old_contents: # encoding: utf-8
  
  """
  dcor: distance correlation and related E-statistics in Python.
  
  E-statistics are functions of distances between statistical observations
  in metric spaces.
  
  Distance covariance and distance correlation are
  dependency measures between random vectors introduced in [SRB07]_ with
  a simple E-statistic estimator.
  
  This package offers functions for calculating several E-statistics
  such as:
  
  - Estimator of the energy distance [SR13]_.
  - Biased and unbiased estimators of distance covariance and
    distance correlation [SRB07]_.
  - Estimators of the partial distance covariance and partial
    distance covariance [SR14]_.
  
  It also provides tests based on these E-statistics:
  
  - Test of homogeneity based on the energy distance.
  - Test of independence based on distance covariance.
  
  References
  ----------
  .. [SR13] Gábor J. Székely and Maria L. Rizzo. Energy statistics: a class of
             statistics based on distances. Journal of Statistical Planning and
             Inference, 143(8):1249 – 1272, 2013.
             URL:
             http://www.sciencedirect.com/science/article/pii/S0378375813000633,
             doi:10.1016/j.jspi.2013.03.018.
  .. [SR14]  Gábor J. Székely and Maria L. Rizzo. Partial distance correlation
             with methods for dissimilarities. The Annals of Statistics,
             42(6):2382–2412, 12 2014.
             doi:10.1214/14-AOS1255.
  .. [SRB07] Gábor J. Székely, Maria L. Rizzo, and Nail K. Bakirov. Measuring and
             testing dependence by correlation of distances. The Annals of
             Statistics, 35(6):2769–2794, 12 2007.
             doi:10.1214/009053607000000505.
  
  """
  import os
  import pathlib
  
  from setuptools import find_packages, setup
  
  version = (
      pathlib.Path(os.path.dirname(__file__)) / 'dcor' / 'VERSION'
  ).read_text().strip()
  
  setup(
      version=version,
      include_package_data=True,
      packages=find_packages(),
  )
  
new_contents: # encoding: utf-8
  
  """
  dcor: distance correlation and related E-statistics in Python.
  
  E-statistics are functions of distances between statistical observations
  in metric spaces.
  
  Distance covariance and distance correlation are
  dependency measures between random vectors introduced in [SRB07]_ with
  a simple E-statistic estimator.
  
  This package offers functions for calculating several E-statistics
  such as:
  
  - Estimator of the energy distance [SR13]_.
  - Biased and unbiased estimators of distance covariance and
    distance correlation [SRB07]_.
  - Estimators of the partial distance covariance and partial
    distance covariance [SR14]_.
  
  It also provides tests based on these E-statistics:
  
  - Test of homogeneity based on the energy distance.
  - Test of independence based on distance covariance.
  
  References
  ----------
  .. [SR13] Gábor J. Székely and Maria L. Rizzo. Energy statistics: a class of
             statistics based on distances. Journal of Statistical Planning and
             Inference, 143(8):1249 – 1272, 2013.
             URL:
             http://www.sciencedirect.com/science/article/pii/S0378375813000633,
             doi:10.1016/j.jspi.2013.03.018.
  .. [SR14]  Gábor J. Székely and Maria L. Rizzo. Partial distance correlation
             with methods for dissimilarities. The Annals of Statistics,
             42(6):2382–2412, 12 2014.
             doi:10.1214/14-AOS1255.
  .. [SRB07] Gábor J. Székely, Maria L. Rizzo, and Nail K. Bakirov. Measuring and
             testing dependence by correlation of distances. The Annals of
             Statistics, 35(6):2769–2794, 12 2007.
             doi:10.1214/009053607000000505.
  
  """
  import os
  import pathlib
  
  from setuptools import find_packages, setup
  
  version = (
      pathlib.Path(os.path.dirname(__file__)) / 'dcor' / 'VERSION'
  ).read_text().strip()
  
  setup(
      name="dcor",
      version=version,
      include_package_data=True,
      packages=find_packages(),
  )
  
subject: Update setup.py