
{{alias}}( N, correction, meanx, x, sx, meany, y, sy )
    Computes the covariance of two  strided arrays provided known means and
    using a one-pass textbook algorithm.

    The `N` and stride parameters determine which elements in the strided arrays
    are accessed at runtime.

    Indexing is relative to the first index. To introduce an offset, use a typed
    array view.

    If `N <= 0`, the function returns `NaN`.

    Parameters
    ----------
    N: integer
        Number of indexed elements.

    correction: number
        Degrees of freedom adjustment. Setting this parameter to a value other
        than `0` has the effect of adjusting the divisor during the calculation
        of the covariance according to `N - c` where `c` corresponds to the
        provided degrees of freedom adjustment. When computing the population
        covariance, setting this parameter to `0` is the standard choice (i.e.,
        the provided arrays contain data constituting entire populations). When
        computing the unbiased sample covariance, setting this parameter to `1`
        is the standard choice (i.e., the provided array contains data sampled
        from larger populations; this is commonly referred to as Bessel's
        correction).

    meanx: number
        Mean of `x`.

    x: Array<number>|TypedArray
        First input array.

    sx: integer
        Stride length of `x`.

    meany: number
        Mean of `y`.

    y: Array<number>|TypedArray
        Second input array.

    sy: integer
        Stride length of `y`.

    Returns
    -------
    out: number
        The covariance.

    Examples
    --------
    // Standard Usage:
    > var x = [ 1.0, -2.0, 2.0 ];
    > {{alias}}( x.length, 1, 1.0/3.0, x, 1, 1.0/3.0, x, 1 )
    ~4.3333

    // Using `N` and stride parameters:
    > x = [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ];
    > {{alias}}( 3, 1, 1.0/3.0, x, 2, 1.0/3.0, x, 2 )
    ~4.3333

    // Using view offsets:
    > var x0 = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, 1.0 ] );
    > var x1 = new {{alias:@stdlib/array/float64}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 );
    > {{alias}}( 3, 1, 1.0/3.0, x1, 2, 1.0/3.0, x1, 2 )
    ~4.3333


{{alias}}.ndarray( N, correction, meanx, x, sx, ox, meany, y, sy, oy )
    Computes the covariance of two strided arrays provided known means and
    using a one-pass textbook algorithm and alternative indexing semantics.

    While typed array views mandate a view offset based on the underlying
    buffer, offset parameters support indexing semantics based on starting
    indices.

    Parameters
    ----------
    N: integer
        Number of indexed elements.

    correction: number
        Degrees of freedom adjustment. Setting this parameter to a value other
        than `0` has the effect of adjusting the divisor during the calculation
        of the covariance according to `N - c` where `c` corresponds to the
        provided degrees of freedom adjustment. When computing the population
        covariance, setting this parameter to `0` is the standard choice (i.e.,
        the provided arrays contain data constituting entire populations). When
        computing the unbiased sample covariance, setting this parameter to `1`
        is the standard choice (i.e., the provided array contains data sampled
        from larger populations; this is commonly referred to as Bessel's
        correction).

    meanx: number
        Mean of `x`.

    x: Array<number>|TypedArray
        First input array.

    sx: integer
        Stride length of `x`.

    ox: integer
        Starting index of `x`.

    meany: number
        Mean of `y`.

    y: Array<number>|TypedArray
        Second input array.

    sy: integer
        Stride length of `y`.

    oy: integer
        Starting index of `y`.

    Returns
    -------
    out: number
        The covariance.

    Examples
    --------
    // Standard Usage:
    > var x = [ 1.0, -2.0, 2.0 ];
    > {{alias}}.ndarray( x.length, 1, 1.0/3.0, x, 1, 0, 1.0/3.0, x, 1, 0 )
    ~4.3333

    // Using offset parameters:
    > var x = [ 1.0, -2.0, 3.0, 2.0, 5.0, 1.0 ];
    > {{alias}}.ndarray( 3, 1, 1.0/3.0, x, 2, 1, 1.0/3.0, x, 2, 1 )
    ~4.3333

    See Also
    --------

