function kstest() {
	var nDistParams;
	var distParams;
	var distArgs;
	var options;
	var alpha;
	var opts;
	var pval;
	var stat;
	var yVal;
	var alt;
	var err;
	var idx;
	var out;
	var val;
	var i;
	var n;
	var x;
	var y;

	x = arguments[ 0 ];
	y = arguments[ 1 ];
	if ( !isNumberArray( x ) && !isTypedArrayLike( x ) ) {
		throw new TypeError( format( 'invalid argument. First argument must be a typed array or number array. Value: `%s`.', x ) );
	}
	if ( !isFunction( y ) && !isString( y ) ) {
		throw new TypeError( format( 'invalid argument. Second argument must be either a CDF function or a string. Value: `%s`.', y ) );
	}
	if ( isString( y ) ) {
		y = getCDF( y );
	}
	nDistParams = y.length - 1.0;
	n = x.length;

	distParams = new Array( nDistParams ); // eslint-disable-line stdlib/no-new-array
	for ( i = 0; i < nDistParams; i++ ) {
		idx = i + 2;
		val = arguments[ idx ];
		if ( !isNumber( val ) || isnan( val ) ) {
			throw new TypeError( format( 'invalid argument. Distribution parameter must be a number. Value: `%s`.', val ) );
		}
		distParams[ i ] = arguments[ idx ];
	}
	opts = {};
	if ( arguments.length > 2 + nDistParams ) {
		options = arguments[ 2 + nDistParams ];
		err = validate( opts, options );
		if ( err ) {
			throw err;
		}
	}
	// Make a copy to prevent mutation of x:
	x = slice.call( x );

	if ( opts.alpha === void 0 ) {
		alpha = 0.05;
	} else {
		alpha = opts.alpha;
	}
	if ( alpha < 0.0 || alpha > 1.0 ) {
		throw new RangeError( format( 'invalid option. `%s` option must be a number on the interval: [0, 1]. Option: `%f`.', 'alpha', alpha ) );
	}

	// Input data has to be sorted:
	if ( opts.sorted !== true ) {
		x.sort( ascending );
	}
	distArgs = [ null ].concat( distParams );
	for ( i = 0; i < n; i++ ) {
		distArgs[ 0 ] = x[ i ];
		yVal = y.apply( null, distArgs );
		x[ i ] = yVal - ( i / n );
	}

	alt = opts.alternative || 'two-sided';
	switch ( alt ) {
	case 'two-sided':
		stat = max( n, [ max( n, x, 1 ), max( n, subtract( 1/n, x ), 1 ) ], 1 );
		break;
	case 'greater':
		stat = max( n, subtract( 1/n, x ), 1 );
		break;
	case 'less':
		stat = max( n, x, 1 );
		break;
	default:
		throw new Error( format( 'invalid option. `%s` option must be one of the following: "%s". Option: `%s`.', 'alternative', [ 'two-sided', 'less', 'greater' ].join( '", "' ), alt ) );
	}
	if ( alt === 'two-sided' ) {
		pval = 1.0 - pKolmogorov( stat, n );
	} else {
		pval = 1.0 - pKolmogorov1( stat, n );
	}

	out = {};
	setReadOnly( out, 'rejected', pval <= alpha );
	setReadOnly( out, 'alpha', alpha );
	setReadOnly( out, 'pValue', pval );
	setReadOnly( out, 'statistic', stat );
	setReadOnly( out, 'method', 'Kolmogorov-Smirnov goodness-of-fit test' );
	setReadOnly( out, 'print', print );
	setReadOnly( out, 'alternative', alt );
	return out;
}
Computes a Kolmogorov-Smirnov goodness-of-fit test.

@param {NumericArray} x - input array holding numeric values
@param {(Function|string)} y - either a CDF function or a string denoting the name of a distribution
@param {...number} [params] - distribution parameters passed to reference CDF
@param {Options} [options] - function options
@param {number} [options.alpha=0.05] - significance level
@param {boolean} [options.sorted=false] - boolean indicating if the input array is already in sorted order
@param {string} [options.alternative="two-sided"] - string indicating whether to conduct two-sided or one-sided hypothesis test (other options: `less`, `greater`)
@throws {TypeError} argument x has to be a typed array or array of numbers
@throws {TypeError} argument y has to be a CDF function or string
@throws {TypeError} options must be an object
@throws {TypeError} alpha option has to be a number
@throws {RangeError} alpha option has to be a number in the interval `[0,1]`
@throws {TypeError} alternative option has to be a string
@throws {Error} alternative option must be `two-sided`, `less` or `greater`
@throws {TypeError} sorted option has to be a boolean
@returns {Object} test result object

@example
var out = kstest( [ 2.0, 1.0, 5.0, -5.0, 3.0, 0.5, 6.0 ], 'normal', 0.0, 1.0 );
// returns { 'pValue': ~0.015, 'statistic': ~0.556, ... }