import { SpatialErrorResult } from './spatial-error';
import { SpatialLagResult } from './spatial-lag';
export declare function dotProduct(x: number[], y: number[]): Promise<number>;
export type LinearRegressionProps = {
    x: number[][];
    y: number[];
    weights?: number[][];
    weightsValues?: number[][];
    weightsId?: string;
    xNames: string[];
    yName: string;
    datasetName: string;
    xUndefs?: number[][];
    yUndefs?: number[];
};
export type LinearRegressionResult = {
    type: string;
    dependentVariable: string;
    independentVariables: string[];
    title: string;
    datasetName: string;
    weightsId?: string;
    'Number of Observations': number;
    'Mean Dependent Var': number;
    'Number of Variables': number;
    'SD Dependent Var': number;
    'Degrees of Freedom': number;
    'R-squared': number;
    'Adjusted R-squared': number;
    'F-statistic': number;
    'Prob(F-statistic)': number;
    'Sum Squared Residual': number;
    'Log Likelihood': number;
    'Sigma-Square': number;
    'Akaike Info Criterion': number;
    'SE of Regression': number;
    'Schwarz Criterion': number;
    'Sigma-Square ML': number;
    'Variable Coefficients': Array<{
        Variable: string;
        Coefficient: number;
        'Std Error': number;
        't-Statistic': number;
        Probability: number;
    }>;
    'REGRESSION DIAGNOSTICS': {
        'MULTICOLLINEARITY CONDITION NUMBER': number;
        'TEST ON NORMALITY OF ERRORS': {
            Test: string;
            'Jarque-Bera DF': number;
            'Jarque-Bera Value': number;
            'Jarque-Bera Probability': number;
        };
    };
    'DIAGNOSTICS FOR HETEROSKEDASTICITY': {
        'BREUSCH-PAGAN TEST': {
            Test: string;
            'Breusch-Pagan DF': number;
            'Breusch-Pagan Value': number;
            'Breusch-Pagan Probability': number;
        };
        'KOENKER-Bassett TEST': {
            Test: string;
            'Koenker-Bassett DF': number;
            'Koenker-Bassett Value': number;
            'Koenker-Bassett Probability': number;
        };
    };
    'DIAGNOSTICS FOR SPATIAL DEPENDENCE': {
        "Moran's I (error)": {
            Test: string;
            "Moran's I (error)": number;
            'Moran’s I (error) Z': number;
            'Moran’s I (error) Probability': number;
        };
        'Lagrange Multiplier (lag)': {
            Test: string;
            'Lagrange Multiplier (lag) DF': number;
            'Lagrange Multiplier (lag) Value': number;
            'Lagrange Multiplier (lag) Probability': number;
        };
        'Robust LM (lag)': {
            Test: string;
            'Robust LM (lag) DF': number;
            'Robust LM (lag) Value': number;
            'Robust LM (lag) Probability': number;
        };
        'Lagrange Multiplier (error)': {
            Test: string;
            'Lagrange Multiplier (error) DF': number;
            'Lagrange Multiplier (error) Value': number;
            'Lagrange Multiplier (error) Probability': number;
        };
        'Robust LM (error)': {
            Test: string;
            'Robust LM (error) DF': number;
            'Robust LM (error) Value': number;
            'Robust LM (error) Probability': number;
        };
        'Lagrange Multiplier (SARMA)': {
            Test: string;
            'Lagrange Multiplier (SARMA) DF': number;
            'Lagrange Multiplier (SARMA) Value': number;
            'Lagrange Multiplier (SARMA) Probability': number;
        };
    };
};
/**
 * Perform a linear regression analysis using OLS.
 *
 * ## Example
 * ```typescript
 * import { linearRegression } from '@geodash/regression';
 *
 * const result = await linearRegression({
 *   x: [[1, 2, 3], [4, 5, 6]],
 *   y: [1, 2, 3],
 *   xNames: ['x1', 'x2', 'x3'],
 *   yName: 'y',
 *   datasetName: 'dataset',
 * });
 * ```
 *
 * ## Example with spatial diagnostics
 * ```typescript
 * import { linearRegression } from '@geodash/regression';
 *
 * // two independent variables, one dependent variable, and weights
 * // three observations
 * const weights = [[1], [0, 2], [0]];
 * const weightsValues = [1.0, 1.0, 1.0];
 *
 * const result = await linearRegression({
 *   x: [[1, 2, 3], [4, 5, 6]],
 *   y: [1, 2, 3],
 *   xNames: ['x1', 'x2'],
 *   yName: 'y',
 *   datasetName: 'dataset',
 *   weightsId: 'weights',
 *   weights
 * });
 * ```
 *
 * @param x - The independent variables.
 * @param y - The dependent variable.
 * @param weightsId - The id of the weights to use.
 * @param weights - The weights to use.
 * @param weightsValues - The values of the weights.
 * @param xNames - The names of the independent variables.
 * @param yName - The name of the dependent variable.
 * @param datasetName - The name of the dataset.
 * @param xUndefs - The undefined values of the independent variables.
 * @param yUndefs - The undefined values of the dependent variable.
 * @returns The result of the linear regression analysis.
 */
export declare function linearRegression({ x, y, weightsId, weights, weightsValues, xNames, yName, datasetName, xUndefs, yUndefs, }: LinearRegressionProps): Promise<LinearRegressionResult>;
export declare function printLinearRegressionResult(regressionReport: LinearRegressionResult): string;
export declare function printNumber(num: number): string;
export declare function printVariableCoefficients(report: LinearRegressionResult | SpatialErrorResult | SpatialLagResult): string;
export declare function printLinearRegressionResultUsingMarkdown(regressionReport: LinearRegressionResult): string;
/**
 * function to check which spatail model should be used based on spatial diagnostics
 */
export declare function selectSpatialModel(regressionReport: LinearRegressionResult): string;