function incrmstdev( W, mean ) {
	var delta;
	var buf;
	var tmp;
	var M2;
	var mu;
	var d1;
	var d2;
	var N;
	var n;
	var i;
	if ( !isPositiveInteger( W ) ) {
		throw new TypeError( format( 'invalid argument. Must provide a positive integer. Value: `%s`.', W ) );
	}
	buf = new Float64Array( W );
	n = W - 1;
	M2 = 0.0;
	i = -1;
	N = 0;
	if ( arguments.length > 1 ) {
		if ( !isNumber( mean ) ) {
			throw new TypeError( format( 'invalid argument. Must provide a number. Value: `%s`.', mean ) );
		}
		mu = mean;
		return accumulator2;
	}
	mu = 0.0;
	return accumulator1;

	/**
	* If provided a value, the accumulator function returns an updated corrected sample standard deviation. If not provided a value, the accumulator function returns the current corrected sample standard deviation.
	*
	* @private
	* @param {number} [x] - input value
	* @returns {(number|null)} corrected sample standard deviation or null
	*/
	function accumulator1( x ) {
		var k;
		var v;
		if ( arguments.length === 0 ) {
			if ( N === 0 ) {
				return null;
			}
			if ( N === 1 ) {
				return ( isnan( M2 ) ) ? NaN : 0.0;
			}
			if ( N < W ) {
				return sqrt( M2 / (N-1) );
			}
			return sqrt( M2 / n );
		}
		// Update the index for managing the circular buffer:
		i = (i+1) % W;

		// Case: incoming value is NaN, the sliding second moment is automatically NaN...
		if ( isnan( x ) ) {
			N = W; // explicitly set to avoid `N < W` branch
			M2 = NaN;
		}
		// Case: initial window...
		else if ( N < W ) {
			buf[ i ] = x; // update buffer
			N += 1;
			delta = x - mu;
			mu += delta / N;
			M2 += delta * (x - mu);
			if ( N === 1 ) {
				return 0.0;
			}
			return sqrt( M2 / (N-1) );
		}
		// Case: N = W = 1
		else if ( N === 1 ) {
			M2 = 0.0;
			return M2;
		}
		// Case: outgoing value is NaN, and, thus, we need to compute the accumulated values...
		else if ( isnan( buf[ i ] ) ) {
			N = 1;
			mu = x;
			M2 = 0.0;
			for ( k = 0; k < W; k++ ) {
				if ( k !== i ) {
					v = buf[ k ];
					if ( isnan( v ) ) {
						N = W; // explicitly set to avoid `N < W` branch
						M2 = NaN;
						break; // second moment is automatically NaN, so no need to continue
					}
					N += 1;
					delta = v - mu;
					mu += delta / N;
					M2 += delta * (v - mu);
				}
			}
		}
		// Case: neither the current second moment nor the incoming value are NaN, so we need to update the accumulated values...
		else if ( isnan( M2 ) === false ) {
			tmp = buf[ i ];
			delta = x - tmp;
			d1 = tmp - mu;
			mu += delta / W;
			d2 = x - mu;
			M2 += delta * (d1 + d2);
		}
		// Case: the current second moment is NaN, so nothing to do until the buffer no longer contains NaN values...

		buf[ i ] = x;
		return sqrt( M2 / n );
	}

	/**
	* If provided a value, the accumulator function returns an updated corrected sample standard deviation. If not provided a value, the accumulator function returns the current corrected sample standard deviation.
	*
	* @private
	* @param {number} [x] - input value
	* @returns {(number|null)} corrected sample standard deviation or null
	*/
	function accumulator2( x ) {
		var k;
		if ( arguments.length === 0 ) {
			if ( N === 0 ) {
				return null;
			}
			if ( N < W ) {
				return sqrt( M2 / N );
			}
			return sqrt( M2 / W );
		}
		// Update the index for managing the circular buffer:
		i = (i+1) % W;

		// Case: incoming value is NaN, the sliding second moment is automatically NaN...
		if ( isnan( x ) ) {
			N = W; // explicitly set to avoid `N < W` branch
			M2 = NaN;
		}
		// Case: initial window...
		else if ( N < W ) {
			buf[ i ] = x; // update buffer
			N += 1;
			delta = x - mu;
			M2 += delta * delta;
			return sqrt( M2 / N );
		}
		// Case: outgoing value is NaN, and, thus, we need to compute the accumulated values...
		else if ( isnan( buf[ i ] ) ) {
			M2 = 0.0;
			for ( k = 0; k < W; k++ ) {
				if ( k !== i ) {
					if ( isnan( buf[ k ] ) ) {
						N = W; // explicitly set to avoid `N < W` branch
						M2 = NaN;
						break; // second moment is automatically NaN, so no need to continue
					}
					delta = buf[ k ] - mu;
					M2 += delta * delta;
				}
			}
		}
		// Case: neither the current second moment nor the incoming value are NaN, so we need to update the accumulated values...
		else if ( isnan( M2 ) === false ) {
			tmp = buf[ i ];
			M2 += ( x-tmp ) * ( x-mu + tmp-mu );
		}
		// Case: the current second moment is NaN, so nothing to do until the buffer no longer contains NaN values...

		buf[ i ] = x;
		return sqrt( M2 / W );
	}
}
Returns an accumulator function which incrementally computes a moving corrected sample standard deviation.

