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

	if (
		isnan( r ) ||
		isnan( p ) ||
		isnan( k ) ||
		r <= 0.0 ||
		p < 0.0 ||
		p > 1.0 ||
		k < 0.0 ||
		k > 1.0
	) {
		return NaN;
	}
	if ( k === 0.0 ) {
		return 0.0;
	}
	if ( k === 1.0 ) {
		return PINF;
	}
	q = 1.0 - p;
	mu = ( r * q ) / p;
	sigma = sqrt( r * q ) / p;
	sigmaInv = 1.0 / sigma;

	// Cornish-Fisher expansion:
	if ( k < 0.5 ) {
		x = -erfcinv( 2.0 * k ) * SQRT2;
	} else {
		x = erfcinv( 2.0 * (1.0-k) ) * SQRT2;
	}
	x2 = x * x;

	// Skewness correction:
	corr = x + (sigmaInv * ( x2 - 1.0 ) / 6.0);
	guess = round( mu + (sigma * corr) );
	return ( cdf( guess, r, p ) >= k ) ?
		search.left( guess, k, r, p ) :
		search.right( guess, k, r, p );
}
Evaluates the quantile function for a negative binomial distribution with number of successes until experiment is stopped `r` and success probability `p` at a probability `k`.

@param {Probability} k - input value
@param {PositiveNumber} r - number of successes until experiment is stopped
@param {Probability} p - success probability
@returns {NonNegativeInteger} evaluated quantile function

@example
var y = quantile( 0.9, 20.0, 0.2 );
// returns 106

@example
var y = quantile( 0.9, 20.0, 0.8 );
// returns 8

@example
var y = quantile( 0.5, 10.0, 0.4 );
// returns 14

@example
var y = quantile( 0.0, 10.0, 0.9 );
// returns 0

@example
var y = quantile( 1.1, 20.0, 0.5 );
// returns NaN

@example
var y = quantile( -0.1, 20.0, 0.5 );
// returns NaN

@example
var y = quantile( 0.5, 0.0, 0.5 );
// returns NaN

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

@example
var y = quantile( 0.3, 20.0, -1.0 );
// returns NaN

@example
var y = quantile( 0.3, 20.0, 1.5 );
// returns NaN

@example
var y = quantile( NaN, 20.0, 0.5 );
// returns NaN

@example
var y = quantile( 0.3, NaN, 0.5 );
// returns NaN

@example
var y = quantile( 0.3, 20.0, NaN );
// returns NaN