diff --git a/CHANGELOG.md b/CHANGELOG.md
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--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,10 @@
+# gneiss changelog
+
+## Version 0.0.2  (changes since 0.0.2 go here)
+
+### Features
+* * Adding in a niche sorting algorithm `gneiss.sort.niche_sort` that can generate a band table given a gradient [#16](https://github.com/biocore/gneiss/pull/16)
+* Adding in utility functions for handing feature tables, metadata, and trees. [#12](https://github.com/biocore/gneiss/pull/12)
+* Adding GPL license.
+
+### Bug fixes
diff --git a/COPYING.txt b/COPYING.txt
new file mode 100644
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--- /dev/null
+++ b/COPYING.txt
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
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+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
diff --git a/gneiss/__init__.py b/gneiss/__init__.py
index ccb3281..a27c03f 100644
--- a/gneiss/__init__.py
+++ b/gneiss/__init__.py
@@ -1,7 +1,7 @@
 # ----------------------------------------------------------------------------
 # Copyright (c) 2016--, gneiss development team.
 #
-# Distributed under the terms of the Modified BSD License.
+# Distributed under the terms of the GPLv3 License.
 #
 # The full license is in the file COPYING.txt, distributed with this software.
 # ----------------------------------------------------------------------------
@@ -9,4 +9,4 @@
 from __future__ import absolute_import, division, print_function
 
 
-__version__ = "0.0.1"
+__version__ = "0.0.2"
diff --git a/gneiss/layouts.py b/gneiss/layouts.py
index 56d88ad..a831cba 100644
--- a/gneiss/layouts.py
+++ b/gneiss/layouts.py
@@ -1,3 +1,11 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2016--, gneiss 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 ete3 import faces, AttrFace, CircleFace, BarChartFace
 
 
diff --git a/gneiss/sort.py b/gneiss/sort.py
new file mode 100644
index 0000000..70cdc58
--- /dev/null
+++ b/gneiss/sort.py
@@ -0,0 +1,116 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2016--, gneiss development team.
+#
+# Distributed under the terms of the GPLv3 License.
+#
+# The full license is in the file COPYING.txt, distributed with this software.
+# ----------------------------------------------------------------------------
+import numpy as np
+import pandas as pd
+from functools import partial
+from gneiss.util import match
+
+
+def mean_niche_estimator(abundances, gradient):
+    """ Estimates the mean niche of an organism.
+
+    Calculates the mean niche of an organism along a gradient.
+    This is done by calculating the expected value of an organism
+    across the gradient.
+
+    Specifically, this module calculates the following
+
+    .. math::
+        E[g | x] =
+         \sum\limits_{i=1}^N g_i \frac{x_i}{\sum\limits_{j=1}^N x_j}
+
+    Where :math:`N` is the number of samples, :math:`x_i` is the proportion of
+    species :math:`x` in sample :math:`i`, :math:`g_i` is the gradient value
+    at sample `i`.
+
+    Parameters
+    ----------
+    abundances : pd.Series, np.float
+        Vector of fraction abundances of an organism over
+        a list of samples.
+    gradient : pd.Series, np.float
+        Vector of numerical gradient values.
+
+    Returns
+    -------
+    np.float :
+        The mean gradient that the organism lives in.
+
+    Raises
+    ------
+    ValueError:
+        If the length of `abundances` is not the same length as `gradient`.
+    ValueError:
+        If the length of `gradient` contains nans.
+    """
+    len_abundances = len(abundances)
+    len_gradient = len(gradient)
+    if len_abundances != len_gradient:
+        raise ValueError("Length of `abundances` (%d) doesn't match the length"
+                         " of the `gradient` (%d)" % (len_abundances,
+                                                      len_gradient))
+    if np.any(pd.isnull(gradient)):
+        raise ValueError("`gradient` cannot have any nans.")
+
+    # normalizes the proportions of the organism across all of the
+    # samples to add to 1.
+    v = abundances / abundances.sum()
+    m = np.dot(gradient, v)
+    return m
+
+
+def niche_sort(table, gradient, niche_estimator=mean_niche_estimator):
+    """ Sort the table according to estimated niches.
+
+    Sorts the table by samples along the gradient
+    and otus by their estimated niche along the gradient.
+
+    Parameters
+    ----------
+    table : pd.DataFrame
+        Contingency table where samples are rows and
+        features (i.e. OTUs) are columns.
+    gradient : pd.Series
+        Vector of numerical gradient values.
+    niche_estimator : function, optional
+        A function that takes in two pandas series and returns an ordered
+        object. The ability for the object to be ordered is critical, since
+        this will allow the table to be sorted according to this ordering.
+        By default, `mean_niche_estimator` will be used.
