import { OpenAssistantTool } from '@openassistant/utils';
import { z } from 'zod';
import { GetValues } from '../types';
/**
 * ## rate Tool
 *
 * This tool calculates rates from a base variable and an event variable using various statistical methods.
 * It's commonly used in epidemiology and spatial analysis to standardize rates across different populations.
 *
 * ### Rate Methods
 *
 * The rate calculation method can be one of the following types:
 * - **Raw Rates**: Simple division of events by base population
 * - **Excess Risk**: Measures excess risk compared to expected values
 * - **Empirical Bayes**: Uses Bayesian smoothing to stabilize rates
 * - **Spatial Rates**: Incorporates spatial information in rate calculation
 * - **Spatial Empirical Bayes**: Combines spatial and Bayesian smoothing
 * - **EB Rate Standardization**: Empirical Bayes with rate standardization
 *
 * ### Parameters
 * - `datasetName`: Name of the dataset containing the variables
 * - `baseVariableName`: Name of the base population variable (denominator)
 * - `eventVariableName`: Name of the event variable (numerator)
 * - `rateMethod`: Rate calculation method (see above)
 * - `weightsID`: ID of spatial weights matrix (required for spatial methods)
 * - `saveData`: Whether to save the calculated rates (optional)
 * - `outputRateVariableName`: Custom name for the output rate variable (optional)
 *
 * **Example user prompts:**
 * - "Calculate the excess risk rates from the base variable 'population' and the event variable 'crimes'"
 * - "Compute spatial empirical Bayes rates for disease incidence"
 * - "Calculate raw crime rates per capita"
 *
 * ### Example
 * ```typescript
 * import { rate } from "@openassistant/geoda";
 * import { convertToVercelAiTool } from "@openassistant/utils";
 *
 * const rateTool = {
 *   ...rate,
 *   context: {
 *     getValues: async (datasetName: string, variableName: string) => {
 *       // Implementation to retrieve values from your data source
 *       return [1000, 2000, 1500, 3000, 2500, 1800, 2200, 1900, 2800, 2100];
 *     },
 *   },
 * };
 *
 * const result = await generateText({
 *   model: openai('gpt-4.1', { apiKey: key }),
 *   prompt: 'Calculate the excess risk rates from the base variable "population" and the event variable "crimes"',
 *   tools: { rate: convertToVercelAiTool(rateTool) },
 * });
 * ```
 */
export declare const rate: OpenAssistantTool<RateFunctionArgs, RateLlmResult, RateAdditionalData, RateContext>;
export type RateFunctionArgs = z.ZodObject<{
    datasetName: z.ZodString;
    baseVariableName: z.ZodString;
    eventVariableName: z.ZodString;
    rateMethod: z.ZodEnum<['Raw Rates', 'Excess Risk', 'Empirical Bayes', 'Spatial Rates', 'Spatial Empirical Bayes', 'EB Rate Standardization']>;
    weightsID: z.ZodOptional<z.ZodString>;
    saveData: z.ZodOptional<z.ZodBoolean>;
    outputRateVariableName: z.ZodOptional<z.ZodString>;
}>;
export type RateLlmResult = {
    success: boolean;
    error?: string;
    details?: string;
    result?: string;
};
export type RateAdditionalData = {
    datasetName: string;
    [key: string]: unknown;
};
export type RateContext = {
    getValues: GetValues;
};
export declare function isRateContext(context: unknown): context is RateContext;
export type RateTool = typeof rate;