class_docstr: Inherits time series methods from TSM class.
  
  **** NON-GAUSSIAN DYNAMIC REGRESSION MODELS ****
  
  Parameters
  ----------
  
  formula : string
      patsy string describing the regression
  
  data : pd.DataFrame
      Field to specify the data that will be used
  
  family : 
      e.g. pf.Normal(0,1)
docstr: Creates total Markov blanket for states
  
  Parameters
  ----------
  beta : np.array
      Contains untransformed starting values for latent variables
  
  alpha : np.array
      A vector of states
  
  Returns
  ----------
  Markov blanket for states
docstr_tok: ["Creates","total","Markov","blanket","for","states","Parameters","--","--","--","--","--","beta",":","np.array","Contains","untransformed","starting","values","for","latent","variables","alpha",":","np.array","A","vector","of","states","Returns","--","--","--","--","--","Markov","blanket","for","states"]
code: def markov_blanket(self, beta, alpha):
      """Creates total Markov blanket for states
  
      Parameters
      ----------
      beta : np.array
          Contains untransformed starting values for latent variables
  
      alpha : np.array
          A vector of states
  
      Returns
      ----------
      Markov blanket for states
      """
      likelihood_blanket = self.likelihood_markov_blanket(beta)
      state_blanket = self.state_likelihood_markov_blanket(
          beta, alpha, 0
      )
      for i in range(self.state_no - 1):
          likelihood_blanket = np.append(
              likelihood_blanket, self.likelihood_markov_blanket(beta)
          )
          state_blanket = np.append(
              state_blanket,
              self.state_likelihood_markov_blanket(beta, alpha, i + 1),
          )
      return likelihood_blanket + state_blanket
  
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 .dynlin import *
label_desc: Use image node_id 8 for calling the NDynReg obj's underlying member method code with example usage: obj.markov_blanket(beta, alpha) and returns: unknown