code: def fit_mle(self, data, k_array=np.arange(0.1, 100, 0.1)):
          
          # todo: check and mention in docstring biases of mle for k_agg
          data = np.array(data)
          mu = np.mean(data)
          return mu, _solve_k_from_mu(data, k_array, nbinom_nll, mu)
docs: %(super)s
  
          In addition to data, gives an optional keyword argument k_array
          containing the values to search for k_agg. A brute force search is then
          used to find the parameter k_agg.