+
+    Returns
+    -------
+    pd.DataFrame :
+        Sorted table according to the gradient of the samples, and the niches
+        of the organisms along that gradient.
+
+    Raises
+    ------
+    ValueError :
+        Raised if `niche_estimator` is not a function.
+    """
+    if not callable(niche_estimator):
+        raise ValueError("`niche_estimator` is not a function.")
+
+    table, gradient = match(table, gradient)
+
+    niche_estimator = partial(niche_estimator,
+                              gradient=gradient)
+
+    # normalizes feature abundances to sum to 1, for each sample.
+    # (i.e. scales values in each row to sum to 1).
+    normtable = table.apply(lambda x: x/x.sum(), axis=1)
+
+    # calculates estimated niche for each feature
+    est_niche = normtable.apply(niche_estimator, axis=0)
+    gradient = gradient.sort_values()
+    est_niche = est_niche.sort_values()
+
+    table = table.reindex(index=gradient.index,
+                          columns=est_niche.index)
+    return table
diff --git a/gneiss/tests/data/large_tree2.nwk b/gneiss/tests/data/large_tree2.nwk
new file mode 100644
index 0000000..a1fb1c7
--- /dev/null
+++ b/gneiss/tests/data/large_tree2.nwk
@@ -0,0 +1 @@
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diff --git a/gneiss/tests/data/small_tree.nwk b/gneiss/tests/data/small_tree.nwk
new file mode 100644
index 0000000..40c794f
--- /dev/null
+++ b/gneiss/tests/data/small_tree.nwk
@@ -0,0 +1 @@
+(O0:0.316212845735,(O1:0.0544249673249,((((O8:6.17283950617e-05,O9:6.17283950617e-05):0.000396077412446,(O6:0.00015943877551,O7:0.00015943877551):0.000298367031998):0.00183128228143,(O4:0.000555555555556,O5:0.000555555555556):0.00173353253338):0.0105194882737,(O2:0.00347222222222,O3:0.00347222222222):0.00933635414042):0.0416163909622):0.26178787841);
diff --git a/gneiss/tests/test_balances.py b/gneiss/tests/test_balances.py
index 9a8ff72..c46bb01 100644
--- a/gneiss/tests/test_balances.py
+++ b/gneiss/tests/test_balances.py
@@ -9,7 +9,8 @@
 from gneiss.layouts import default_layout
 from skbio import TreeNode
 from skbio.util import get_data_path
-
+from skbio.stats.composition import _check_orthogonality
+from gneiss.util import rename_internal_nodes
 
 class TestPlot(unittest.TestCase):
 
@@ -151,15 +152,30 @@ def test_balance_basis_large1(self):
             get_data_path('large_tree_basis.txt',
                           subfolder='data'))
         res_basis, res_keys = balance_basis(t)
+        _check_orthogonality(res_basis)
 
         exp_basis = exp_basis[:, ::-1]
         print(exp_basis.shape)
         for i in range(len(res_basis)):
             print(i)
+            print(exp_basis[i]- res_basis[i])
             npt.assert_allclose(exp_basis[i], res_basis[i])
 
-        npt.assert_allclose(exp_basis[:, ::-1], res_basis)
+        npt.assert_allclose(exp_basis[:, ::-1], res_basis, rtol=1e-5, atol=1e-5)
+
+    def test_balance_basis_large2(self):
+        fname = get_data_path('large_tree2.nwk',
+                              subfolder='data')
+        t = TreeNode.read(fname)
+        res_basis, res_keys = balance_basis(t)
+        _check_orthogonality(res_basis)
 
+    def test_balance_basis_small1(self):
+        fname = get_data_path('small_tree.nwk',
+                              subfolder='data')
+        t = TreeNode.read(fname)
+        res_basis, res_keys = balance_basis(t)
+        _check_orthogonality(res_basis)
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/gneiss/tests/test_sort.py b/gneiss/tests/test_sort.py
new file mode 100644
index 0000000..1288b75
--- /dev/null
+++ b/gneiss/tests/test_sort.py
@@ -0,0 +1,178 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2016--, gneiss development team.
+#
+# Distributed under the terms of the GPLv3 License.
+#
+# The full license is in the file COPYING.txt, distributed with this software.
