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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 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 PINF = require( '@stdlib/constants/float64/pinf' );
var NINF = require( '@stdlib/constants/float64/ninf' );
// MAIN //
/**
* Evaluates the natural logarithm of the probability density function (PDF) for a Weibull distribution with shape parameter `k` and scale parameter `lambda` at a value `x`.
*
* @param {number} x - input value
* @param {PositiveNumber} k - shape parameter
* @param {PositiveNumber} lambda - scale parameter
* @returns {number} evaluated logarithm of probability density function
*
* @example
* var y = logpdf( 2.0, 1.0, 0.5 );
* // returns ~-3.307
*
* @example
* var y = logpdf( 0.1, 1.0, 1.0 );
* // returns ~-0.1
*
* @example
* var y = logpdf( -1.0, 4.0, 2.0 );
* // returns -Infinity
*
* @example
* var y = logpdf( NaN, 0.6, 1.0 );
* // returns NaN
*
* @example
* var y = logpdf( 0.0, NaN, 1.0 );
* // returns NaN
*
* @example
* var y = logpdf( 0.0, 0.0, NaN );
* // returns NaN
*
* @example
* var y = logpdf( 2.0, 0.0, -1.0 );
* // returns NaN
*/
function logpdf( x, k, lambda ) {
var xol;
if (
isnan( k ) ||
isnan( lambda ) ||
k <= 0.0 ||
lambda <= 0.0
) {
return NaN;
}
if ( x < 0.0 ) {
return NINF;
}
if ( x === PINF || x === NINF ) {
return NINF;
}
if ( x === 0.0 ) {
return ( k === 1.0 ) ? ln( k/lambda ): NINF;
}
xol = x / lambda;
return ln( k / lambda ) + ( ( k - 1.0 ) * ln( xol ) ) - pow( xol, k );
}
// EXPORTS //
module.exports = logpdf;
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