time-to-botec/js/node_modules/@stdlib/stats/base/nanvariancetk/docs/repl.txt

{{alias}}( N, correction, x, stride )
    Computes the variance of a strided array ignoring `NaN` values and using a
    one-pass textbook 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`.

    If every indexed element is `NaN`, 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 variance according to `n - c` where `c` corresponds to the
        provided degrees of freedom adjustment and `n` corresponds to the number
        of non-`NaN` indexed elements. 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 unbiased 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).

    x: Array<number>|TypedArray
        Input array.

    stride: integer
        Index increment.

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

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

    // Using `N` and `stride` parameters:
    > x = [ -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 )
    ~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 );
    > N = {{alias:@stdlib/math/base/special/floor}}( x0.length / 2 );
    > stride = 2;
    > {{alias}}( N, 1, x1, stride )
    ~4.3333

{{alias}}.ndarray( N, correction, x, stride, offset )
    Computes the variance of a strided array ignoring `NaN` values and using a
    one-pass textbook 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 variance according to `n - c` where `c` corresponds to the
        provided degrees of freedom adjustment and `n` corresponds to the number
        of non-`NaN` indexed elements. 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 unbiased 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).

    x: Array<number>|TypedArray
        Input array.

    stride: integer
        Index increment.

    offset: integer
        Starting index.

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

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

    // Using offset parameter:
    > var x = [ 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 )
    ~4.3333

    See Also
    --------