@stdlib/stats/base/dists/negative-binomial/pmf/lib/factory.js

Standard library statistical functions.

/**
* @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 isNonNegativeInteger = require( '@stdlib/math/base/assert/is-nonnegative-integer' );
var constantFunction = require( '@stdlib/utils/constant-function' );
var isnan = require( '@stdlib/math/base/assert/is-nan' );
var ibetaDerivative = require( './ibeta_derivative.js' );


// MAIN //

/**
* Returns a function for evaluating the probability mass function (PMF) for a negative binomial distribution with number of successes until experiment is stopped `r` and success probability `p`.
*
* @param {PositiveNumber} r - number of successes until experiment is stopped
* @param {Probability} p - success probability
* @returns {Function} PMF
*
* @example
* var pmf = factory( 10, 0.5 );
* var y = pmf( 3.0 );
* // returns ~0.027
*
* y = pmf( 5.0 );
* // returns ~0.061
*/
function factory( r, p ) {
	if (
		isnan( r ) ||
		isnan( p ) ||
		r <= 0.0 ||
		p <= 0.0 ||
		p > 1.0
	) {
		return constantFunction( NaN );
	}
	return pmf;

	/**
	* Evaluates the probability mass function (PMF) for a negative binomial distribution.
	*
	* @private
	* @param {number} x - input value
	* @returns {Probability} evaluated PMF
	*
	* @example
	* var y = pmf( 2.0 );
	* // returns <number>
	*/
	function pmf( x ) {
		if ( isnan( x ) ) {
			return NaN;
		}
		if ( !isNonNegativeInteger( x ) ) {
			return 0.0;
		}
		return ( p / ( r + x ) ) * ibetaDerivative( p, r, x + 1.0 );
	}
}


// EXPORTS //

module.exports = factory;