## Method

-   Let \\(W\\) be a window of \\(N\\) elements over which we want to compute an corrected sample standard deviation.

-   We first recognize that the corrected sample standard deviation is defined as the square root of the unbiased sample variance. Accordingly, in order to derive an update equation for the corrected sample standard deviation, deriving an update equation for the unbiased sample variance is sufficient.

-   The difference between the unbiased sample variance in a window \\(W_i\\) and the unbiased sample variance in a window \\(W_{i+1})\\) is given by

    ```tex
    \Delta s^2 = s_{i+1}^2 - s_{i}^2
    ```

-   If we multiply both sides by \\(N-1\\),

    ```tex
    (N-1)(\Delta s^2) = (N-1)s_{i+1}^2 - (N-1)s_{i}^2
    ```

-   If we substitute the definition of the unbiased sample variance having the form

    ```tex
    \begin{align*}
    s^2 &= \frac{1}{N-1} \biggl( \sum_{i=1}^{N} (x_i - \bar{x})^2 \biggr) \\
        &= \frac{1}{N-1} \biggl( \sum_{i=1}^{N} (x_i^2 - 2\bar{x}x_i + \bar{x}^2) \biggr) \\
        &= \frac{1}{N-1} \biggl( \sum_{i=1}^{N} x_i^2 - 2\bar{x} \sum_{i=1}^{N} x_i + \sum_{i=1}^{N} \bar{x}^2) \biggr) \\
        &= \frac{1}{N-1} \biggl( \sum_{i=1}^{N} x_i^2 - \frac{2N\bar{x}\sum_{i=1}^{N} x_i}{N} + N\bar{x}^2 \biggr) \\
        &= \frac{1}{N-1} \biggl( \sum_{i=1}^{N} x_i^2 - 2N\bar{x}^2 + N\bar{x}^2 \biggr) \\
        &= \frac{1}{N-1} \biggl( \sum_{i=1}^{N} x_i^2 - N\bar{x}^2 \biggr)
    \end{align*}
    ```

    we return

    ```tex
    (N-1)(\Delta s^2) = \biggl(\sum_{k=1}^N x_k^2 - N\bar{x}_{i+1}^2 \biggr) - \biggl(\sum_{k=0}^{N-1} x_k^2 - N\bar{x}_{i}^2 \biggr)
    ```

-   This can be further simplified by recognizing that subtracting the sums reduces to \\(x_N^2 - x_0^2\\); in which case,

    ```tex
    \begin{align*}
    (N-1)(\Delta s^2) &= x_N^2 - x_0^2 - N\bar{x}_{i+1}^2 + N\bar{x}_{i}^2 \\
    &= x_N^2 - x_0^2 - N(\bar{x}_{i+1}^2 - \bar{x}_{i}^2) \\
    &= x_N^2 - x_0^2 - N(\bar{x}_{i+1} - \bar{x}_{i})(\bar{x}_{i+1} + \bar{x}_{i})
    \end{align*}
    ```

-   Recognizing that the difference of means can be expressed

    ```tex
    \bar{x}_{i+1} - \bar{x}_i = \frac{1}{N} \biggl( \sum_{k=1}^N x_k - \sum_{k=0}^{N-1} x_k \biggr) = \frac{x_N - x_0}{N}
    ```

    and substituting into the equation above

    ```tex
    (N-1)(\Delta s^2) = x_N^2 - x_0^2 - (x_N - x_0)(\bar{x}_{i+1} + \bar{x}_{i})
    ```

-   Rearranging terms gives us the update equation

    ```tex
    \begin{align*}
    (N-1)(\Delta s^2) &= (x_N - x_0)(x_N + x_0) - (x_N - x_0)(\bar{x}_{i+1} + \bar{x}_{i})
    &= (x_N - x_0)(x_N + x_0 - \bar{x}_{i+1} - \bar{x}_{i}) \\
    &= (x_N - x_0)(x_N - \bar{x}_{i+1} + x_0 - \bar{x}_{i})
    \end{align*}
    ```

@param {PositiveInteger} W - window size
@param {number} [mean] - mean value
@throws {TypeError} first argument must be a positive integer
@throws {TypeError} second argument must be a number
@returns {Function} accumulator function

@example
var accumulator = incrmstdev( 3 );

var s = accumulator();
// returns null

s = accumulator( 2.0 );
// returns 0.0

s = accumulator( -5.0 );
// returns ~4.95

s = accumulator( 3.0 );
// returns ~4.36

s = accumulator( 5.0 );
// returns ~5.29

s = accumulator();
// returns ~5.29

@example
var accumulator = incrmstdev( 3, 5.0 );