+# ----------------------------------------------------------------------------
+import numpy as np
+import pandas as pd
+import unittest
+from gneiss.sort import niche_sort, mean_niche_estimator
+import pandas.util.testing as pdt
+
+
+class TestSort(unittest.TestCase):
+    def setUp(self):
+        pass
+
+    def test_mean_niche_estimator1(self):
+        gradient = pd.Series(
+            [1, 2, 3, 4, 5],
+            index=['s1', 's2', 's3', 's4', 's5'])
+        values = pd.Series(
+            [1, 1, 0, 0, 0],
+            index=['s1', 's2', 's3', 's4', 's5'])
+        m = mean_niche_estimator(values, gradient)
+        self.assertEqual(m, 1.5)
+
+    def test_mean_niche_estimator2(self):
+        gradient = pd.Series(
+            [1, 2, 3, 4, 5],
+            index=['s1', 's2', 's3', 's4', 's5'])
+        values = pd.Series(
+            [1, 3, 0, 0, 0],
+            index=['s1', 's2', 's3', 's4', 's5'])
+        m = mean_niche_estimator(values, gradient)
+        self.assertEqual(m, 1.75)
+
+    def test_mean_niche_estimator_bad_length(self):
+        gradient = pd.Series(
+            [1, 2, 3, 4, 5],
+            index=['s1', 's2', 's3', 's4', 's5'])
+        values = pd.Series(
+            [1, 3, 0, 0, 0, 0],
+            index=['s1', 's2', 's3', 's4', 's5', 's6'])
+
+        with self.assertRaises(ValueError):
+            mean_niche_estimator(values, gradient)
+
+    def test_mean_niche_estimator_missing(self):
+        gradient = pd.Series(
+            [1, 2, 3, 4, np.nan],
+            index=['s1', 's2', 's3', 's4', 's5'])
+        values = pd.Series(
+            [1, 3, 0, 0, 0],
+            index=['s1', 's2', 's3', 's4', 's5'])
+
+        with self.assertRaises(ValueError):
+            mean_niche_estimator(values, gradient)
+
+    def test_basic_niche_sort(self):
+        table = pd.DataFrame(
+            [[1, 1, 0, 0, 0],
+             [0, 1, 1, 0, 0],
+             [0, 0, 1, 1, 0],
+             [0, 0, 0, 1, 1]],
+            columns=['s1', 's2', 's3', 's4', 's5'],
+            index=['o1', 'o2', 'o3', 'o4']).T
+        gradient = pd.Series(
+            [1, 2, 3, 4, 5],
+            index=['s1', 's2', 's3', 's4', 's5'])
+        res_table = niche_sort(table, gradient)
+        pdt.assert_frame_equal(table, res_table)
+
+    def test_basic_niche_sort_error(self):
+        table = pd.DataFrame(
+            [[1, 1, 0, 0, 0],
+             [0, 1, 1, 0, 0],
+             [0, 0, 1, 1, 0],
+             [0, 0, 0, 1, 1]],
+            columns=['s1', 's2', 's3', 's4', 's5'],
+            index=['o1', 'o2', 'o3', 'o4']).T
+        gradient = pd.Series(
+            [1, 2, 3, 4, 5],
+            index=['s1', 's2', 's3', 's4', 's5'])
+        with self.assertRaises(ValueError):
+            niche_sort(table, gradient, niche_estimator='rawr')
+
+    def test_basic_niche_sort_scrambled(self):
+        # Swap samples s1 and s2 and features o1 and o2 to see if this can
+        # obtain the original table structure.
+        table = pd.DataFrame(
+            [[1, 0, 1, 0, 0],
+             [1, 1, 0, 0, 0],
+             [0, 0, 1, 1, 0],
+             [0, 0, 0, 1, 1]],
+            columns=['s2', 's1', 's3', 's4', 's5'],
+            index=['o2', 'o1', 'o3', 'o4']).T
+
+        gradient = pd.Series(
+            [2, 1, 3, 4, 5],
+            index=['s2', 's1', 's3', 's4', 's5'])
+
+        exp_table = pd.DataFrame(
+            [[1, 1, 0, 0, 0],
+             [0, 1, 1, 0, 0],
+             [0, 0, 1, 1, 0],
+             [0, 0, 0, 1, 1]],
+            columns=['s1', 's2', 's3', 's4', 's5'],
+            index=['o1', 'o2', 'o3', 'o4']).T
+
+        res_table = niche_sort(table, gradient)
+
+        pdt.assert_frame_equal(exp_table, res_table)
+
+    def test_basic_niche_sort_lambda(self):
+        table = pd.DataFrame(
+            [[1, 1, 0, 0, 0],
+             [0, 0, 1, 1, 0],
+             [0, 1, 1, 0, 0],
+             [0, 0, 0, 1, 1]],
+            columns=['s1', 's2', 's3', 's4', 's5'],
+            index=['o1', 'o3', 'o2', 'o4']).T
+        gradient = pd.Series(
+            [1, 2, 3, 4, 5],
+            index=['s1', 's2', 's3', 's4',  's5'])
+
+        exp_table = pd.DataFrame(
+            [[1, 1, 0, 0, 0],
+             [0, 1, 1, 0, 0],
+             [0, 0, 1, 1, 0],
+             [0, 0, 0, 1, 1]],
+            columns=['s1', 's2', 's3', 's4', 's5'],
+            index=['o1', 'o2', 'o3', 'o4']).T
+
+        def _dumb_estimator(v, gradient):
+            v[v > 0] = 1
+            values = v / v.sum()
+            return np.dot(gradient, values)
+
+        res_table = niche_sort(table, gradient,
+                               niche_estimator=_dumb_estimator)
+        pdt.assert_frame_equal(exp_table, res_table)
+
+    def test_basic_niche_sort_immutable(self):
+        # Swap samples s1 and s2 and features o1 and o2 to see if this can
+        # obtain the original table structure.
