DOC: Improve appearance of README

Add additional badges and fix rst errors [skip ci]
norov · Jun 21, 2016 · ae46551 · ae46551
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diff --git a/README.rst b/README.rst
@@ -1,64 +1,99 @@
-.. image:: https://travis-ci.org/statsmodels/statsmodels.svg?branch=master
-    :target: https://travis-ci.org/statsmodels/statsmodels
+|Travis Build Status| |Appveyor Build Status| |Coveralls Coverage|
 
-What Statsmodels is
-===================
+About Statsmodels
+=================
 
-Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models.
+Statsmodels is a Python package that provides a complement to scipy for
+statistical computations including descriptive statistics and estimation
+and inference for statistical models.
 
 
-Change in Location of Documentation
-===================================
-
-Due to the current outage of our online documentation, we recreated the
-documentation on GitHub.
+Documentation
+=============
 
 The documentation for the latest release is at
 
-   http://statsmodels.github.io/stable/index.html
+   http://www.statsmodels.org/stable/
 
 The documentation for the development version is at
 
-   http://statsmodels.github.io/dev/index.html
+   http://www.statsmodels.org/dev/
+
+Recent improvements are highlighted in the release notes
 
+   http://www.statsmodels.org/stable/release/version0.8.html
+
+Backups of documentation are available at http://statsmodels.github.io/stable/
+and http://statsmodels.github.io/dev/.
 
-This is currently a temporary solution that will be transformed into the new permanent location.
 
 
 Main Features
 =============
 
-* linear regression models: Generalized least squares (including weighted least squares and
-  least squares with autoregressive errors), ordinary least squares.
-* glm: Generalized linear models with support for all of the one-parameter
-  exponential family distributions.
-* discrete: regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators
-* rlm: Robust linear models with support for several M-estimators.
-* tsa: models for time series analysis
-  - univariate time series analysis: AR, ARIMA
-  - vector autoregressive models, VAR and structural VAR
-  - descriptive statistics and process models for time series analysis
-* nonparametric : (Univariate) kernel density estimators
-* datasets: Datasets to be distributed and used for examples and in testing.
-* stats: a wide range of statistical tests
+* Linear regression models:
+
+  - Ordinary least squares
+  - Generalized least squares
+  - Weighted least squares
+  - Least squares with autoregressive errors
+  - Quantile regression
+
+* Mixed Linear Model with mixed effects and variance components
+* GLM: Generalized linear models with support for all of the one-parameter
+  exponential family distributions
+* GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
+* Discrete models:
+
+  - Logit and Probit
+  - Multinomial logit (MNLogit)
+  - Poisson regresion
+  - Negative Binomial regression
+
+* RLM: Robust linear models with support for several M-estimators.
+* Time Series Analysis: models for time series analysis
+
+  - Complete StateSpace modeling framework
+
+    - Seasonal ARIMA and ARIMAX models
+    - VARMA and VARMAX models
+    - Dynamic Factor models
+
+  - Markov switching models (MSAR), also known as Hidden Markov Models (HMM)
+  - Univariate time series analysis: AR, ARIMA
+  - Vector autoregressive models, VAR and structural VAR
+  - Hypothesis tests for time series: unit root, cointegration and others
+  - Descriptive statistics and process models for time series analysis
+
+* Nonparametric statistics: (Univariate) kernel density estimators
+* Datasets: Datasets used for examples and in testing
+* Statistics: a wide range of statistical tests
+
   - diagnostics and specification tests
   - goodness-of-fit and normality tests
   - functions for multiple testing
   - various additional statistical tests
-* iolib
+
+* Imputation with MICE and regression on order statistic
+* Principal Component Analysis with missing data
+* I/O
+
   - Tools for reading Stata .dta files into numpy arrays.
-  - printing table output to ascii, latex, and html
-* miscellaneous models
-* sandbox: statsmodels contains a sandbox folder with code in various stages of
-  developement and testing which is not considered "production ready".
-  This covers among others Mixed (repeated measures) Models, GARCH models, general method
-  of moments (GMM) estimators, kernel regression, various extensions to scipy.stats.distributions,
-  panel data models, generalized additive models and information theoretic measures.
+  - Table output to ascii, latex, and html
 
+* Miscellaneous models
+* Sandbox: statsmodels contains a sandbox folder with code in various stages of
+  developement and testing which is not considered "production ready".   This covers
+  among others
 
-Where to get it
-===============
+  - Generalized method of moments (GMM) estimators
+  - Kernel regression
+  - Various extensions to scipy.stats.distributions
+  - Panel data models
+  - Information theoretic measures
 
+How to get it
+=============
 The master branch on GitHub is the most up to date code
 
     https://www.github.com/statsmodels/statsmodels
@@ -75,55 +110,42 @@ Binaries can be installed in Anaconda
 
     conda install statsmodels
 
-Development snapshots are also avaiable in Anaconda
+Development snapshots are also available in Anaconda (infrequently updated)
 
     conda install -c https://conda.binstar.org/statsmodels statsmodels
 
-
-Installation from sources
-=========================
+Installing from sources
+=======================
 
 See INSTALL.txt for requirements or see the documentation
 
-    http://statsmodels.sf.net/devel/install.html
-
+    http://statsmodels.github.io/dev/install.html
 
 License
 =======
 
 Modified BSD (3-clause)
 
-
-Documentation
-=============
-
-The official documentation is hosted on SourceForge
-
-    http://statsmodels.sf.net/
-
-
-Windows Help
-============
-The source distribution for Windows includes a htmlhelp file (statsmodels.chm).
-This can be opened from the python interpreter ::
-
-    >>> import statsmodels.api as sm
-    >>> sm.open_help()
-
-
 Discussion and Development
 ==========================
 
 Discussions take place on our mailing list.
 
     http://groups.google.com/group/pystatsmodels
 
-We are very interested in feedback about usability and suggestions for improvements.
-
+We are very interested in feedback about usability and suggestions for
+improvements.
 
 Bug Reports
 ===========
 
 Bug reports can be submitted to the issue tracker at
 
     https://github.com/statsmodels/statsmodels/issues
+
+.. |Travis Build Status| image:: https://travis-ci.org/statsmodels/statsmodels.svg?branch=master
+   :target: https://travis-ci.org/statsmodels/statsmodels
+.. |Appveyor Build Status| image:: https://ci.appveyor.com/api/projects/status/gx18sd2wc63mfcuc/branch/master?svg=true
+   :target: https://ci.appveyor.com/project/josef-pkt/statsmodels/branch/master
+.. |Coveralls Coverage| image:: https://coveralls.io/repos/github/statsmodels/statsmodels/badge.svg?branch=master
+   :target: https://coveralls.io/github/statsmodels/statsmodels?branch=master