{{alias}}( N, correction, x, stride )
    Computes the standard error of the mean for a double-precision floating-
    point strided array using a one-pass trial mean algorithm.

    The `N` and `stride` parameters determine which elements in `x` 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 standard deviation according to `N - c` where `c` corresponds to
        the provided degrees of freedom adjustment. When computing the standard
        deviation 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 standard deviation,
        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).

    x: Float64Array
        Input array.

    stride: integer
        Index increment.

    Returns
    -------
    out: number
        Standard error of the mean.

    Examples
    --------
    // Standard Usage:
    > var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 2.0 ] );
    > {{alias}}( x.length, 1, x, 1 )
    ~1.20185

    // Using `N` and `stride` parameters:
    > x = new {{alias:@stdlib/array/float64}}( [ -2.0, 1.0, 1.0, -5.0, 2.0, -1.0 ] );
    > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
    > var stride = 2;
    > {{alias}}( N, 1, x, stride )
    ~1.20185

    // 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 );
    > N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 );
    > stride = 2;
    > {{alias}}( N, 1, x1, stride )
    ~1.20185

{{alias}}.ndarray( N, correction, x, stride, offset )
    Computes the standard error of the mean for a double-precision floating-
    point strided array using a one-pass trial mean algorithm and alternative
    indexing semantics.

    While typed array views mandate a view offset based on the underlying
    buffer, the `offset` parameter supports indexing semantics based on a
    starting index.

    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 standard deviation according to `N - c` where `c` corresponds to
        the provided degrees of freedom adjustment. When computing the standard
        deviation 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 standard deviation,
        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).

    x: Float64Array
        Input array.

    stride: integer
        Index increment.

    offset: integer
        Starting index.

    Returns
    -------
    out: number
        Standard error of the mean.

    Examples
    --------
    // Standard Usage:
    > var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 2.0 ] );
    > {{alias}}.ndarray( x.length, 1, x, 1, 0 )
    ~1.20185

    // Using offset parameter:
    > var x = new {{alias:@stdlib/array/float64}}( [ 1.0, -2.0, 3.0, 2.0, 5.0, -1.0 ] );
    > var N = {{alias:@stdlib/math/base/special/floor}}( x.length / 2 );
    > {{alias}}.ndarray( N, 1, x, 2, 1 )
    ~1.20185

    See Also
    --------