+        table = pd.DataFrame(
+            [[1, 0, 1, 0, 0],
+             [1, 1, 0, 0, 0],
+             [0, 0, 1, 1, 0],
+             [0, 0, 0, 1, 1]],
+            columns=['s2', 's1', 's3', 's4', 's5'],
+            index=['o2', 'o1', 'o3', 'o4']).T
+
+        gradient = pd.Series(
+            [2, 1, 3, 4, 5],
+            index=['s2', 's1', 's3', 's4', 's5'])
+
+        exp_table = pd.DataFrame(
+            [[1, 0, 1, 0, 0],
+             [1, 1, 0, 0, 0],
+             [0, 0, 1, 1, 0],
+             [0, 0, 0, 1, 1]],
+            columns=['s2', 's1', 's3', 's4', 's5'],
+            index=['o2', 'o1', 'o3', 'o4']).T
+
+        exp_gradient = pd.Series(
+            [2, 1, 3, 4, 5],
+            index=['s2', 's1', 's3', 's4', 's5'])
+
+        niche_sort(table, gradient)
+        pdt.assert_frame_equal(exp_table, table)
+        pdt.assert_series_equal(exp_gradient, gradient)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/gneiss/tests/test_util.py b/gneiss/tests/test_util.py
new file mode 100644
index 0000000..fecf40f
--- /dev/null
+++ b/gneiss/tests/test_util.py
@@ -0,0 +1,313 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2016--, gneiss development team.
+#
+# Distributed under the terms of the GPLv3 License.
+#
+# The full license is in the file COPYING.txt, distributed with this software.
+# ----------------------------------------------------------------------------
+
+import unittest
+import pandas as pd
+import pandas.util.testing as pdt
+from skbio import TreeNode
+from gneiss.util import match, match_tips, rename_internal_nodes
+
+
+class TestUtil(unittest.TestCase):
+
+    def test_match(self):
+        table = pd.DataFrame([[0, 0, 1, 1],
+                              [2, 2, 4, 4],
+                              [5, 5, 3, 3],
+                              [0, 0, 0, 1]],
+                             index=['s1', 's2', 's3', 's4'],
+                             columns=['o1', 'o2', 'o3', 'o4'])
+        metadata = pd.DataFrame([['a', 'control'],
+                                 ['b', 'control'],
+                                 ['c', 'diseased'],
+                                 ['d', 'diseased']],
+                                index=['s1', 's2', 's3', 's4'],
+                                columns=['Barcode', 'Treatment'])
+        exp_table, exp_metadata = table, metadata
+        res_table, res_metadata = match(table, metadata)
+
+        # make sure that the metadata and table indeces match
+        pdt.assert_index_equal(res_table.index, res_metadata.index)
+
+        res_table = res_table.sort_index()
+        exp_table = exp_table.sort_index()
+
+        res_metadata = res_metadata.sort_index()
+        exp_metadata = exp_metadata.sort_index()
+
+        pdt.assert_frame_equal(exp_table, res_table)
+        pdt.assert_frame_equal(exp_metadata, res_metadata)
+
+    def test_match_immutable(self):
+        # tests to make sure that the original tables don't change.
