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

Code before:
        If `lmbda` is not None, do the transformation for that value.

    alpha : float, optional
        If `lmbda` is None and `alpha` is not None (default), return the
        ``100 * (1-alpha)%`` confidence  interval for `lmbda` as the third
        output argument. Must be between 0.0 and 1.0.

        If `lmbda` is not None, `alpha` is ignored.
    optimizer : callable, optional
        If `lmbda` is None, `optimizer` is the scalar optimizer used to find
        the value of `lmbda` that minimizes the negative log-likelihood
        function. `optimizer` is a callable that accepts one argument:

        fun : callable
            The objective function, which evaluates the negative
            log-likelihood function at a provided value of `lmbda`

        and returns an object, such as an instance of
        `scipy.optimize.OptimizeResult`, which holds the optimal value of
        `lmbda` in an attribute `x`.

        See the example in `boxcox_normmax` or the documentation of
        `scipy.optimize.minimize_scalar` for more information.

        If `lmbda` is not None, `optimizer` is ignored.
    nan_policy : {‘propagate’, ‘omit’, ‘raise’}
        Defines how to handle NaNs in `x`.

        - ``propagate``: if a NaN is present, all outputs will contain NaNs.
        - ``omit``: NaNs will be omitted when calculating the optimal `maxlog` and
          confidence interval; NaNs in `x` will remain NaNs in the transformed data.
        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.

    Returns
    -------
    boxcox : ndarray
        Box-Cox power transformed array.
    maxlog : float, optional
        If the `lmbda` parameter is None, the second returned argument is
        the `lmbda` that maximizes the log-likelihood function.
    (min_ci, max_ci) : tuple of float, optional
        If `lmbda` parameter is None and `alpha` is not None, this returned
        tuple of floats represents the minimum and maximum confidence limits
        given `alpha`.

    See Also
    --------
    probplot, boxcox_normplot, boxcox_normmax, boxcox_llf

    Notes
    -----

Review comment (suggestion):
```suggestion
    nan_policy : {'propagate', 'omit', 'raise'}
```

Code after:
        If `lmbda` is not None, do the transformation for that value.

    alpha : float, optional
        If `lmbda` is None and `alpha` is not None (default), return the
        ``100 * (1-alpha)%`` confidence  interval for `lmbda` as the third
        output argument. Must be between 0.0 and 1.0.

        If `lmbda` is not None, `alpha` is ignored.
    optimizer : callable, optional
        If `lmbda` is None, `optimizer` is the scalar optimizer used to find
        the value of `lmbda` that minimizes the negative log-likelihood
        function. `optimizer` is a callable that accepts one argument:

        fun : callable
            The objective function, which evaluates the negative
            log-likelihood function at a provided value of `lmbda`

        and returns an object, such as an instance of
        `scipy.optimize.OptimizeResult`, which holds the optimal value of
        `lmbda` in an attribute `x`.

        See the example in `boxcox_normmax` or the documentation of
        `scipy.optimize.minimize_scalar` for more information.

        If `lmbda` is not None, `optimizer` is ignored.
    nan_policy : {'propagate', 'omit', 'raise'}
        Defines how to handle NaNs in `x`.

        - ``propagate``: if a NaN is present, all outputs will contain NaNs.
        - ``omit``: NaNs will be omitted when calculating the optimal `maxlog` and
          confidence interval; NaNs in `x` will remain NaNs in the transformed data.
        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.

    Returns
    -------
    boxcox : ndarray
        Box-Cox power transformed array.
    maxlog : float, optional
        If the `lmbda` parameter is None, the second returned argument is
        the `lmbda` that maximizes the log-likelihood function.
    (min_ci, max_ci) : tuple of float, optional
        If `lmbda` parameter is None and `alpha` is not None, this returned
        tuple of floats represents the minimum and maximum confidence limits
        given `alpha`.

    See Also
    --------
    probplot, boxcox_normplot, boxcox_normmax, boxcox_llf

    Notes
    -----