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: Used to determine the confidence intervals for the variance.
  It calls test_var and when called by an optimizer,
  finds the value of sig2_0 that is chi2.ppf(significance-level)
  
  Parameters
  ----------
  var_test : float
      Hypothesized value of the variance
  
  Returns
  -------
  diff : float
      The difference between the log likelihood ratio at var_test and a
      pre-specified value.
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code: def _ci_limits_var(self, var):
      """
      Used to determine the confidence intervals for the variance.
      It calls test_var and when called by an optimizer,
      finds the value of sig2_0 that is chi2.ppf(significance-level)
  
      Parameters
      ----------
      var_test : float
          Hypothesized value of the variance
  
      Returns
      -------
      diff : float
          The difference between the log likelihood ratio at var_test and a
          pre-specified value.
      """
      return self.test_var(var)[0] - self.r0
  
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 10 for calling the _OptFuncts obj's underlying member method code with example usage: obj._ci_limits_var(var) and returns: unknown