+        table = pd.DataFrame([[0, 0, 1, 1],
+                              [2, 2, 4, 4],
+                              [5, 5, 3, 3],
+                              [0, 0, 0, 1]],
+                             index=['s1', 's2', 's3', 's4'],
+                             columns=['o1', 'o2', 'o3', 'o4'])
+        metadata = pd.DataFrame([['a', 'control'],
+                                 ['c', 'diseased'],
+                                 ['b', 'control']],
+                                index=['s1', 's3', 's2'],
+                                columns=['Barcode', 'Treatment'])
+
+        exp_table = pd.DataFrame([[0, 0, 1, 1],
+                                  [2, 2, 4, 4],
+                                  [5, 5, 3, 3],
+                                  [0, 0, 0, 1]],
+                                 index=['s1', 's2', 's3', 's4'],
+                                 columns=['o1', 'o2', 'o3', 'o4'])
+        exp_metadata = pd.DataFrame([['a', 'control'],
+                                     ['c', 'diseased'],
+                                     ['b', 'control']],
+                                    index=['s1', 's3', 's2'],
+                                    columns=['Barcode', 'Treatment'])
+        match(table, metadata)
+        pdt.assert_frame_equal(table, exp_table)
+        pdt.assert_frame_equal(metadata, exp_metadata)
+
+    def test_match_duplicate(self):
+        table1 = pd.DataFrame([[0, 0, 1, 1],
+                               [2, 2, 4, 4],
+                               [5, 5, 3, 3],
+                               [0, 0, 0, 1]],
+                              index=['s2', 's2', 's3', 's4'],
+                              columns=['o1', 'o2', 'o3', 'o4'])
+        metadata1 = pd.DataFrame([['a', 'control'],
+                                  ['b', 'control'],
+                                  ['c', 'diseased'],
+                                  ['d', 'diseased']],
+                                 index=['s1', 's2', 's3', 's4'],
+                                 columns=['Barcode', 'Treatment'])
+
+        table2 = pd.DataFrame([[0, 0, 1, 1],
+                               [2, 2, 4, 4],
+                               [5, 5, 3, 3],
+                               [0, 0, 0, 1]],
+                              index=['s1', 's2', 's3', 's4'],
+                              columns=['o1', 'o2', 'o3', 'o4'])
+        metadata2 = pd.DataFrame([['a', 'control'],
+                                  ['b', 'control'],
+                                  ['c', 'diseased'],
+                                  ['d', 'diseased']],
+                                 index=['s1', 's1', 's3', 's4'],
+                                 columns=['Barcode', 'Treatment'])
+
+        with self.assertRaises(ValueError):
+            match(table1, metadata1)
+        with self.assertRaises(ValueError):
+            match(table2, metadata2)
+
+    def test_match_scrambled(self):
+        table = pd.DataFrame([[0, 0, 1, 1],
+                              [2, 2, 4, 4],
+                              [5, 5, 3, 3],
+                              [0, 0, 0, 1]],
+                             index=['s1', 's2', 's3', 's4'],
+                             columns=['o1', 'o2', 'o3', 'o4'])
+        metadata = pd.DataFrame([['a', 'control'],
+                                 ['c', 'diseased'],
+                                 ['b', 'control'],
+                                 ['d', 'diseased']],
+                                index=['s1', 's3', 's2', 's4'],
+                                columns=['Barcode', 'Treatment'])
+        exp_table = table
+        exp_metadata = pd.DataFrame([['a', 'control'],
+                                     ['b', 'control'],
+                                     ['c', 'diseased'],
+                                     ['d', 'diseased']],
+                                    index=['s1', 's2', 's3', 's4'],
+                                    columns=['Barcode', 'Treatment'])
+
+        res_table, res_metadata = match(table, metadata)
+        # make sure that the metadata and table indeces match
+        pdt.assert_index_equal(res_table.index, res_metadata.index)
+
+        res_table = res_table.sort_index()
+        exp_table = exp_table.sort_index()
+
+        res_metadata = res_metadata.sort_index()
+        exp_metadata = exp_metadata.sort_index()
+
+        pdt.assert_frame_equal(exp_table, res_table)
+        pdt.assert_frame_equal(exp_metadata, res_metadata)
+
+    def test_match_intersect(self):
+        table = pd.DataFrame([[0, 0, 1, 1],
+                              [2, 2, 4, 4],
+                              [5, 5, 3, 3],
+                              [0, 0, 0, 1]],
+                             index=['s1', 's2', 's3', 's4'],
+                             columns=['o1', 'o2', 'o3', 'o4'])
+        metadata = pd.DataFrame([['a', 'control'],
+                                 ['c', 'diseased'],
+                                 ['b', 'control']],
+                                index=['s1', 's3', 's2'],
+                                columns=['Barcode', 'Treatment'])
+
+        exp_table = pd.DataFrame([[0, 0, 1, 1],
+                                  [2, 2, 4, 4],
+                                  [5, 5, 3, 3]],
+                                 index=['s1', 's2', 's3'],
+                                 columns=['o1', 'o2', 'o3', 'o4'])
+
+        exp_metadata = pd.DataFrame([['a', 'control'],
+                                     ['b', 'control'],
+                                     ['c', 'diseased']],
+                                    index=['s1', 's2', 's3'],
+                                    columns=['Barcode', 'Treatment'])
+
+        res_table, res_metadata = match(table, metadata)
+        # sort for comparison, since the match function
+        # scrambles the names due to hashing.
