docs: def rank_centrality(n_items, data, alpha=0.0):
      """Compute the Rank Centrality estimate of model parameters.
  
      This function implements Negahban et al.'s Rank Centrality algorithm
      [NOS12]_. The algorithm is similar to :func:`~choix.ilsr_pairwise`, but
      considers the *ratio* of wins for each pair (instead of the total count).
  
      The transition rates of the Rank Centrality Markov chain are initialized
      with ``alpha``. When ``alpha > 0``, this corresponds to a form of
      regularization (see :ref:`regularization` for details).
  
      Parameters
      ----------
      n_items : int
          Number of distinct items.
      data : list of lists
          Pairwise-comparison data.
      alpha : float, optional
          Regularization parameter.
  
      Returns
      -------
      params : numpy.ndarray
          An estimate of model parameters.
      """
      _, chain = _init_lsr(n_items, alpha, None)
      for winner, loser in data:
          chain[loser, winner] += 1.0
      # Transform the counts into ratios.
      idx = chain > 0  # Indices (i,j) of non-zero entries.
      chain[idx] = chain[idx] / (chain + chain.T)[idx]
      # Finalize the Markov chain by adding the self-transition rate.
      chain -= np.diag(chain.sum(axis=1))
      return log_transform(statdist(chain))