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 smoothed state estimate given state matrices and 
  latent variables.
  
  Parameters
  ----------
  
  data : np.array
      Data to be smoothed
  
  beta : np.array
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  - Smoothed states
docstr_tok: ["Creates","smoothed","state","estimate","given","state","matrices","and","latent","variables",".","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, H, mu):
      """Creates smoothed state estimate given state matrices and
      latent variables.
  
      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 = self._ss_matrices(beta)
      alpha, V = nl_univariate_KFS(data, Z, H, T, Q, R, mu)
      return alpha, V
  
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 25 for calling the NLLEV obj's underlying member method code with example usage: obj.smoothed_state(data, beta, H, mu) and returns: alpha,  V