
{{alias}}( arrays )
    Computes the variance of a one-dimensional ndarray using a two-pass
    algorithm.

    If provided an empty one-dimensional ndarray, the function returns `NaN`.

    If `N - c` is less than or equal to `0` (where `N` corresponds to the number
    of elements in the input ndarray and `c` corresponds to the provided degrees
    of freedom adjustment), the function returns `NaN`.

    Parameters
    ----------
    arrays: ArrayLikeObject<ndarray>
        Array-like object containing the following ndarrays:

        - a one-dimensional input ndarray.
        - a zero-dimensional ndarray specifying the degrees of freedom
        adjustment. Providing a non-zero degrees of freedom adjustment has the
        effect of adjusting the divisor during the calculation of the variance
        according to `N-c` where `N` is the number of elements in the input
        ndarray and `c` corresponds to the provided degrees of freedom
        adjustment. When computing the variance of a population, setting this
        parameter to `0` is the standard choice (i.e., the provided array
        contains data constituting an entire population). When computing the
        corrected sample variance, setting this parameter to `1` is the standard
        choice (i.e., the provided array contains data sampled from a larger
        population; this is commonly referred to as Bessel's correction).

    Returns
    -------
    out: number
        The variance.

    Examples
    --------
    // Create the input ndarray:
    > var x = {{alias:@stdlib/ndarray/vector/ctor}}( [ 1.0, -2.0, 2.0 ], 'generic' );

    // Create the correction ndarray:
    > var opts = { 'dtype': 'generic' };
    > var correction = {{alias:@stdlib/ndarray/from-scalar}}( 1.0, opts );

    // Compute the variance:
    > {{alias}}( [ x, correction ] )
    ~4.333

    See Also
    --------

