{{alias}}( x[, options] )
    Performs a chi-square independence test.

    For a two-way contingency table `x` (represented as a two-dimensional
    `ndarray` or `array` of `arrays`), the null hypothesis that the joint
    distribution of the cell counts is the product of the row and column
    marginals is tested, i.e. whether the row and column variables are
    independent.

    The function returns an object containing the test statistic, p-value, and
    decision.

    Parameters
    ----------
    x: (MatrixLike|Array<Array>)
        Two-way table of cell counts.

    options: Object (optional)
        Options.

    options.alpha: number (optional)
        Significance level of the hypothesis test. Must be on the interval
        [0,1]. Default: 0.05.

    options.correct: boolean (optional)
        Boolean indicating whether to use Yates' continuity correction when
        provided a 2x2 contingency table. Default: true.

    Returns
    -------
    out: Object
        Test result object.

    out.alpha: number
        Significance level.

    out.rejected: boolean
        Test decision.

    out.pValue: number
        Test p-value.

    out.statistic: number
        Test statistic.

    out.df: number
        Degrees of freedom.

    out.expected: ndarray
        Expected cell counts.

    out.method: string
        Test name.

    out.print: Function
        Function to print formatted output.

    Examples
    --------
    // Provide expected probabilities...
    > var x = [ [ 20, 30 ], [ 30, 20 ] ];
    > var out = {{alias}}( x )
    { 'rejected': false, 'pValue': ~0.072, 'statistic': 3.24, ... }
    > out.print()

    // Set significance level...
    > var opts = { 'alpha': 0.1 };
    > out = {{alias}}( x, opts )
    { 'rejected': true, 'pValue': ~0.072, 'statistic': 3.24, ... }
    > out.print()

    // Disable Yates' continuity correction (primarily used with small counts):
    > opts = { 'correct': false };
    > out = {{alias}}( x, opts )
    {...}

    See Also
    --------