docstr: Returns the score vector of the log-likelihood.
  
  http://www.tandfonline.com/doi/pdf/10.1080/00949650903389993
  
  Parameters
  ----------
  params : ndarray
      Parameter at which score is evaluated.
  
  Returns
  -------
  score : ndarray
      First derivative of loglikelihood function.
docstr_tok: ["Returns","the","score","vector","of","the","log-likelihood",".","http",":","\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/00949650903389993","Parameters","--","--","--","--","--","params",":","ndarray","Parameter","at","which","score","is","evaluated",".","Returns","--","--","--","-","score",":","ndarray","First","derivative","of","loglikelihood","function","."]
code: def score(self, params):
      """
      Returns the score vector of the log-likelihood.
  
      http://www.tandfonline.com/doi/pdf/10.1080/00949650903389993
  
      Parameters
      ----------
      params : ndarray
          Parameter at which score is evaluated.
  
      Returns
      -------
      score : ndarray
          First derivative of loglikelihood function.
      """
      sf1, sf2 = self.score_factor(params)
  
      d1 = np.dot(sf1, self.exog)
      d2 = np.dot(sf2, self.exog_precision)
      return np.concatenate((d1, d2))
  
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 9 for calling the BetaModel obj's underlying member method code with example usage: obj.score(params) and returns: np