class_docstr: Inherits time series methods from TSM class.
  
  **** DYNAMIC AUTOREGRESSIVE MODEL ****
  
  Parameters
  ----------
  
  ar : int
      Number of autoregressive lags
  
  data : pd.DataFrame
      Field to specify the data that will be used
class_docstr_tok: ["Inherits","time","series","methods","from","TSM","class",".","*","*","*","*","DYNAMIC","AUTOREGRESSIVE","MODEL","*","*","*","*","Parameters","--","--","--","--","--","ar",":","int","Number","of","autoregressive","lags","data",":","pd.DataFrame","Field","to","specify","the","data","that","will","be","used"]
docstr: Creates the negative log marginal likelihood of the model
  
  Parameters
  ----------
  
  data : np.array
      Data to be smoothed
  
  beta : np.array
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  - Smoothed states
docstr_tok: ["Creates","the","negative","log","marginal","likelihood","of","the","model","Parameters","--","--","--","--","--","data",":","np.array","Data","to","be","smoothed","beta",":","np.array","Contains","untransformed","starting","values","for","latent","variables","Returns","--","--","--","--","--","-","Smoothed","states"]
code: def smoothed_state(self, data, beta):
      """Creates the negative log marginal likelihood of the model
  
      Parameters
      ----------
  
      data : np.array
          Data to be smoothed
  
      beta : np.array
          Contains untransformed starting values for latent variables
  
      Returns
      ----------
      - Smoothed states
      """
  
      T, Z, R, Q, H = self._ss_matrices(beta)
      alpha, V = dl_univariate_KFS(data, Z, H, T, Q, R, 0.0)
      return alpha, V
  
imports: import copy
  import sys
  import numpy
  import pandas
  import scipy.stats
  from patsy import dmatrices, dmatrix, demo_data
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .kalman import *
label_desc: Use image node_id 14 for calling the DAR obj's underlying member method code with example usage: obj.smoothed_state(data, beta) and returns: alpha,  V