class_docstr: Inherits time series methods from TSM class.
  
  **** GENERALIZED AUTOREGRESSIVE SCORE LOCAL LEVEL MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the univariate time series data that will be used.
  
  integ : int (default : 0)
      Specifies how many time to difference the time series.
  
  target : str (pd.DataFrame) or int (np.array)
      Specifies which column name or array index to use. By default, first
      column/array will be selected as the dependent variable.
  
  dist : str
      Which distribution to use
docstr: Returns the negative loglikelihood of the model
  
  Parameters
  ----------
  beta : np.array
      Contains untransformed starting values for latent variables
docstr_tok: ["Returns","the","negative","loglikelihood","of","the","model","Parameters","--","--","--","--","--","beta",":","np.array","Contains","untransformed","starting","values","for","latent","variables"]
code: def neg_loglik(self, beta):
      """Returns the negative loglikelihood of the model
  
      Parameters
      ----------
      beta : np.array
          Contains untransformed starting values for latent variables
      """
      theta, Y, _ = self._model(beta)
      parm = np.array(
          [
              self.latent_variables.z_list[k].prior.transform(beta[k])
              for k in range(beta.shape[0])
          ]
      )
      (
          model_scale,
          model_shape,
          model_skewness,
      ) = self._get_scale_and_shape(parm)
      return self.family.neg_loglikelihood(
          Y, self.link(theta), model_scale, model_shape, model_skewness
      )
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import scipy.special
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .gas_core_recursions import gas_llev_recursion
label_desc: Use image node_id 16 for calling the GASLLEV obj's underlying member method code with example usage: obj.neg_loglik(beta) and returns: self