export declare enum RatesOptions {
    RawRates = "Raw Rates",
    ExcessRisk = "Excess Risk",
    EmpiricalBayes = "Empirical Bayes",
    SpatialRates = "Spatial Rates",
    SpatialEmpiricalBayes = "Spatial Empirical Bayes",
    EBRateStandardization = "EB Rate Standardization"
}
export type CalculateRatesProps = {
    eventValues: number[];
    baseValues: number[];
    method: string;
    neighbors?: number[][];
};
export declare function calculateRates({ eventValues, baseValues, method, neighbors, }: CalculateRatesProps): number[];
/**
 * Compute Raw Rate or crude rate (proportion), the simple ratio of the events
 * (number of lung cancer cases) over the population at risk (the county population).
 *
 * @param baseValues The values of base variable.
 * @param eventValues The values of event variable.
 * @returns The rates values.
 */
export declare function rawRates(baseValues: number[], eventValues: number[]): number[];
/**
 * ## Description
 * Compute excess risk (relative risk), the ratio of the observed rate at a location to some reference rate.
 *
 * The reference risk ($\bar{\pi}$) is estimated from the aggregate of all observations as:
 * $\bar{\pi} = \frac{\sum O_i}{\sum P_i}$
 * where $O_i$ is the observed number of events and $P_i$ is the population/denominator.
 * This is not a simple average of rates, but rather a population-weighted average
 * that properly assigns the contribution of each area to the overall total.
 *
 * The expected value ($E_i$) for each observation is then calculated as:
 * $E_i = \bar{\pi} \times P_i$
 *
 * The relative risk ($RR_i$) then follows as:
 * $RR_i = \frac{r_i}{\bar{\pi_i}} = \frac{O_i/P_i}{E_i/P_i} = \frac{O_i}{E_i}$
 *
 * If an area matches the (regional) reference rate, the corresponding relative risk is one. Values greater
 * than one suggest an excess, whereas values smaller than one suggest a shortfall. The interpretation
 * depends on the context. For example, in disease analysis, a relative risk larger than one would indicate
 * an area where the prevalence of the disease is greater than would be expected. In regional economics,
 * a location quotient greater than one, suggests employment in a sector that exceeds the local needs,
 * implying an export sector.
 *
 * In public health, this ratio is known as standardized mortality rate (SMR).
 * In regional economics, when applied to employment sectors, it's called a location quotient (LQ).
 *
 * @example
 * ```ts
 * import { excessRisk } from '@geoda/core';
 * const baseValues = [100, 200, 300, 400, 500];
 * const eventValues = [10, 20, 30, 40, 50];
 * const rates = excessRisk(baseValues, eventValues);
 * ```
 *
 * @param baseValues The values of base variable.
 * @param eventValues The values of event variable.
 * @returns The rates values.
 */
export declare function excessRisk(baseValues: number[], eventValues: number[]): number[];
/**
 * ## Description
 * Compute the empirical Bayes smoothed rates using the Poisson-Gamma model.
 *
 * The EB estimate for risk in location i is:
 * $\pi_i^{EB} = w_i \cdot r_i + (1 - w_i) \cdot \theta$
 *
 * where:
 * - $r_i$ is the crude (raw) rate
 * - $\theta$ is the reference rate (prior mean)
 * - $w_i$ is the weight calculated as: $w_i = \sigma^2/(\sigma^2 + \mu/P_i)$
 * - $P_i$ is the population at risk in area i
 * - $\mu$ and $\sigma^2$ are the mean and variance of the prior distribution
 *
 * The method:
 * 1. Estimates $\theta$ (theta1) as the reference rate: $\sum O_i/\sum P_i$
 * 2. Estimates $\sigma^2$ (theta2) using the formula: $(\sum P_i(r_i - \mu)^2/\sum P_i) - \mu/(\sum P_i/n)$
 * 3. If $\sigma^2$ is negative, sets it to 0 (conventional approach)
 * 4. Computes weights and final smoothed rates
 *
 * Small areas (with small population at risk) will have their rates adjusted considerably,
 * while larger areas will see minimal changes.
 *
 * @param baseValues The values of base variable (P_i, population at risk).
 * @param eventValues The values of event variable (O_i, observed events).
 * @returns The empirical Bayes smoothed rates.
 */
export declare function empiricalBayes(baseValues: number[], eventValues: number[]): number[];
/**
 * ## Description
 * Compute the spatial empirical Bayes smoothed rates using a local reference rate for each observation.
 *
 * For each location i, the reference mean ($\mu_i$) is computed from its spatial window as:
 * $$\mu_i = \frac{\sum_j w_{ij}O_j}{\sum_j w_{ij}P_j}$$
 *
 * The local prior variance ($\sigma^2_i$) is estimated as:
 * $$\sigma^2_i = \frac{\sum_j w_{ij}P_j(r_j - \mu_i)^2}{\sum_j w_{ij}P_j} - \frac{\mu_i}{\sum_j w_{ij}P_i/(k_i + 1)}$$
 *
 * where:
 * - $w_{ij}$ are binary spatial weights (1 for neighbors, 0 otherwise)
 * - $O_j$ are observed events in area j
 * - $P_j$ are populations at risk in area j
 * - $r_j$ are crude rates in area j
 * - $k_i$ is the number of neighbors of area i
 *
 * Key differences from standard EB:
 * 1. Uses a local reference rate specific to each observation's spatial window
 * 2. Requires sufficient observations in the reference window for effective smoothing
 * 3. Block weights are useful to avoid irregularity in neighbor counts
 *
 * Note: If the estimated variance is negative, it is set to zero as in standard EB.
 *
 * @param baseValues The values of base variable (populations at risk, $P_i$).
 * @param eventValues The values of event variable (observed events, $O_i$).
 * @param neighbors The list of neighbors for each location.
 * @returns The spatial empirical Bayes smoothed rates.
 */
export declare function spatialEmpiricalBayes(baseValues: number[], eventValues: number[], neighbors: number[][]): number[];
/**
 * Compute the spatial rates, which is the ratio of the events (number of lung cancer cases)
 * over the population at risk (the county population) and its neighbors.
 *
 * @param baseValues The values of base variable.
 * @param eventValues The values of event variable.
 * @param neighbors The list of neighbors for each value.
 * @returns The rates values.
 */
export declare function spatialRates(baseValues: number[], eventValues: number[], neighbors: number[][]): number[];
export declare function rateStandardizeEB(baseValues: number[], eventValues: number[]): number[];