text: /**
  * @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 tape = require( 'tape' );
  var isnan = require( '@stdlib/math-base-assert-is-nan' );
  var abs = require( '@stdlib/math-base-special-abs' );
  var PINF = require( '@stdlib/constants-float64-pinf' );
  var NINF = require( '@stdlib/constants-float64-ninf' );
  var EPS = require( '@stdlib/constants-float64-eps' );
  var factory = require( './../lib/factory.js' );
  
  
  // FIXTURES //
  
  var positiveMean = require( './fixtures/julia/positive_mean.json' );
  var negativeMean = require( './fixtures/julia/negative_mean.json' );
  var largeVariance = require( './fixtures/julia/large_variance.json' );
  
  
  // TESTS //
  
  tape( 'main export is a function', function test( t ) {
  	t.ok( true, __filename );
  	t.equal( typeof factory, 'function', 'main export is a function' );
  	t.end();
  });
  
  tape( 'the function returns a function', function test( t ) {
  	var logcdf = factory( 0.0, 1.0 );
  	t.equal( typeof logcdf, 'function', 'returns a function' );
  	t.end();
  });
  
  tape( 'if provided `NaN` for any parameter, the created function returns `NaN`', function test( t ) {
  	var logcdf;
  	var y;
  
  	logcdf = factory( 0.0, 1.0 );
  	y = logcdf( NaN );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	logcdf = factory( NaN, 1.0 );
  	y = logcdf( 0.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	logcdf = factory( 1.0, NaN );
  	y = logcdf( 0.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	logcdf = factory( NaN, NaN );
  	y = logcdf( 0.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	logcdf = factory( NaN, NaN );
  	y = logcdf( NaN );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	t.end();
  });
  
  tape( 'if provided a valid `mu` and `c`, the function returns a function which returns `0` when provided `+infinity` for `x`', function test( t ) {
  	var logcdf;
  	var y;
  
  	logcdf = factory( 0.0, 1.0 );
  	y = logcdf( PINF );
  	t.equal( y, 0.0, 'returns 0' );
  
  	t.end();
  });
  
  tape( 'if provided a valid `mu` and `c`, the function returns a function which returns `-infinity` when provided `-infinity` for `x`', function test( t ) {
  	var logcdf;
  	var y;
  
  	logcdf = factory( 0.0, 1.0 );
  	y = logcdf( NINF );
  	t.equal( y, NINF, 'returns -infinity' );
  
  	t.end();
  });
  
  tape( 'the created function returns `-infinity` for `x < mu`', function test( t ) {
  	var logcdf;
  	var y;
  
  	logcdf = factory( 0.0, 1.0 );
  	y= logcdf( -1.0 );
  	t.equal( y, NINF, 'returns -infinity' );
  
  	logcdf = factory( 4.0, 1.0 );
  	y = logcdf( 3.0 );
  	t.equal( y, NINF, 'returns -infinity' );
  
  	t.end();
  });
  
  tape( 'if provided a nonpositive `c`, the created function always returns `NaN`', function test( t ) {
  	var logcdf;
  	var y;
  
  	logcdf = factory( 0.0, -1.0 );
  
  	y = logcdf( 2.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	y = logcdf( 0.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	logcdf = factory( 0.0, 0.0 );
  
  	y = logcdf( 2.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	y = logcdf( 0.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	logcdf = factory( 0.0, NINF );
  	y = logcdf( 2.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	logcdf = factory( PINF, NINF );
  	y = logcdf( 2.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	logcdf = factory( NINF, NINF );
  	y = logcdf( 2.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	logcdf = factory( NaN, NINF );
  	y = logcdf( 2.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	t.end();
  });
  
  tape( 'the created function evaluates the logcdf for `x` given positive `mu`', function test( t ) {
  	var expected;
  	var logcdf;
  	var delta;
  	var tol;
  	var mu;
  	var c;
  	var i;
  	var x;
  	var y;
  
  	expected = positiveMean.expected;
  	x = positiveMean.x;
  	mu = positiveMean.mu;
  	c = positiveMean.c;
  	for ( i = 0; i < x.length; i++ ) {
  		logcdf = factory( mu[i], c[i] );
  		y = logcdf( x[i] );
  		if ( y === expected[i] ) {
  			t.equal( y, expected[i], 'x: '+x[i]+', mu:'+mu[i]+', c: '+c[i]+', y: '+y+', expected: '+expected[i] );
  		} else {
  			delta = abs( y - expected[ i ] );
  			tol = 1.0 * EPS * abs( expected[ i ] );
  			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. mu: '+mu[i]+'. c: '+c[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
  		}
  	}
  	t.end();
  });
  
  tape( 'the created function evaluates the logcdf for `x` given negative `mu`', function test( t ) {
  	var expected;
  	var logcdf;
  	var delta;
  	var tol;
  	var mu;
  	var c;
  	var i;
  	var x;
  	var y;
  
  	expected = negativeMean.expected;
  	x = negativeMean.x;
  	mu = negativeMean.mu;
  	c = negativeMean.c;
  	for ( i = 0; i < x.length; i++ ) {
  		logcdf = factory( mu[i], c[i] );
  		y = logcdf( x[i] );
  		if ( y === expected[i] ) {
  			t.equal( y, expected[i], 'x: '+x[i]+', mu:'+mu[i]+', c: '+c[i]+', y: '+y+', expected: '+expected[i] );
  		} else {
  			delta = abs( y - expected[ i ] );
  			tol = 1.0 * EPS * abs( expected[ i ] );
  			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. mu: '+mu[i]+'. c: '+c[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
  		}
  	}
  	t.end();
  });
  
  tape( 'the created function evaluates the logcdf for `x` given large variance ( = large `b`)', function test( t ) {
  	var expected;
  	var logcdf;
  	var delta;
  	var tol;
  	var mu;
  	var c;
  	var i;
  	var x;
  	var y;
  
  	expected = largeVariance.expected;
  	x = largeVariance.x;
  	mu = largeVariance.mu;
  	c = largeVariance.c;
  	for ( i = 0; i < x.length; i++ ) {
  		logcdf = factory( mu[i], c[i] );
  		y = logcdf( x[i] );
  		if ( y === expected[i] ) {
  			t.equal( y, expected[i], 'x: '+x[i]+', mu:'+mu[i]+', c: '+c[i]+', y: '+y+', expected: '+expected[i] );
  		} else {
  			delta = abs( y - expected[ i ] );
  			tol = 1.0 * EPS * abs( expected[ i ] );
  			t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. mu: '+mu[i]+'. c: '+c[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
  		}
  	}
  	t.end();
  });
  