import { KernelDensityEstimateConfig, KernelDensityEstimateConfigBase } from '../kernelDensityEstimate.js';
import * as utilities from '../utilities.js';
export interface SplitMultiModalDistributionConfigBase extends KernelDensityEstimateConfigBase {
    noiseValuesPerSample?: number;
    random?: () => number;
    iterations?: number;
}
export type SplitMultiModalDistributionConfig = SplitMultiModalDistributionConfigBase & utilities.DataOrSortedData;
export type NoiseOptions = Pick<SplitMultiModalDistributionConfig, 'getBandwidth' | 'getThreshold' | 'noiseValuesPerSample' | 'random' | 'data' | 'sortedData'>;
export declare function getLocalMaxima(config: KernelDensityEstimateConfig): number[];
export declare function generateNoise({ data, sortedData, getThreshold, getBandwidth, noiseValuesPerSample, random, }: NoiseOptions): number[];
/**
 * Splits a multimodal dataset into unimodal datasets.
 * Uses kernel density estimation for multimodal distribution detection.
 * See http://adereth.github.io/blog/2014/10/12/silvermans-mode-detection-method-explained/
 */
export declare function splitMultimodalDistributionUsingKernelDensityEstimation(config: SplitMultiModalDistributionConfig): number[][];
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