docs: def psisloo(log_lik, **kwargs):
      r"""PSIS leave-one-out log predictive densities.
  
      Computes the log predictive densities given posterior samples of the log
      likelihood terms :math:`p(y_i|\theta^s)` in input parameter `log_lik`.
      Returns a sum of the leave-one-out log predictive densities `loo`,
      individual leave-one-out log predictive density terms `loos` and an estimate
      of Pareto tail indeces `ks`. The estimates are unreliable if tail index
      ``k > 0.7`` (see more in the references listed in the module docstring).
  
      Additional keyword arguments are passed to the :meth:`psislw()` function
      (see the corresponding documentation).
  
      Parameters
      ----------
      log_lik : ndarray
          Array of size n x m containing n posterior samples of the log likelihood
          terms :math:`p(y_i|\theta^s)`.
  
      Returns
      -------
      loo : scalar
          sum of the leave-one-out log predictive densities
  
      loos : ndarray
          individual leave-one-out log predictive density terms
  
      ks : ndarray
          estimated Pareto tail indeces
  
      """
      # ensure overwrite flag in passed arguments
      kwargs['overwrite_lw'] = True
      # log raw weights from log_lik
      lw = -log_lik
      # compute Pareto smoothed log weights given raw log weights
      lw, ks = psislw(lw, **kwargs)
      # compute
      lw += log_lik
      loos = sumlogs(lw, axis=0)
      loo = loos.sum()
      return loo, loos, ks