docstr: Derivative of the expected endog with respect to the parameters.
  
  not verified yet
  
  Parameters
  ----------
  params : ndarray
      parameter at which score is evaluated
  
  Returns
  -------
  The value of the derivative of the expected endog with respect
  to the parameter vector.
docstr_tok: ["Derivative","of","the","expected","endog","with","respect","to","the","parameters",".","not","verified","yet","Parameters","--","--","--","--","--","params",":","ndarray","parameter","at","which","score","is","evaluated","Returns","--","--","--","-","The","value","of","the","derivative","of","the","expected","endog","with","respect","to","the","parameter","vector","."]
code: def _deriv_mean_dparams(self, params):
      """
      Derivative of the expected endog with respect to the parameters.
  
      not verified yet
  
      Parameters
      ----------
      params : ndarray
          parameter at which score is evaluated
  
      Returns
      -------
      The value of the derivative of the expected endog with respect
      to the parameter vector.
      """
      link = self.link
      lin_pred = self.predict(params, which="linear")
      idl = link.inverse_deriv(lin_pred)
      dmat = self.exog * idl[:, None]
      return np.column_stack(
          (dmat, np.zeros(self.exog_precision.shape))
      )
  
imports: import numpy
  from scipy.special import gammaln
  import patsy
  import statsmodels.base.wrapper
  import statsmodels.regression.linear_model
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.base.model import GenericLikelihoodModel, GenericLikelihoodModelResults, _LLRMixin
  from statsmodels.genmod import families
label_desc: Use image node_id 18 for calling the BetaModel obj's underlying member method code with example usage: obj._deriv_mean_dparams(params) and returns: np