+        res_table = res_table.sort_index()
+        res_metadata = res_metadata.sort_index()
+        pdt.assert_frame_equal(exp_table, res_table)
+        pdt.assert_frame_equal(exp_metadata, res_metadata)
+
+    def test_match_tips(self):
+        table = pd.DataFrame([[0, 0, 1, 1],
+                              [2, 2, 4, 4],
+                              [5, 5, 3, 3],
+                              [0, 0, 0, 1]],
+                             index=['s1', 's2', 's3', 's4'],
+                             columns=['a', 'b', 'c', 'd'])
+        tree = TreeNode.read([u"(((a,b)f, c),d)r;"])
+        exp_table, exp_tree = table, tree
+        res_table, res_tree = match_tips(table, tree)
+        pdt.assert_frame_equal(exp_table, res_table)
+        self.assertEqual(str(exp_tree), str(res_tree))
+
+    def test_match_tips_scrambled_tips(self):
+        table = pd.DataFrame([[0, 0, 1, 1],
+                              [2, 3, 4, 4],
+                              [5, 5, 3, 3],
+                              [0, 0, 0, 1]],
+                             index=['s1', 's2', 's3', 's4'],
+                             columns=['a', 'b', 'c', 'd'])
+        tree = TreeNode.read([u"(((b,a)f, c),d)r;"])
+        exp_tree = tree
+        exp_table = pd.DataFrame([[0, 0, 1, 1],
+                                  [3, 2, 4, 4],
+                                  [5, 5, 3, 3],
+                                  [0, 0, 0, 1]],
+                                 index=['s1', 's2', 's3', 's4'],
+                                 columns=['b', 'a', 'c', 'd'])
+
+        res_table, res_tree = match_tips(table, tree)
+        pdt.assert_frame_equal(exp_table, res_table)
+        self.assertEqual(str(exp_tree), str(res_tree))
+
+    def test_match_tips_scrambled_columns(self):
+        table = pd.DataFrame([[0, 0, 1, 1],
+                              [3, 2, 4, 4],
+                              [5, 5, 3, 3],
+                              [0, 0, 0, 1]],
+                             index=['s1', 's2', 's3', 's4'],
+                             columns=['b', 'a', 'c', 'd'])
+        tree = TreeNode.read([u"(((a,b)f, c),d)r;"])
+        exp_tree = tree
+        exp_table = pd.DataFrame([[0, 0, 1, 1],
+                                  [2, 3, 4, 4],
+                                  [5, 5, 3, 3],
+                                  [0, 0, 0, 1]],
+                                 index=['s1', 's2', 's3', 's4'],
+                                 columns=['a', 'b', 'c', 'd'])
+
+        res_table, res_tree = match_tips(table, tree)
+        pdt.assert_frame_equal(exp_table, res_table)
+        self.assertEqual(str(exp_tree), str(res_tree))
+
+    def test_match_tips_intersect_tips(self):
+        # there are less tree tips than table columns
+        table = pd.DataFrame([[0, 0, 1, 1],
+                              [2, 3, 4, 4],
+                              [5, 5, 3, 3],
+                              [0, 0, 0, 1]],
+                             index=['s1', 's2', 's3', 's4'],
+                             columns=['a', 'b', 'c', 'd'])
+        tree = TreeNode.read([u"((a,b)f,d)r;"])
+        exp_table = pd.DataFrame([[0, 0, 1],
+                                  [2, 3, 4],
+                                  [5, 5, 3],
+                                  [0, 0, 1]],
+                                 index=['s1', 's2', 's3', 's4'],
+                                 columns=['a', 'b', 'd'])
+        exp_tree = tree
+        res_table, res_tree = match_tips(table, tree)
+        pdt.assert_frame_equal(exp_table, res_table)
+        self.assertEqual(str(exp_tree), str(res_tree))
+
+    def test_match_tips_intersect_columns(self):
+        # table has less columns than tree tips
+        table = pd.DataFrame([[0, 0, 1],
+                              [2, 3, 4],
+                              [5, 5, 3],
+                              [0, 0, 1]],
+                             index=['s1', 's2', 's3', 's4'],
+                             columns=['a', 'b', 'd'])
+        tree = TreeNode.read([u"(((a,b)f, c),d)r;"])
+        exp_table = pd.DataFrame([[1, 0, 0],
+                                  [4, 2, 3],
+                                  [3, 5, 5],
+                                  [1, 0, 0]],
+                                 index=['s1', 's2', 's3', 's4'],
+                                 columns=['d', 'a', 'b'])
+        exp_tree = TreeNode.read([u"(d,(a,b)f)r;"])
+        res_table, res_tree = match_tips(table, tree)
+        pdt.assert_frame_equal(exp_table, res_table)
+        self.assertEqual(str(exp_tree), str(res_tree))
+
+    def test_match_tips_intersect_tree_immutable(self):
+        # tests to see if tree chnages.
