class_docstr: A class that holds functions that are optimized/solved.
  
  The general setup of the class is simple.  Any method that starts with
  _opt_ creates a vector of estimating equations named est_vect such that
  np.dot(p, (est_vect))=0 where p is the weight on each
  observation as a 1 x n array and est_vect is n x k.  Then _modif_Newton is
  called to determine the optimal p by solving for the Lagrange multiplier
  (eta) in the profile likelihood maximization problem.  In the presence
  of nuisance parameters, _opt_ functions are  optimized over to profile
  out the nuisance parameters.
  
  Any method starting with _ci_limits calculates the log likelihood
  ratio for a specific value of a parameter and then subtracts a
  pre-specified critical value.  This is solved so that llr - crit = 0.
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docstr: Called by test_skew.  This function is optimized over
  nuisance parameters mu and sigma
  
  Parameters
  ----------
  nuis_params : 1darray
      An array with a  nuisance mean and variance parameter
  
  Returns
  -------
  llr : float
      The log likelihood ratio of a pre-specified skewness holding
      the nuisance parameters constant.
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code: def _opt_skew(self, nuis_params):
      """
      Called by test_skew.  This function is optimized over
      nuisance parameters mu and sigma
  
      Parameters
      ----------
      nuis_params : 1darray
          An array with a  nuisance mean and variance parameter
  
      Returns
      -------
      llr : float
          The log likelihood ratio of a pre-specified skewness holding
          the nuisance parameters constant.
      """
      endog = self.endog
      nobs = self.nobs
      mu_data = endog - nuis_params[0]
      sig_data = ((endog - nuis_params[0]) ** 2) - nuis_params[1]
      skew_data = (
          ((endog - nuis_params[0]) ** 3) / (nuis_params[1] ** 1.5)
      ) - self.skew0
      est_vect = np.column_stack((mu_data, sig_data, skew_data))
      eta_star = self._modif_newton(
          np.array([1.0 / nobs, 1.0 / nobs, 1.0 / nobs]),
          est_vect,
          np.ones(nobs) * (1.0 / nobs),
      )
      denom = 1.0 + np.dot(eta_star, est_vect.T)
      self.new_weights = 1.0 / nobs * 1.0 / denom
      llr = np.sum(np.log(nobs * self.new_weights))
      return -2 * llr
  
imports: import numpy
  from scipy import optimize
  from scipy.stats import chi2, skew, kurtosis
  from statsmodels.base.optimizer import _fit_newton
  import itertools
  from statsmodels.graphics import utils
label_desc: Use image node_id 11 for calling the _OptFuncts obj's underlying member method code with example usage: obj._opt_skew(nuis_params) and returns: unknown