function quantile( r, n, p ) {
	var sigmaInv;
	var guess;
	var sigma;
	var corr;
	var mu;
	var x2;
	var x;

	if (
		isnan( r ) ||
		isnan( n ) ||
		isnan( p ) ||
		r < 0.0 ||
		r > 1.0 ||
		p < 0.0 ||
		p > 1.0 ||
		!isNonNegativeInteger( n ) ||
		n === PINF
	) {
		return NaN;
	}
	if ( r === 1.0 || p === 1.0 ) {
		return n;
	}
	if ( r === 0.0 || p === 0.0 || n === 0 ) {
		return 0.0;
	}
	// Cornish-Fisher expansion:
	mu = n * p;
	sigma = sqrt( n * p * ( 1.0-p ) );
	sigmaInv = 1.0 / sigma;
	if ( r < 0.5 ) {
		x = -erfcinv( 2.0 * r ) * SQRT2;
	} else {
		x = erfcinv( 2.0 * ( 1.0-r ) ) * SQRT2;
	}
	x2 = x * x;

	// Skewness correction:
	corr = x + ( sigmaInv * ( x2-1.0 ) / 6.0 );
	guess = round( mu + (sigma * corr) );
	if ( cdf( guess, n, p ) >= r ) {
		return searchLeft( guess, r, n, p );
	}
	return searchRight( guess, r, n, p );
}
Evaluates the quantile function for a binomial distribution with number of trials `n` and success probability `p` at a probability `r`.

@param {Probability} r - input value
@param {NonNegativeInteger} n - number of trials
@param {Probability} p  - success probability
@returns {NonNegativeInteger} evaluated quantile function

@example
var y = quantile( 0.4, 20, 0.2 );
// returns 3

@example
var y = quantile( 0.8, 20, 0.2 );
// returns 5

@example
var y = quantile( 0.5, 10, 0.4 );
// returns 4

@example
var y = quantile( 0.0, 10, 0.4 );
// returns 0

@example
var y = quantile( 1.0, 10, 0.4 );
// returns 10

@example
var y = quantile( NaN, 20, 0.5 );
// returns NaN

@example
var y = quantile( 0.2, NaN, 0.5 );
// returns NaN

@example
var y = quantile( 0.2, 20, NaN );
// returns NaN

@example
var y = quantile( 0.5, 1.5, 0.5 );
// returns NaN

@example
var y = quantile( 0.5, -2.0, 0.5 );
// returns NaN

@example
var y = quantile( 0.5, 20, -1.0 );
// returns NaN

@example
var y = quantile( 0.5, 20, 1.5 );
// returns NaN