
{{alias}}( x, y[, options] )
    Locally-weighted polynomial regression via the LOWESS algorithm.

    Parameters
    ----------
    x: Array<number>
        x-axis values (abscissa values).

    y: Array<number>
        Corresponding y-axis values (ordinate values).

    options: Object (optional)
        Function options.

    options.f: number (optional)
        Positive number specifying the smoothing span, i.e., the proportion of
        points which influence smoothing at each value. Larger values
        correspond to more smoothing. Default: `2/3`.

    options.nsteps: number (optional)
        Number of iterations in the robust fit (fewer iterations translates to
        faster function execution). If set to zero, the nonrobust fit is
        returned. Default: `3`.

    options.delta: number (optional)
        Nonnegative number which may be used to reduce the number of
        computations. Default: 1/100th of the range of `x`.

    options.sorted: boolean (optional)
        Boolean indicating if the input array `x` is sorted. Default: `false`.

    Returns
    -------
    out: Object
        Object with ordered x-values and fitted values.

    Examples
    --------
    > var x = new {{alias:@stdlib/array/float64}}( 100 );
    > var y = new {{alias:@stdlib/array/float64}}( x.length );
    > for ( var i = 0; i < x.length; i++ ) {
    ...     x[ i ] = i;
    ...     y[ i ] = ( 0.5*i ) + ( 10.0*{{alias:@stdlib/random/base/randn}}() );
    ... }
    > var out = {{alias}}( x, y );
    > var yhat = out.y;

    > var h = {{alias:@stdlib/plot/ctor}}( [ x, x ], [ y, yhat ] );
    > h.lineStyle = [ 'none', '-' ];
    > h.symbols = [ 'closed-circle', 'none' ];

    > h.view( 'window' );

    See Also
    --------