+        table = pd.DataFrame([[0, 0, 1],
+                              [2, 3, 4],
+                              [5, 5, 3],
+                              [0, 0, 1]],
+                             index=['s1', 's2', 's3', 's4'],
+                             columns=['a', 'b', 'd'])
+        tree = TreeNode.read([u"(((a,b)f, c),d)r;"])
+        match_tips(table, tree)
+        self.assertEqual(str(tree), u"(((a,b)f,c),d)r;\n")
+
+    def test_rename_internal_nodes(self):
+        tree = TreeNode.read([u"(((a,b), c),d)r;"])
+        exp_tree = TreeNode.read([u"(((a,b)y2, c)y1,d)y0;"])
+        res_tree = rename_internal_nodes(tree)
+        self.assertEqual(str(exp_tree), str(res_tree))
+
+    def test_rename_internal_nodes_names(self):
+        tree = TreeNode.read([u"(((a,b), c),d)r;"])
+        exp_tree = TreeNode.read([u"(((a,b)ab, c)abc,d)r;"])
+        res_tree = rename_internal_nodes(tree, ['r', 'abc', 'ab'])
+        self.assertEqual(str(exp_tree), str(res_tree))
+
+    def test_rename_internal_nodes_names_mismatch(self):
+        tree = TreeNode.read([u"(((a,b), c),d)r;"])
+        with self.assertRaises(ValueError):
+            rename_internal_nodes(tree, ['r', 'abc'])
+
+    def test_rename_internal_nodes_warning(self):
+        tree = TreeNode.read([u"(((a,b)y2, c),d)r;"])
+        with self.assertWarns(Warning):
+            rename_internal_nodes(tree)
+
+    def test_rename_internal_nodes_immutable(self):
+        tree = TreeNode.read([u"(((a,b)y2, c),d)r;"])
+        rename_internal_nodes(tree)
+        self.assertEqual(str(tree), "(((a,b)y2,c),d)r;\n")
+
+    def test_rename_internal_nodes_mutable(self):
+        tree = TreeNode.read([u"(((a,b)y2, c),d)r;"])
+        rename_internal_nodes(tree, inplace=True)
+        self.assertEqual(str(tree), "(((a,b)y2,c)y1,d)y0;\n")
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/gneiss/util.py b/gneiss/util.py
new file mode 100644
index 0000000..98e7031
--- /dev/null
+++ b/gneiss/util.py
@@ -0,0 +1,162 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2016--, gneiss development team.
+#
+# Distributed under the terms of the GPLv3 License.
+#
+# The full license is in the file COPYING.txt, distributed with this software.
+# ----------------------------------------------------------------------------
+import warnings
+
+
+def match(table, metadata):
+    """ Matches samples between a contingency table and a metadata table.
+
+    Sorts samples in metadata and contingency table in the same order.
+    If there are sames contained in the contigency table, but not in metadata
+    or vice versa, the intersection of samples in the contingency table and the
+    metadata table will returned.
+
+    Parameters
+    ----------
+    table : pd.DataFrame
+        Contingency table where samples correspond to rows and
+        features correspond to columns.
+    metadata: pd.DataFrame
+        Metadata table where samples correspond to rows and
+        explanatory metadata variables correspond to columns.
+
+    Returns
+    -------
+    pd.DataFrame :
+        Filtered contingency table.
+    pd.DataFrame :
+        Filtered metadata table
+
+    Raises
+    ------
+    ValueError:
+        Raised if duplicate sample ids are present in `table`.
+    ValueError:
+        Raised if duplicate sample ids are present in `metadata`.
+    ValueError:
+        Raised if `table` and `metadata` have incompatible sizes.
+
+    """
+    subtableids = set(table.index)
+    submetadataids = set(metadata.index)
+    if len(subtableids) != len(table.index):
+        raise ValueError("`table` has duplicate sample ids.")
+    if len(submetadataids) != len(metadata.index):
+        raise ValueError("`metadata` has duplicate sample ids.")
+
+    idx = subtableids & submetadataids
+    subtable = table.loc[idx]
+    submetadata = metadata.loc[idx]
+    return subtable, submetadata
+
+
+def match_tips(table, tree):
+    """ Returns the contingency table and tree with matched tips.
+
+    Sorts the columns of the contingency table to match the tips in
+    the tree.  The ordering of the tips is in post-traversal order.
+
+    If the tree is multi-furcating, then the tree is reduced to a
+    bifurcating tree by randomly inserting internal nodes.
+
+    The intersection of samples in the contingency table and the
+    tree will returned.
