diff --git a/README.rst b/README.rst index d02b112ad..59ab36e64 100644 --- 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 @@ -75,42 +110,22 @@ 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 ========================== @@ -118,8 +133,8 @@ 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 =========== @@ -127,3 +142,10 @@ 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