ing arrays for `df`
    and `t` with shapes compatible for broadcasting. Compute `stdtr` at
    4 points for 3 degrees of freedom resulting in an array of shape 3x4.

    >>> dfs = np.array([[1], [2], [3]])
    >>> t = np.array([2, 4, 6, 8])
    >>> dfs.shape, t.shape
    ((3, 1), (4,))

    >>> stdtr(dfs, t)
    array([[0.85241638, 0.92202087, 0.94743154, 0.96041658],
           [0.90824829, 0.97140452, 0.98666426, 0.99236596],
           [0.93033702, 0.98599577, 0.99536364, 0.99796171]])

    The t distribution is also available as `scipy.stats.t`. Calling `stdtr`
    directly can be much faster than calling the ``cdf`` method of
    `scipy.stats.t`. To get the same results, one must use the following
    parametrization: ``scipy.stats.t(df).cdf(x) = stdtr(df, x)``.

    >>> from scipy.stats import t
    >>> df, x = 3, 1
    >>> stdtr_result = stdtr(df, x)  # this can be faster than below
    >>> stats_result = t(df).cdf(x)
    >>> stats_result == stdtr_result  # test that results are equal
    True
    Ústdtridfaß