upgrade to pymc3 Beta!

Author: jsalvatier

readme.md

@@ -1,52 +1,53 @@
-# PyMC 3
+# PyMC3
 [![Gitter](https://badges.gitter.im/Join Chat.svg)](https://gitter.im/pymc-devs/pymc?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
 [![Build Status](https://travis-ci.org/pymc-devs/pymc3.png?branch=master)](https://travis-ci.org/pymc-devs/pymc3)
 
-PyMC is a python module for Bayesian statistical modeling and model fitting which focuses on advanced Markov chain Monte Carlo fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.
+PyMC3 is a python module for Bayesian statistical modeling and model fitting which focuses on advanced Markov chain Monte Carlo fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.
 
 Check out the [Tutorial](http://pymc-devs.github.io/pymc3/getting_started/)!
 
-***PyMC 3 is alpha software*** and is not ready for use in production. We encourage most new users to use the current release version in the [PyMC 2 repository](https://github.com/pymc-devs/pymc).
+PyMC3 is Beta software. Users should consider using [PyMC 2 repository](https://github.com/pymc-devs/pymc).
 
 ## Features
 
  * Intuitive model specification syntax, for example, `x ~ N(0,1)` translates to `x = Normal(0,1)`
- * Powerful sampling algorithms such as [Hamiltonian Monte Carlo](http://en.wikipedia.org/wiki/Hybrid_Monte_Carlo)
- * Easy optimization for finding the *maximum a posteriori* point
+ * Powerful sampling algorithms such as the [No U-Turn Sampler] allow complex models with thousands of parameters with little specialized knowledge of fitting algorithms.
+ * Easy optimization for finding the *maximum a posteriori*(MAP) point
  * [Theano](http://deeplearning.net/software/theano/) features
   * Numpy broadcasting and advanced indexing
   * Linear algebra operators
   * Computation optimization and dynamic C compilation
  * Simple extensibility
+ * Transparent support for missing value imputation
 
 ## Getting started
- * [PyMC 3 Tutorial](http://pymc-devs.github.io/pymc3/getting_started/)
+ * [PyMC3 Tutorial](http://pymc-devs.github.io/pymc3/getting_started/)
  * Coal Mining Disasters model in [PyMC 2](https://github.com/pymc-devs/pymc/blob/master/pymc/examples/disaster_model.py) and [PyMC 3](https://github.com/pymc-devs/pymc3/blob/master/pymc3/examples/disaster_model.py)
  * [Global Health Metrics & Evaluation model](http://nbviewer.ipython.org/urls/raw.github.com/pymc-devs/pymc3/master/pymc3/examples/GHME%202013.ipynb) case study for GHME 2013
  * [Stochastic Volatility model](http://nbviewer.ipython.org/urls/raw.github.com/pymc-devs/pymc3/master/pymc3/examples/stochastic_volatility.ipynb)
  * [Several blog posts on linear regression](http://twiecki.github.io/tag/bayesian-statistics.html)
  * [Talk at PyData NYC 2013 on PyMC3](http://twiecki.github.io/blog/2013/12/12/bayesian-data-analysis-pymc3/)
  * [PyMC3 port of the models presented in the book "Doing Bayesian Data Analysis" by John Kruschke](https://github.com/aloctavodia/Doing_bayesian_data_analysis)
- * [The PyMC examples folder](https://github.com/pymc-devs/pymc3/tree/master/pymc3/examples)
+ * [The PyMC3 examples folder](https://github.com/pymc-devs/pymc3/tree/master/pymc3/examples)
 
 ## Installation
 
-The latest version of PyMC 3 can be installed from the master branch using pip:
+The latest version of PyMC3 can be installed from the master branch using pip:
 
 ```
 pip install --process-dependency-links git+https://github.com/pymc-devs/pymc3
 ```
 
-The `--process-dependency-links` flag ensures that the developmental branch of Theano, which PyMC requires, is installed. If a recent developmental version of Theano has been installed with another method, this flag can be dropped.
+The `--process-dependency-links` flag ensures that the developmental branch of Theano, which PyMC3 requires, is installed. If a recent developmental version of Theano has been installed with another method, this flag can be dropped.
 