+
+    Parameters
+    ----------
+    table : pd.DataFrame
+        Contingency table where samples correspond to rows and
+        features correspond to columns.
+    tree : skbio.TreeNode
+        Tree object where the leafs correspond to the features.
+
+    Returns
+    -------
+    pd.DataFrame :
+        Subset of the original contingency table with the common features.
+    skbio.TreeNode :
+        Sub-tree with the common features.
+
+    Raises
+    ------
+    ValueError:
+        Raised if `table` and `tree` have incompatible sizes.
+
+    See Also
+    --------
+    skbio.TreeNode.bifurcate
+    skbio.TreeNode.tips
+    """
+    tips = [x.name for x in tree.tips()]
+    common_tips = list(set(tips) & set(table.columns))
+
+    _table = table.loc[:, common_tips]
+    _tree = tree.shear(names=common_tips)
+
+    _tree.bifurcate()
+    _tree.prune()
+    sorted_features = [n.name for n in _tree.tips()]
+    _table = _table.reindex_axis(sorted_features, axis=1)
+
+    return _table, _tree
+
+
+def rename_internal_nodes(tree, names=None, inplace=False):
+    """ Names the internal according to level ordering.
+
+    The tree will be traversed in level order (i.e. top-down, left to right).
+    If `names` is not specified, the node with the smallest label (y0)
+    will be located at the root of the tree, and the node with the largest
+    label will be located at bottom right corner of the tree.
+
+    Parameters
+    ----------
+    tree : skbio.TreeNode
+        Tree object where the leafs correspond to the features.
+    names : list, optional
+        List of labels to rename the tip names.  It is assumed that the
+        names are listed in level ordering, and the length of the list
+        is at least as long as the number of internal nodes.
+    inplace : bool, optional
+        Specifies if the operation should be done on the original tree or not.
+
+    Returns
+    -------
+    skbio.TreeNode
+       Tree with renamed internal nodes.
+
+    Raises
+    ------
+    ValueError:
+        Raised if `tree` and `name` have incompatible sizes.
+    """
+    if inplace:
+        _tree = tree
+    else:
+        _tree = tree.copy()
+
+    non_tips = [n for n in _tree.levelorder() if not n.is_tip()]
+    if names is not None and len(non_tips) != len(names):
+        raise ValueError("`_tree` and `names` have incompatible sizes, "
+                         "`_tree` has %d tips, `names` has %d elements." %
+                         (len(non_tips), len(names)))
+
+    i = 0
+    for n in _tree.levelorder():
+        if not n.is_tip():
+            if names is None:
+                label = 'y%i' % i
+            else:
+                label = names[i]
+            if n.name is not None and label == n.name:
+                warnings.warn("Warning. Internal node (%s) has been replaced "
+                              "with (%s)" % (n.name, label))
+
+            n.name = label
+            i += 1
+    return _tree
diff --git a/ipynb/balance_trees.ipynb b/ipynb/balance_trees.ipynb
index 13d41be..96f22b9 100644
--- a/ipynb/balance_trees.ipynb
+++ b/ipynb/balance_trees.ipynb
@@ -45,7 +45,7 @@
     "$$\n",
     "b_i = \\sqrt{\\frac{l_i r_i}{l_i + r_i}} \n",
     " \\log \\big( \\frac{(\\prod_{x_n \\in L}{x_n})^{1/l_i}}\n",
-    " {(\\prod_{x_m \\in L}{x_m})^{1/r_i}} \\big)\n",
+    " {(\\prod_{x_m \\in R}{x_m})^{1/r_i}} \\big)\n",
     "$$\n",
     "\n",
     "where $b_i$ is the $i$th balance, $l_i$ is the number of leaves in the left subtree, $r_i$ is the number of leaves in the right subtree  $x_n$ are the abundances of the species in the left subtree, and $x_m$ are the abundances of species in the right subtree.\n"
diff --git a/setup.py b/setup.py
index 2d9f4f7..28a305e 100644
--- a/setup.py
+++ b/setup.py
@@ -35,13 +35,13 @@ def finalize_options(self):
     extensions = cythonize(extensions)
 
 classes = """
-    Development Status :: 0 - pre-alpha
+    Development Status :: 2 - Pre-Alpha
     License :: OSI Approved :: BSD License
     Topic :: Software Development :: Libraries
     Topic :: Scientific/Engineering
     Topic :: Scientific/Engineering :: Bio-Informatics
-    Programming Language :: Python :: 2
-    Programming Language :: Python :: 2 :: Only
+    Programming Language :: Python :: 3
+    Programming Language :: Python :: 3 :: Only
     Operating System :: Unix
     Operating System :: POSIX
     Operating System :: MacOS :: MacOS X