docstr: Values were obtained via the R `energy` package.
  
  R code:
  ------
  > dcov.test(x, y, R=200)
  
      dCov independence test (permutation test)
  
  data:  index 1, replicates 200
  nV^2 = 45829, p-value = 0.004975
  sample estimates:
      dCov
  47.86925
  
  > DCOR(x, y)
  $dCov
  [1] 47.86925
  
  $dCor
  [1] 0.9999704
  
  $dVarX
  [1] 47.28702
  
  $dVarY
  [1] 48.46151
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code: def setup_class(cls):
      """
      Values were obtained via the R `energy` package.
  
      R code:
      ------
      > dcov.test(x, y, R=200)
  
          dCov independence test (permutation test)
  
      data:  index 1, replicates 200
      nV^2 = 45829, p-value = 0.004975
      sample estimates:
          dCov
      47.86925
  
      > DCOR(x, y)
      $dCov
      [1] 47.86925
  
      $dCor
      [1] 0.9999704
  
      $dVarX
      [1] 47.28702
  
      $dVarY
      [1] 48.46151
      """
      np.random.seed(3)
  
      cls.x = np.array(range(1, 101)).reshape((20, 5))
      cls.y = cls.x + np.log(cls.x)
  
      cls.dcor_exp = 0.9999704
      cls.dcov_exp = 47.86925
      cls.dvar_x_exp = 47.28702
      cls.dvar_y_exp = 48.46151
      cls.pval_emp_exp = 0.004975
      cls.test_stat_emp_exp = 45829
  
      # The values above are functions of the following values, and
      # therefore when the above group of variables is computed correctly
      # it means this group of variables was also correctly calculated.
      cls.S_exp = 5686.03162
      cls.test_stat_asym_exp = 2.8390102
      cls.pval_asym_exp = 0.00452
  
imports: import numpy
  from numpy.testing import assert_almost_equal
  import pytest
  from statsmodels.datasets import get_rdataset
  from statsmodels.datasets.tests.test_utils import IGNORED_EXCEPTIONS
  import statsmodels.stats.dist_dependence_measures
  from statsmodels.tools.sm_exceptions import HypothesisTestWarning
label_desc: Use image node_id 1 for calling the TestDistDependenceMeasures obj's underlying member method code with example usage: obj.setup_class(cls) without return types