-Another option is to clone the repository and install PyMC using `python setup.py install` or `python setup.py develop`.
+Another option is to clone the repository and install PyMC3 using `python setup.py install` or `python setup.py develop`.
 
-**Note:** Running `pip install pymc` will install PyMC 2.3, not PyMC 3, from PyPI.
+**Note:** Running `pip install pymc` will install PyMC 2.3, not PyMC3, from PyPI.
 
 ## Dependencies
 
-PyMC is tested on Python 2.7 and 3.3 and depends on Theano, NumPy,
+PyMC3 is tested on Python 2.7 and 3.3 and depends on Theano, NumPy,
 SciPy, Pandas, and Matplotlib (see setup.py for version information).
 
 ### Optional

release-notes.md

@@ -0,0 +1,41 @@
+# Release Notes
+
+## PyMC3 3.0b (June 16th, 2015)
+
+Probabilistic programming allows for flexible specification of Bayesian statistical models in code. PyMC3 is a new, open-source probabilistic programmer framework with an intuitive, readable and concise, yet powerful, syntax that is close to the natural notation statisticians use to describe models. It features next-generation fitting techniques, such as the No U-Turn Sampler, that allow fitting complex models with thousands of parameters without specialized knowledge of fitting algorithms.
+
+PyMC3 has recently seen rapid development. With the addition of two new major features: automatic transforms and missing value imputation, PyMC3 has become ready for wider use. PyMC3 is now refined enough that adding features is easy, so we don't expect adding features in the future will require drastic changes. It has also become user friendly enough for a broader audience. Automatic transformations mean NUTS and find_MAP work with less effort, and friendly error messages mean its easy to diagnose problems with your model.
+
+Thus, Thomas, Chris and I are pleased to announce that PyMC3 is now in Beta.
+
+### Highlights
+* Transforms now automatically applied to constrained distributions
+* Transforms now specified with a `transform=` argument on Distributions. `model.TransformedVar` is gone.
+* Transparent missing value imputation support added with MaskedArrays or pandas.DataFrame NaNs.
+* Bad default values now ignored
+* Profile theano functions using `model.profile(model.logpt)`
+
+### Contributors since 3.0a
+* A. Flaxman <[email protected]>
+* Andrea Zonca <[email protected]>
+* Andreas Klostermann <[email protected]>
+* Andrew Clegg <[email protected]>
+* AustinRochford <[email protected]>
+* Benjamin Edwards <[email protected]>
+* Brian Naughton <[email protected]>
+* Chad Heyne <[email protected]>
+* Chris Fonnesbeck <[email protected]>
+* Corey Farwell <[email protected]>
+* John Salvatier <[email protected]>
+* Karlson Pfannschmidt <[email protected]>
+* Kyle Bishop <[email protected]>
+* Kyle Meyer <[email protected]>
+* Mack Sweeney <[email protected]>
+* Osvaldo Martin <[email protected]>
+* Raymond Roberts <[email protected]>
+* Rodrigo Benenson <[email protected]>
+* Thomas Wiecki <[email protected]>
+* Zach Ploskey <[email protected]>
+* maahnman <[email protected]>
+* paul sorenson <[email protected]>
+* zenourn <[email protected]>

setup.py

@@ -14,7 +14,7 @@
 LICENSE = "Apache License, Version 2.0"
 VERSION = "3.0"
 
-classifiers = ['Development Status :: 3 - Alpha',
+classifiers = ['Development Status :: 4 - Beta',
                'Programming Language :: Python',
                'Programming Language :: Python :: 2',
                'Programming Language :: Python :: 3',