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

Code before:
            Useful to compare different methods.
    optimizer : callable, optional
        `optimizer` is a callable that accepts one argument:

        fun : callable
            The objective function to be minimized. `fun` accepts one argument,
            the Box-Cox transform parameter `lmbda`, and returns the value of
            the function (e.g., the negative log-likelihood) at the provided
            argument. The job of `optimizer` is to find the value of `lmbda`
            that *minimizes* `fun`.

        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 below or the documentation of
        `scipy.optimize.minimize_scalar` for more information.
    ymax : float, optional
        The unconstrained optimal transform parameter may cause Box-Cox
        transformed data to have extreme magnitude or even overflow.
        This parameter constrains MLE optimization such that the magnitude
        of the transformed `x` does not exceed `ymax`. The default is
        the maximum value of the input dtype. If set to infinity,
        `boxcox_normmax` returns the unconstrained optimal lambda.
        Ignored when ``method='pearsonr'``.
    nan_policy:
        Defines how to handle input NaNs.

        - ``propagate``: if a NaN is present in the input, the output will be NaN.
        - ``omit``: NaNs will be omitted when performing the calculation.
        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.

    Returns
    -------
    maxlog : float or ndarray
        The optimal transform parameter found.  An array instead of a scalar
        for ``method='all'``.

    See Also
    --------
    boxcox, boxcox_llf, boxcox_normplot, scipy.optimize.minimize_scalar

    Examples
    --------
    >>> import numpy as np
    >>> from scipy import stats
    >>> import matplotlib.pyplot as plt

    We can generate some data and determine the optimal ``lmbda`` in various
    ways:


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

Code after:
            Useful to compare different methods.
    optimizer : callable, optional
        `optimizer` is a callable that accepts one argument:

        fun : callable
            The objective function to be minimized. `fun` accepts one argument,
            the Box-Cox transform parameter `lmbda`, and returns the value of
            the function (e.g., the negative log-likelihood) at the provided
            argument. The job of `optimizer` is to find the value of `lmbda`
            that *minimizes* `fun`.

        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 below or the documentation of
        `scipy.optimize.minimize_scalar` for more information.
    ymax : float, optional
        The unconstrained optimal transform parameter may cause Box-Cox
        transformed data to have extreme magnitude or even overflow.
        This parameter constrains MLE optimization such that the magnitude
        of the transformed `x` does not exceed `ymax`. The default is
        the maximum value of the input dtype. If set to infinity,
        `boxcox_normmax` returns the unconstrained optimal lambda.
        Ignored when ``method='pearsonr'``.
    nan_policy : {'propagate', 'omit', 'raise'}
        Defines how to handle input NaNs.

        - ``propagate``: if a NaN is present in the input, the output will be NaN.
        - ``omit``: NaNs will be omitted when performing the calculation.
        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.

    Returns
    -------
    maxlog : float or ndarray
        The optimal transform parameter found.  An array instead of a scalar
        for ``method='all'``.

    See Also
    --------
    boxcox, boxcox_llf, boxcox_normplot, scipy.optimize.minimize_scalar

    Examples
    --------
    >>> import numpy as np
    >>> from scipy import stats
    >>> import matplotlib.pyplot as plt

    We can generate some data and determine the optimal ``lmbda`` in various
    ways: