function incrpcorrdist( meanx, meany ) {
	var pcorr;
	if ( arguments.length ) {
		if ( !isNumber( meanx ) ) {
			throw new TypeError( format( 'invalid argument. First argument must be a number. Value: `%s`.', meanx ) );
		}
		if ( !isNumber( meany ) ) {
			throw new TypeError( format( 'invalid argument. Second argument must be a number. Value: `%s`.', meany ) );
		}
		pcorr = incrpcorr( meanx, meany );
	} else {
		pcorr = incrpcorr();
	}
	return accumulator;

	/**
	* If provided input values, the accumulator function returns an updated sample correlation distance. If not provided input values, the accumulator function returns the current sample correlation distance.
	*
	* @private
	* @param {number} [x] - new value
	* @param {number} [y] - new value
	* @returns {(number|null)} sample correlation distance or null
	*/
	function accumulator( x, y ) {
		var r;
		if ( arguments.length === 0 ) {
			r = pcorr();
			if ( r === null ) {
				return r;
			}
			return 1.0 - r;
		}
		return 1.0 - pcorr( x, y );
	}
}
Returns an accumulator function which incrementally computes a sample Pearson product-moment correlation distance.

## Method

-   The sample Pearson product-moment correlation distance is defined as

    ```tex
    d = 1 - r = 1 - \frac{\operatorname{cov}_n(x,y)}{\sigma_x \sigma_y}
    ```

-   The implementation thus computes the sample Pearson product-moment correlation coefficient \\(r\\) and subtracts the coefficient from 1.

@param {number} [meanx] - mean value
@param {number} [meany] - mean value
@throws {TypeError} first argument must be a number
@throws {TypeError} second argument must be a number
@returns {Function} accumulator function

@example
var accumulator = incrpcorrdist();

var d = accumulator();
// returns null

d = accumulator( 2.0, 1.0 );
// returns 1.0

d = accumulator( -5.0, 3.14 );
// returns ~2.0

d = accumulator();
// returns ~2.0

@example
var accumulator = incrpcorrdist( 2.0, -3.0 );