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Statsmodels: statistical modeling and econometrics in Python
Python AGS Script HTML Stata R C Other
add-tokyo dcm_rebased desc-stats maintenance/0.3.x maintenance/0.4.x-defunct maintenance/0.4.x maintenance/0.5.x maintenance/0.6.x maintenance/0.7.x master misc_io nonlinearls survival-rebased
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What it is ========== Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. 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 - diagnostics and specification tests - goodness-of-fit and normality tests - functions for multiple testing - various additional statistical tests * iolib - 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. Where to get it =============== The master branch on GitHub is the most up to date code https://www.github.com/statsmodels/statsmodels Source download of release tags are available on GitHub https://github.com/statsmodels/statsmodels/tags Binaries and source distributions are available from PyPi http://pypi.python.org/pypi/statsmodels/ Installation from sources ========================= See INSTALL.txt for requirements or see the documentation http://statsmodels.sf.net/devel/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. Bug Reports =========== Bug reports can be submitted to the issue tracker at https://github.com/statsmodels/statsmodels/issues