All files / dists/signrank/pdf/lib main.js

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/**
* @license Apache-2.0
*
* Copyright (c) 2020 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 isPositiveInteger = require( '@stdlib/math/base/assert/is-positive-integer' );
var isInteger = require( '@stdlib/math/base/assert/is-integer' );
var isfinite = require( '@stdlib/math/base/assert/is-finite' );
var isnan = require( '@stdlib/math/base/assert/is-nan' );
var exp = require( '@stdlib/math/base/special/exp' );
var ln = require( '@stdlib/math/base/special/ln' );
var LN2 = require( '@stdlib/constants/float64/ln-two' );
var weights = require( './weights.js' );
 
 
// MAIN //
 
/**
* Evaluates the probability density function (PDF) of the Wilcoxon signed rank test statistic with `n` observations.
*
* @param {number} x - input value
* @param {PositiveInteger} n - number of observations
* @returns {Probability} evaluated PDF
*
* @example
* var y = pdf( 7.0, 9 );
* // returns ~0.01
*
* @example
* var y = pdf( 7.0, 6 );
* // returns ~0.063
*
* @example
* var y = pdf( -1.0, 40 );
* // returns 0.0
*
* @example
* var y = pdf( NaN, 10 );
* // returns NaN
*
* @example
* var y = pdf( 0.0, NaN );
* // returns NaN
*
* @example
* var y = pdf( 2.0, -1 );
* // returns NaN
*
* @example
* var y = pdf( 2.0, 1.8 );
* // returns NaN
*/
function pdf( x, n ) {
	var mlim;
	if (
		isnan( x ) ||
		!isPositiveInteger( n ) ||
		!isfinite( n )
	) {
		return NaN;
	}
	if ( !isInteger( x ) ) {
		return 0.0;
	}
	mlim = ( n * ( n + 1 ) ) / 2;
	if ( x < 0.0 || x > mlim ) {
		return 0.0;
	}
	return exp( ln( weights( x, n ) ) - ( n * LN2 ) );
}
 
 
// EXPORTS //
 
module.exports = pdf;