class_docstr: Inherits time series methods from TSM class.
  
  **** NON-GAUSSIAN LOCAL LEVEL MODEL ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the time series data that will be used.
  
  integ : int (default : 0)
      Specifies how many times 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.
  
  family : 
      e.g. pf.Normal(0,1)
class_docstr_tok: ["Inherits","time","series","methods","from","TSM","class",".","*","*","*","*","NON-GAUSSIAN","LOCAL","LEVEL","MODEL","*","*","*","*","Parameters","--","--","--","--","--","data",":","pd.DataFrame","or","np.array","Field","to","specify","the","time","series","data","that","will","be","used",".","integ",":","int","(","default",":","0",")","Specifies","how","many","times","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",".","family",":","e.g",".","pf.Normal","(","0,1",")"]
docstr: Creates negative loglikelihood of the model
  
  Parameters
  ----------
  beta : np.array
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  - Negative loglikelihood
docstr_tok: ["Creates","negative","loglikelihood","of","the","model","Parameters","--","--","--","--","--","beta",":","np.array","Contains","untransformed","starting","values","for","latent","variables","Returns","--","--","--","--","--","-","Negative","loglikelihood"]
code: def neg_loglik(self, beta):
      """Creates negative loglikelihood of the model
  
      Parameters
      ----------
      beta : np.array
          Contains untransformed starting values for latent variables
  
      Returns
      ----------
      - Negative loglikelihood
      """
      states = np.zeros([self.state_no, self.data_length])
      states[0, :] = beta[self.z_no : self.z_no + self.data_length]
      parm = np.array(
          [
              self.latent_variables.z_list[k].prior.transform(beta[k])
              for k in range(self.z_no)
          ]
      )  # transformed distribution parameters
      scale, shape, skewness = self._get_scale_and_shape(parm)
      return self.state_likelihood(
          beta, states
      ) + self.family.neg_loglikelihood(
          self.data, self.link(states[0, :]), scale, shape, skewness
      )
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  from scipy import optimize
  from ..None import inference
  from ..None import families
  from ..None import output
  from ..None import tsm
  from ..None import data_check
  from ..None import covariances
  from ..None import results
  from ..None import gas
  from .kalman import *
  from .llm import *
label_desc: Use image node_id 3 for calling the NLLEV obj's underlying member method code with example usage: obj.neg_loglik(beta) and returns: unknown