/**
 * Compute z-scores for `source`: (x - mean) / std (ddof=1). NaNs preserved.
 * @param source Input array
 * @param options Optional `{ skipna? }` to ignore NaNs
 * @returns Float64Array of z-scores
 */
export declare function zscore(source: ArrayLike<number>, skipna?: boolean): Float64Array;
/**
 * Normalize to [0,1] by min/max scaling. Preserves NaNs when `skipna` is true.
 * @param source Input array
 * @param options Optional `{ skipna? }`
 * @returns Float64Array of normalized values
 */
export declare function norminmax(source: ArrayLike<number>, skipna?: boolean): Float64Array;
/**
 * Pearson correlation between `x` and `y`. When `skipna` is true only
 * pairwise-valid entries are used; otherwise a dense fast-path is taken.
 * @param x First input array
 * @param y Second input array
 * @param options Optional `{ skipna? }`
 * @returns Correlation coefficient or NaN
 */
export declare function corr(x: ArrayLike<number>, y: ArrayLike<number>, skipna?: boolean): number;
/**
 * Rolling Pearson correlation computed from rolling covariance and stddev.
 * Supports alignment modes via `options.outLength` ('min' or 'max').
 * @param x First input array
 * @param y Second input array
 * @param period Window length
 * @param options Optional `{ skipna?, outLength? }`
 * @returns Float64Array of rolling correlations
 */
export declare function rollcorr(x: ArrayLike<number>, y: ArrayLike<number>, period: number, options?: {
    skipna?: boolean;
    outLength?: 'min' | 'max';
}): Float64Array;
/**
 * Winsorize values to the given lower and upper quantile bounds.
 * Preserves NaNs when `skipna` is true.
 * @param source Input array
 * @param options Optional `{ lower?, upper?, skipna? }` where bounds are in [0,1]
 * @returns Float64Array of winsorized values
 */
export declare function winsorize(source: ArrayLike<number>, options?: {
    lower?: number;
    upper?: number;
    skipna?: boolean;
}): Float64Array;