function quantile( p, lambda ) {
	var sigmaInv;
	var guess;
	var sigma;
	var corr;
	var x2;
	var x;
	if ( isnan( lambda ) || lambda < 0.0 ) {
		return NaN;
	}
	if ( isnan( p ) || p < 0.0 || p > 1.0 ) {
		return NaN;
	}
	if ( lambda === 0.0 ) {
		return 0.0;
	}
	if ( p === 0.0 ) {
		return 0.0;
	}
	if ( p === 1.0 ) {
		return PINF;
	}
	// Cornish-Fisher expansion:
	sigma = sqrt( lambda );
	sigmaInv = 1.0 / sigma;
	if ( p < 0.5 ) {
		x = -erfcinv( 2.0 * p ) * SQRT2;
	} else {
		x = erfcinv( 2.0 * ( 1.0 - p ) ) * SQRT2;
	}
	x2 = x * x;

	// Skewness correction:
	corr = x + (sigmaInv * ( x2 - 1.0 ) / 6.0);
	guess = round( lambda + (sigma * corr) );
	return ( cdf( guess, lambda ) >= p ) ?
		search.left( guess, p, lambda ) :
		search.right( guess, p, lambda );
}
Evaluates the quantile function for a Poisson distribution with mean parameter `lambda` at a probability `p`.

@param {Probability} p - input value
@param {NonNegativeNumber} lambda - mean parameter
@returns {NonNegativeInteger} evaluated quantile function

@example
var y = quantile( 0.5, 2.0 );
// returns 2

@example
var y = quantile( 0.9, 4.0 );
// returns 7

@example
var y = quantile( 0.1, 200.0 );
// returns 182

@example
var y = quantile( 1.1, 0.0 );
// returns NaN

@example
var y = quantile( -0.2, 0.0 );
// returns NaN

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

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