Github organization change

README.md

@@ -7,10 +7,10 @@ Prophet is [open source software](https://code.facebook.com/projects/) released
 ## Important links
 
 
-- Homepage: https://facebookincubator.github.io/prophet/
-- HTML documentation: https://facebookincubator.github.io/prophet/docs/quick_start.html
-- Issue tracker: https://github.com/facebookincubator/prophet/issues
-- Source code repository: https://github.com/facebookincubator/prophet
+- Homepage: https://facebook.github.io/prophet/
+- HTML documentation: https://facebook.github.io/prophet/docs/quick_start.html
+- Issue tracker: https://github.com/facebook/prophet/issues
+- Source code repository: https://github.com/facebook/prophet
 - Prophet R package: https://cran.r-project.org/package=prophet
 - Prophet Python package: https://pypi.python.org/pypi/fbprophet/
 - Release blogpost: https://research.fb.com/prophet-forecasting-at-scale/
@@ -25,7 +25,7 @@ Prophet is a [CRAN package](https://cran.r-project.org/package=prophet) so you c
 > install.packages('prophet')
 ```
 
-After installation, you can [get started!](https://facebookincubator.github.io/prophet/docs/quick_start.html#r-api)
+After installation, you can [get started!](https://facebook.github.io/prophet/docs/quick_start.html#r-api)
 
 ### Windows
 
@@ -44,7 +44,7 @@ $ pip install fbprophet
 
 The major dependency that Prophet has is `pystan`.   PyStan has its own [installation instructions](http://pystan.readthedocs.io/en/latest/installation_beginner.html).
 
-After installation, you can [get started!](https://facebookincubator.github.io/prophet/docs/quick_start.html#python-api)
+After installation, you can [get started!](https://facebook.github.io/prophet/docs/quick_start.html#python-api)
 
 ### Windows
 

docs/README.md

@@ -8,7 +8,7 @@ See [CONTRIBUTING.md](./CONTRIBUTING.md) for details on how to add or modify con
 
 ## Jupyter Notebooks
 
-Most of the `doc` pages are generated from [Jupyter notebooks](http://jupyter.org/) in the [notebooks](https://github.com/facebookincubator/prophet/tree/master/notebooks) directory at the base of the source tree.  Please make changes there and then rebuild the docs:
+Most of the `doc` pages are generated from [Jupyter notebooks](http://jupyter.org/) in the [notebooks](https://github.com/facebook/prophet/tree/master/notebooks) directory at the base of the source tree.  Please make changes there and then rebuild the docs:
 
 ```
 $ cd docs

docs/_config.yml

@@ -20,7 +20,7 @@ baseurl: "/prophet"
 # This was also be set to the right thing automatically for local development
 # https://github.com/blog/2277-what-s-new-in-github-pages-with-jekyll-3-3
 # http://jekyllrb.com/news/2016/10/06/jekyll-3-3-is-here/
-url: "http://facebookincubator.github.io"
+url: "http://facebook.github.io"
 
 # Note: There are new filters in Jekyll 3.3 to help with absolute and relative urls
 # absolute_url
@@ -31,7 +31,7 @@ url: "http://facebookincubator.github.io"
 # https://github.com/blog/2277-what-s-new-in-github-pages-with-jekyll-3-3
 
 # The GitHub repo for your project
-ghrepo: "facebookincubator/prophet"
+ghrepo: "facebook/prophet"
 
 # Use these color settings to determine your colour scheme for the site.
 color:

docs/_data/nav.yml

@@ -3,5 +3,5 @@
   category: docs
 
 - title: GitHub
-  href: https://github.com/facebookincubator/prophet
+  href: https://github.com/facebook/prophet
   category: external

docs/_docs/contributing.md

@@ -5,7 +5,7 @@ title: "How to Contribute"
 permalink: /docs/contributing.html
 ---
 
-Prophet has an non-fixed release cycle but we will be making bugfixes in response to user feedback and adding features.  Its current state is Beta (v0.2), we expect no obvious bugs. Please let us know if you encounter a bug by [filing an issue](https://github.com/facebookincubator/prophet/issues).
+Prophet has an non-fixed release cycle but we will be making bugfixes in response to user feedback and adding features.  Its current state is Beta (v0.2), we expect no obvious bugs. Please let us know if you encounter a bug by [filing an issue](https://github.com/facebook/prophet/issues).
 
 We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.
 
@@ -15,7 +15,7 @@ We require that any API changes or feature additions are made available for both
 
