lib/index.js
function getCDF( mu, s2 ) {
		var A = 1 / 2,
			B = Math.sqrt( s2*2 );
		/**
		* FUNCTION: cdf( x )
		*	Evaluates the cumulative distribution function at input value `x`.
		*
		* @private
		* @param {Number} x - input value
		* @returns {Number} evaluated CDF
		*/
		return function cdf( x ) {
			var C = x - mu;
			return A * ( 1 + erf( C / B ) );
		};
	}
function getCDF( mu, s2 ) {
		var A = 1 / 2,
			B = Math.sqrt( s2*2 );
		/**
		* FUNCTION: cdf( x )
		*	Evaluates the cumulative distribution function at input value `x`.
		*
		* @private
		* @param {Number} x - input value
		* @returns {Number} evaluated CDF
		*/
		return function cdf( x ) {
			var C = x - mu;
			return A * ( 1 + erf( C / B ) );
		};
	}
function getCDF ( mu , s2 ) { var A = 1 / 2 , B = Math . sqrt ( s2 * 2 ) ; /**
		* FUNCTION: cdf( x )
		*	Evaluates the cumulative distribution function at input value `x`.
		*
		* @private
		* @param {Number} x - input value
		* @returns {Number} evaluated CDF
		*/ return function cdf ( x ) { var C = x - mu ; return A * ( 1 + erf ( C / B ) ) ; } ; }
end FUNCTION getPDF() 
FUNCTION: getCDF( mu, variance )
Returns a cumulative density function for a normal distribution with mean `mu` and variance `s2`.

@private
@param {Number} mu - distribution mean
@param {Number} variance - distribution variance
@returns {Function} cumulative density function (CDF)
end FUNCTION getPDF () FUNCTION : getCDF ( mu variance ) Returns a cumulative density function for a normal distribution with mean mu and variance s2 .