Update URL of GH repository

README.rst

@@ -7,29 +7,29 @@ bandicoot
     :alt: Version
 
 .. image:: https://img.shields.io/pypi/l/bandicoot.svg
-    :target: https://github.com/yvesalexandre/bandicoot/blob/master/LICENSE
+    :target: https://github.com/computationalprivacy/bandicoot/blob/master/LICENSE
     :alt: MIT License
 
 .. image:: https://img.shields.io/pypi/dm/bandicoot.svg
     :target: https://pypi.python.org/pypi/bandicoot
     :alt: PyPI downloads
 
-.. image:: https://img.shields.io/travis/yvesalexandre/bandicoot.svg
-    :target: https://travis-ci.org/yvesalexandre/bandicoot
+.. image:: https://img.shields.io/travis/computationalprivacy/bandicoot.svg
+    :target: https://travis-ci.org/computationalprivacy/bandicoot
     :alt: Continuous integration
 
 .. begin
 
 **bandicoot** (http://bandicoot.mit.edu) is Python toolbox to analyze mobile phone metadata. It provides a complete, easy-to-use environment for data-scientist to analyze mobile phone metadata. With only a few lines of code, load your datasets, visualize the data, perform analyses, and export the results.
 
-.. image:: https://raw.githubusercontent.com/yvesalexandre/bandicoot/master/docs/_static/bandicoot-dashboard.png
+.. image:: https://raw.githubusercontent.com/computationalprivacy/bandicoot/master/docs/_static/bandicoot-dashboard.png
     :alt: Bandicoot interactive visualization
 
 ---------------
 Where to get it
 ---------------
 
-The source code is currently hosted on Github at https://github.com/yvesalexandre/bandicoot. Binary installers for the latest released version are available at the Python package index:
+The source code is currently hosted on Github at https://github.com/computationalprivacy/bandicoot. Binary installers for the latest released version are available at the Python package index:
 
     http://pypi.python.org/pypi/bandicoot/
 

docs/quickstart.rst

@@ -8,7 +8,7 @@ The easiest way to install it is using PyPI::
         pip install bandicoot
 
 
-Alternatively, you can download it from `here <https://github.com/yvesalexandre/bandicoot/archive/master.zip>`_. Once unzipped you can either import it ``import bandicoot as bc`` or install it::
+Alternatively, you can download it from `here <https://github.com/computationalprivacy/bandicoot/archive/master.zip>`_. Once unzipped you can either import it ``import bandicoot as bc`` or install it::
 
         python setup.py install
 
@@ -242,5 +242,5 @@ The following code will load all the users in one directory, compute the indicat
 
   bc.to_csv(indicators, 'bandicoot_indicators_full.csv')
 
-The full pipeline file is available `here <https://github.com/yvesalexandre/bandicoot/blob/master/sample_code/full_pipeline.py>`_. A parallel version using `MultiProcessing <https://docs.python.org/2/library/multiprocessing.html>`_ is available `here <https://github.com/yvesalexandre/bandicoot/blob/master/sample_code/full_pipeline_mp.py>`_.
+The full pipeline file is available `here <https://github.com/computationalprivacy/bandicoot/blob/master/sample_code/full_pipeline.py>`_. A parallel version using `MultiProcessing <https://docs.python.org/2/library/multiprocessing.html>`_ is available `here <https://github.com/computationalprivacy/bandicoot/blob/master/sample_code/full_pipeline_mp.py>`_.
 

setup.py

@@ -9,8 +9,8 @@
     name='bandicoot',
     author='Yves-Alexandre de Montjoye',
     author_email='[email protected]',
-    version="0.5.3",
-    url="https://github.com/yvesalexandre/bandicoot",
+    version="0.5.4",
+    url="https://github.com/computationalprivacy/bandicoot",
     license="MIT",
     packages=[
         'bandicoot',