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

Code before:
        finite_variance = sigma >= xp.finfo(sigma.dtype).smallest_normal
        logvar = xpx.apply_where(finite_variance, (sigma,), xp.log, fill_value=-xp.inf)

    loglike = (-n / 2 * logvar
               + (lmb - 1) * xp.sum(xp.sign(data) * xp.log1p(xp.abs(data)), axis=axis))

    return loglike


@xp_capabilities(np_only=True)
def yeojohnson_normmax(x, brack=None, *, nan_policy='propagate'):
    """Compute optimal Yeo-Johnson transform parameter.

    Compute optimal Yeo-Johnson transform parameter for input data, using
    maximum likelihood estimation.

    Parameters
    ----------
    x : array_like
        Input array.
    brack : 2-tuple, optional
        The starting interval for a downhill bracket search with
        `optimize.brent`. Note that this is in most cases not critical; the
        final result is allowed to be outside this bracket. If None,
        `optimize.fminbound` is used with bounds that avoid overflow.
    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
        The optimal transform parameter found.

    See Also
    --------
    yeojohnson, yeojohnson_llf, yeojohnson_normplot

    Notes
    -----
    .. versionadded:: 1.2.0

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


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

Code after:
        finite_variance = sigma >= xp.finfo(sigma.dtype).smallest_normal
        logvar = xpx.apply_where(finite_variance, (sigma,), xp.log, fill_value=-xp.inf)

    loglike = (-n / 2 * logvar
               + (lmb - 1) * xp.sum(xp.sign(data) * xp.log1p(xp.abs(data)), axis=axis))

    return loglike


@xp_capabilities(np_only=True)
def yeojohnson_normmax(x, brack=None, *, nan_policy='propagate'):
    """Compute optimal Yeo-Johnson transform parameter.

    Compute optimal Yeo-Johnson transform parameter for input data, using
    maximum likelihood estimation.

    Parameters
    ----------
    x : array_like
        Input array.
    brack : 2-tuple, optional
        The starting interval for a downhill bracket search with
        `optimize.brent`. Note that this is in most cases not critical; the
        final result is allowed to be outside this bracket. If None,
        `optimize.fminbound` is used with bounds that avoid overflow.
    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
        The optimal transform parameter found.

    See Also
    --------
    yeojohnson, yeojohnson_llf, yeojohnson_normplot

    Notes
    -----
    .. versionadded:: 1.2.0

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