Repository: scipy/scipy
File: scipy/stats/_correlation.py (Python)

Code before:

    y_continuous : bool, default: False
        Whether `y` is assumed to be drawn from a continuous distribution.
        If `y` is drawn from a continuous distribution, results are valid
        whether this is assumed or not, but enabling this assumption will
        result in faster computation and typically produce similar results.

    Returns
    -------
    res : SignificanceResult
        An object containing attributes:

        statistic : float
            The xi correlation statistic.
        pvalue : float
            The associated *p*-value: the probability of a statistic at least as
            high as the observed value under the null hypothesis of independence.

    See Also
    --------
    scipy.stats.pearsonr, scipy.stats.spearmanr, scipy.stats.kendalltau

    Notes
    -----
    There is currently no special handling of ties in `x`; they are broken arbitrarily
    by the implementation. [1]_ recommends: "if there are ties among the Xi’s, then
    choose an increasing rearrangement as above by breaking ties uniformly at random."
    This is easily accomplished by adding a small amount of random noise to `x`; see
    examples.

    [1]_ notes that the statistic is not symmetric in `x` and `y` *by design*:
    "...we may want to understand if :math:`Y` is a function :math:`X`, and not just
    if one of the variables is a function of the other." See [1]_ Remark 1.

    References
    ----------
    .. [1] Chatterjee, Sourav. "A new coefficient of correlation." Journal of
           the American Statistical Association 116.536 (2021): 2009-2022.
           :doi:`10.1080/01621459.2020.1758115`.

    Examples
    --------
    Generate perfectly correlated data, and observe that the xi correlation is
    nearly 1.0.

    >>> import numpy as np
    >>> from scipy import stats
    >>> rng = np.random.default_rng(348932549825235)
    >>> x = rng.uniform(0, 10, size=100)
    >>> y = np.sin(x)
    >>> res = stats.chatterjeexi(x, y)

Review comment (suggestion):
```suggestion
    by the implementation. [1]_ recommends: "if there are ties among the Xi's, then
```

Code after:

    y_continuous : bool, default: False
        Whether `y` is assumed to be drawn from a continuous distribution.
        If `y` is drawn from a continuous distribution, results are valid
        whether this is assumed or not, but enabling this assumption will
        result in faster computation and typically produce similar results.

    Returns
    -------
    res : SignificanceResult
        An object containing attributes:

        statistic : float
            The xi correlation statistic.
        pvalue : float
            The associated *p*-value: the probability of a statistic at least as
            high as the observed value under the null hypothesis of independence.

    See Also
    --------
    scipy.stats.pearsonr, scipy.stats.spearmanr, scipy.stats.kendalltau

    Notes
    -----
    There is currently no special handling of ties in `x`; they are broken arbitrarily
    by the implementation. [1]_ recommends: "if there are ties among the Xi's, then
    choose an increasing rearrangement as above by breaking ties uniformly at random."
    This is easily accomplished by adding a small amount of random noise to `x`; see
    examples.

    [1]_ notes that the statistic is not symmetric in `x` and `y` *by design*:
    "...we may want to understand if :math:`Y` is a function :math:`X`, and not just
    if one of the variables is a function of the other." See [1]_ Remark 1.

    References
    ----------
    .. [1] Chatterjee, Sourav. "A new coefficient of correlation." Journal of
           the American Statistical Association 116.536 (2021): 2009-2022.
           :doi:`10.1080/01621459.2020.1758115`.

    Examples
    --------
    Generate perfectly correlated data, and observe that the xi correlation is
    nearly 1.0.

    >>> import numpy as np
    >>> from scipy import stats
    >>> rng = np.random.default_rng(348932549825235)
    >>> x = rng.uniform(0, 10, size=100)
    >>> y = np.sin(x)
    >>> res = stats.chatterjeexi(x, y)