docs: def mm_rankings(n_items, data, initial_params=None, alpha=0.0,
          max_iter=10000, tol=1e-8):
      """Compute the ML estimate of model parameters using the MM algorithm.
  
      This function computes the maximum-likelihood (ML) estimate of model
      parameters given ranking data (see :ref:`data-rankings`), using the
      minorization-maximization (MM) algorithm [Hun04]_, [CD12]_.
  
      If ``alpha > 0``, the function returns the maximum a-posteriori (MAP)
      estimate under a (peaked) Dirichlet prior. See :ref:`regularization` for
      details.
  
      Parameters
      ----------
      n_items : int
          Number of distinct items.
      data : list of lists
          Ranking data.
      initial_params : array_like, optional
          Parameters used to initialize the iterative procedure.
      alpha : float, optional
          Regularization parameter.
      max_iter : int, optional
          Maximum number of iterations allowed.
      tol : float, optional
          Maximum L1-norm of the difference between successive iterates to
          declare convergence.
  
      Returns
      -------
      params : numpy.ndarray
          The ML estimate of model parameters.
      """
      return _mm(n_items, data, initial_params, alpha, max_iter, tol,
              _mm_rankings)