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@stdlib/stats

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Standard library statistical functions.

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/** * @license Apache-2.0 * * Copyright (c) 2018 The Stdlib Authors. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ 'use strict'; // MODULES // var constantFunction = require( '@stdlib/utils/constant-function' ); var isnan = require( '@stdlib/math/base/assert/is-nan' ); var pow = require( '@stdlib/math/base/special/pow' ); var ln = require( '@stdlib/math/base/special/ln' ); var NINF = require( '@stdlib/constants/float64/ninf' ); var PI = require( '@stdlib/constants/float64/pi' ); // MAIN // /** * Returns a function for evaluating the natural logarithm of the probability density function (PDF) for a lognormal distribution with location parameter `mu` and scale parameter `sigma`. * * @param {number} mu - location parameter * @param {PositiveNumber} sigma - scale parameter * @returns {Function} logPDF * * @example * var logpdf = factory( 4.0, 2.0 ); * var y = logpdf( 10.0 ); * // returns ~-4.275 * * y = logpdf( 2.0 ); * // returns ~-3.672 */ function factory( mu, sigma ) { var s2; var A; var B; if ( isnan( mu ) || isnan( sigma ) || sigma <= 0.0 ) { return constantFunction( NaN ); } s2 = pow( sigma, 2.0 ); A = -0.5 * ln( 2.0 * s2 * PI ); B = -1.0 / ( 2.0 * s2 ); return logpdf; /** * Evaluates the natural logarithm of the probability density function (PDF) for a lognormal distribution. * * @private * @param {number} x - input value * @returns {number} evaluated logPDF * * @example * var y = logpdf( 2.5 ); * // returns <number> */ function logpdf( x ) { if ( isnan( x ) ) { return NaN; } if ( x <= 0.0 ) { return NINF; } return A - ln( x ) + ( B * pow( ln(x) - mu, 2.0 ) ); } } // EXPORTS // module.exports = factory;