, 1000)
    >>> fig, ax = plt.subplots(figsize=(8, 8))
    >>> for parameter_set in parameters_list:
    ...     n, k, style = parameter_set
    ...     nbdtri_vals = nbdtri(k, n, cdf_vals)
    ...     ax.plot(cdf_vals, nbdtri_vals, label=rf"$k={k},\ n={n}$",
    ...             ls=style)
    >>> ax.legend()
    >>> ax.set_ylabel("$p$")
    >>> ax.set_xlabel("$CDF$")
    >>> title = "nbdtri: inverse of negative binomial CDF with respect to $p$"
    >>> ax.set_title(title)
    >>> plt.show()

    `nbdtri` can evaluate different parameter sets by providing arrays with
    shapes compatible for broadcasting for `k`, `n` and `p`. Here we compute
    the function for three different `k` at four locations `p`, resulting in
    a 3x4 array.

    >>> k = np.array([[5], [10], [15]])
    >>> y = np.array([0.3, 0.5, 0.7, 0.9])
    >>> k.shape, y.shape
    ((3, 1), (4,))

    >>> nbdtri(k, 5, y)
    array([[0.37258157, 0.45169416, 0.53249956, 0.64578407],
           [0.24588501, 0.30451981, 0.36778453, 0.46397088],
           [0.18362101, 0.22966758, 0.28054743, 0.36066188]])
    