docs: def kendalltau_dist(params1, params2=None):
      r"""Compute the Kendall tau distance between two models.
  
      This function computes the Kendall tau distance between the rankings
      induced by two parameter vectors. Let :math:`\sigma_i` be the rank of item
      ``i`` in the model described by ``params1``, and :math:`\tau_i` be its rank
      in the model described by ``params2``. The Kendall tau distance is defined
      as the number of pairwise disagreements between the two rankings, i.e.,
  
      .. math::
  
        \sum_{i=1}^N \sum_{j=1}^N
          \mathbf{1} \{ \sigma_i > \sigma_j \wedge \tau_i < \tau_j \}
  
      By convention, items with the lowest parameters are ranked first (i.e.,
      sorted using the natural order).
  
      If the argument ``params2`` is ``None``, the second model is assumed to
      rank the items by their index: item ``0`` has rank 1, item ``1`` has rank
      2, etc.
  
      If some values are equal within a parameter vector, all items are given a
      distinct rank, corresponding to the order in which the values occur.
  
      Parameters
      ----------
      params1 : array_like
          Parameters of the first model.
      params2 : array_like, optional
          Parameters of the second model.
  
      Returns
      -------
      dist : float
          Kendall tau distance.
      """
      assert params2 is None or len(params1) == len(params2)
      ranks1 = rankdata(params1, method="ordinal")
      if params2 is None:
          ranks2 = np.arange(1, len(params1) + 1, dtype=float)
      else:
          ranks2 = rankdata(params2, method="ordinal")
      tau, _ = kendalltau(ranks1, ranks2)
      n_items = len(params1)
      n_pairs = n_items * (n_items - 1) / 2
      return round((n_pairs - n_pairs * tau) / 2)