old_contents: r"""
  Ordination methods (:mod:`skbio.math.stats.ordination`)
  =======================================================
  
  .. currentmodule:: skbio.math.stats.ordination
  
  This module contains several ordination methods, including Principal
  Coordinate Analysis, Correspondence Analysis, Redundancy Analysis and
  Canonical Correspondence Analysis.
  
  Classes
  -------
  
  .. autosummary::
     :toctree: generated/
  
     PCoA
     CA
     RDA
     CCA
     OrdinationResults
  
  """
  # ----------------------------------------------------------------------------
  # Copyright (c) 2013--, scikit-bio development team.
  #
  # Distributed under the terms of the Modified BSD License.
  #
  # The full license is in the file COPYING.txt, distributed with this software.
  # ----------------------------------------------------------------------------
  
  from .correspondence_analysis import CA
  from .redundancy_analysis import RDA
  from .canonical_correspondence_analysis import CCA
  from .principal_coordinate_analysis import PCoA
  from .base import OrdinationResults
  
  __all__ = ['CA', 'RDA', 'CCA', 'PCoA', 'OrdinationResults']
  
  from numpy.testing import Tester
  test = Tester().test
  
new_contents: r"""
  Ordination methods (:mod:`skbio.math.stats.ordination`)
  =======================================================
  
  .. currentmodule:: skbio.math.stats.ordination
  
  This module contains several ordination methods, including Principal
  Coordinate Analysis, Correspondence Analysis, Redundancy Analysis and
  Canonical Correspondence Analysis.
  
  Classes
  -------
  
  .. autosummary::
     :toctree: generated/
  
     PCoA
     CA
     RDA
     CCA
     OrdinationResults
  
  Examples
  --------
  
  Let's import a few utility functions to load the data:
  
  >>> from skbio.util.testing import get_data_path
  >>> import os
  >>> from functools import partial
  >>> path = partial(get_data_path, subfolder=os.path.join('tests', 'data'))
  
  It's an artificial dataset (table 11.3 in [1]_) that represents fish
  abundance in different sites (`Y`, the response variables) and
  environmental variables (`X`, the explanatory variables).
  
  >>> import numpy as np
  >>> X = np.loadtxt(path('example3_X'))
  >>> Y = np.loadtxt(path('example3_Y'))
  
  We can now create a CCA object to perform canonical correspondence
  analysis. Matrix `X` contains a continuous variable (depth) and a
  categorical one (substrate type) encoded using a one-hot encoding. We
  explicitly need to avoid perfect collinearity, so we'll drop one of
  the substrate types (the last column of `X`). We also expect to
  increase pandas integration to ease analyses.
  
  >>> from skbio.maths.stats.ordination import CCA
  >>> ordination_result = CCA(Y, X[:, :-1],
                              ['Site0', 'Site1', 'Site2', 'Site3', 'Site4',
                               'Site5', 'Site6', 'Site7', 'Site8', 'Site9'],
                              ['Species0', 'Species1', 'Species2', 'Species3',
                               'Species4', 'Species5', 'Species6', 'Species7',
                               'Species8'])
  
  Exploring the results we see that the first three axes explain about
  80% of all the variance.
  
  >>> sc_2 = ordination_result.scores(scaling=2)
  >>> print sc_2.proportion_explained
  [ 0.46691091  0.23832652  0.10054837  0.10493671  0.04480535  0.02974698
    0.01263112  0.00156168  0.00053235]
  
  References
  ----------
  
  .. [1] Legendre P. and Legendre L. 1998. Numerical Ecology. Elsevier,
     Amsterdam.
  
  """
  # ----------------------------------------------------------------------------
  # Copyright (c) 2013--, scikit-bio development team.
  #
  # Distributed under the terms of the Modified BSD License.
  #
  # The full license is in the file COPYING.txt, distributed with this software.
  # ----------------------------------------------------------------------------
  
  from .correspondence_analysis import CA
  from .redundancy_analysis import RDA
  from .canonical_correspondence_analysis import CCA
  from .principal_coordinate_analysis import PCoA
  from .base import OrdinationResults
  
  __all__ = ['CA', 'RDA', 'CCA', 'PCoA', 'OrdinationResults']
  
  from numpy.testing import Tester
  test = Tester().test
  
subject: Add some examples to ordination's init.