2 tables) and returns the expected-frequency
 * table plus Cramér's V effect size that `chiSquareTest` does not.
 *
 * @example
 * chi2Contingency([[10, 20], [30, 40]], { correction: false });
 * // { chi2: 0.7937, pValue: 0.373, dof: 1, expected: [[12, 18], [28, 42]], cramersV }
 */
export declare function chi2Contingency(table: readonly (readonly number[])[], opts?: Chi2ContingencyOptions): Chi2ContingencyResult;
/** Supported multiple-testing correction methods. */
export type MultipleTestMethod = 'bonferroni' | 'holm' | 'bh';
/**
 * Multiple-testing p-value adjustment, returned in the original input order.
 * Matches `statsmodels.stats.multitest.multipletests`.
 *
 * - `bonferroni`: `min(1, p_i * n)`.
 * - `holm` (step-down): sort ascending; adjusted_(k) = `min(1, max_{j<=k}
 *   (n - j + 1) * p_(j))`, enforced monotonic non-decreasing.
 * - `bh` (Benjamini-Hochberg FDR, step-up): sort ascending; adjusted_(k) =
 *   `min(1, min_{j>=k} (n / j) * p_(j))`, enforced monotonic non-decreasing
 *   from the largest p-value down.
 *
 * Same algorithm as `multipleComparison` (`typed/hypothesis.js`) — an
 * equivalent alias kept for the `statsmodels`-matching name; both names are
 * supported and always return identical results.
 *
 * @example multipleTest([0.01, 0.04, 0.5], 'bonferroni'); // [0.03, 0.12, 1]
 */
export declare function multipleTest(pValues: readonly number[], method: MultipleTestMethod): number[];
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