 ## Documentation
 
-Most of the `doc` pages are generated from [Jupyter notebooks](http://jupyter.org/) in the [notebooks](https://github.com/facebookincubator/prophet/tree/master/notebooks) directory at the base of the source tree.  Please make changes there and then rebuild the docs:
+Most of the `doc` pages are generated from [Jupyter notebooks](http://jupyter.org/) in the [notebooks](https://github.com/facebook/prophet/tree/master/notebooks) directory at the base of the source tree.  Please make changes there and then rebuild the docs:
 
 ```
 $ cd docs

docs/_docs/quick_start.md

@@ -10,7 +10,7 @@ Prophet follows the `sklearn` model API.  We create an instance of the `Prophet`
 
 The input to Prophet is always a dataframe with two columns: `ds` and `y`.  The `ds` (datestamp) column must contain a date or datetime (either is fine). The `y` column must be numeric, and represents the measurement we wish to forecast.
 
-As an example, let's look at a time series of daily page views for the Wikipedia page for [Peyton Manning](https://en.wikipedia.org/wiki/Peyton_Manning).  We scraped this data using the [Wikipediatrend](https://cran.r-project.org/web/packages/wikipediatrend/vignettes/using-wikipediatrend.html) package in R.  Peyton Manning provides a nice example because it illustrates some of Prophet's features, like multiple seasonality, changing growth rates, and the ability to model special days (such as Manning's playoff and superbowl appearances). The CSV is available [here](https://github.com/facebookincubator/prophet/blob/master/examples/example_wp_peyton_manning.csv).
+As an example, let's look at a time series of daily page views for the Wikipedia page for [Peyton Manning](https://en.wikipedia.org/wiki/Peyton_Manning).  We scraped this data using the [Wikipediatrend](https://cran.r-project.org/web/packages/wikipediatrend/vignettes/using-wikipediatrend.html) package in R.  Peyton Manning provides a nice example because it illustrates some of Prophet's features, like multiple seasonality, changing growth rates, and the ability to model special days (such as Manning's playoff and superbowl appearances). The CSV is available [here](https://github.com/facebook/prophet/blob/master/examples/example_wp_peyton_manning.csv).
 
 First we'll import the data and log-transform the y variable.
 
@@ -216,7 +216,7 @@ In R, we use the normal model fitting API.  We provide a `prophet` function that
 library(prophet)
 library(dplyr)
 ```
-First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and numeric value respectively. As above, we use here the log number of views to Petyon Manning's Wikipedia page, available [here](https://github.com/facebookincubator/prophet/blob/master/examples/example_wp_peyton_manning.csv).
+First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and numeric value respectively. As above, we use here the log number of views to Petyon Manning's Wikipedia page, available [here](https://github.com/facebook/prophet/blob/master/examples/example_wp_peyton_manning.csv).
 
 ```R
 # R

notebooks/quick_start.ipynb

@@ -32,7 +32,7 @@
    "source": [
     "The input to Prophet is always a dataframe with two columns: `ds` and `y`.  The `ds` (datestamp) column must contain a date or datetime (either is fine). The `y` column must be numeric, and represents the measurement we wish to forecast.\n",
     "\n",
-    "As an example, let's look at a time series of daily page views for the Wikipedia page for [Peyton Manning](https://en.wikipedia.org/wiki/Peyton_Manning).  We scraped this data using the [Wikipediatrend](https://cran.r-project.org/web/packages/wikipediatrend/vignettes/using-wikipediatrend.html) package in R.  Peyton Manning provides a nice example because it illustrates some of Prophet's features, like multiple seasonality, changing growth rates, and the ability to model special days (such as Manning's playoff and superbowl appearances). The CSV is available [here](https://github.com/facebookincubator/prophet/blob/master/examples/example_wp_peyton_manning.csv).\n",
+    "As an example, let's look at a time series of daily page views for the Wikipedia page for [Peyton Manning](https://en.wikipedia.org/wiki/Peyton_Manning).  We scraped this data using the [Wikipediatrend](https://cran.r-project.org/web/packages/wikipediatrend/vignettes/using-wikipediatrend.html) package in R.  Peyton Manning provides a nice example because it illustrates some of Prophet's features, like multiple seasonality, changing growth rates, and the ability to model special days (such as Manning's playoff and superbowl appearances). The CSV is available [here](https://github.com/facebook/prophet/blob/master/examples/example_wp_peyton_manning.csv).\n",
     "\n",
     "First we'll import the data and log-transform the y variable."
    ]
@@ -375,7 +375,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and numeric value respectively. As above, we use here the log number of views to Petyon Manning's Wikipedia page, available [here](https://github.com/facebookincubator/prophet/blob/master/examples/example_wp_peyton_manning.csv)."
+    "First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and numeric value respectively. As above, we use here the log number of views to Petyon Manning's Wikipedia page, available [here](https://github.com/facebook/prophet/blob/master/examples/example_wp_peyton_manning.csv)."
    ]
   },
   {

python/README

@@ -5,14 +5,14 @@ Prophet is a procedure for forecasting time series data.  It is based on an addi
 
 Prophet is `open source software <https://code.facebook.com/projects/>`_ released by Facebook's `Core Data Science team <https://research.fb.com/category/data-science/>`_.
 
-Full documentation and examples available at the homepage: https://facebookincubator.github.io/prophet/
+Full documentation and examples available at the homepage: https://facebook.github.io/prophet/
 
 Important links
 ---------------
 
-- HTML documentation: https://facebookincubator.github.io/prophet/docs/quick_start.html
-- Issue tracker: https://github.com/facebookincubator/prophet/issues
-- Source code repository: https://github.com/facebookincubator/prophet
+- HTML documentation: https://facebook.github.io/prophet/docs/quick_start.html
+- Issue tracker: https://github.com/facebook/prophet/issues
+- Source code repository: https://github.com/facebook/prophet
 - Implementation of Prophet in R: https://cran.r-project.org/package=prophet
 
 

python/setup.py

@@ -99,7 +99,7 @@ setup(
     name='fbprophet',
     version='0.2',
     description='Automatic Forecasting Procedure',
-    url='https://facebookincubator.github.io/prophet/',
+    url='https://facebook.github.io/prophet/',
     author='Sean J. Taylor <sjt@fb.com>, Ben Letham <bletham@fb.com>',
     author_email='sjt@fb.com',
     license='BSD',