scikitsstatsmodels

Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation of statistical models.

regression: 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 choice models: Poisson, probit, logit, multinomial logit

rlm: Robust linear models with support for several M-estimators.

tsa: Time series analysis models, including ARMA, AR, VAR

nonparametric : (Univariate) kernel density estimators

datasets: Datasets to be distributed and used for examples and in testing.

PyDTA: Tools for reading Stata*.dta files into numpy arrays.

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stats: a wide range of statistical tests

sandbox : There is also a sandbox which contains code for generalized additive models (untested), mixed effects models, cox proportional hazards model (both are untested and still dependent on the nipy formula framework), generating descriptive statistics, and printing table output to ascii, latex, and html. There is also experimental code for systems of equations regression, time series models, panel data estimators and information theoretic measures. None of this code is considered production ready.

Development branches will be on Github. This is where to go to get the most up to date code in the trunk branch. Experimental code is hosted here in branches and in developer forks. This code is merged to master often. We try to make sure that the master branch is always stable.

Source download of stable tags will be on SourceForge.

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The official documentation is hosted on SourceForge.

The sphinx docs are currently undergoing a lot of work. They are not yet comprehensive, but should get you started.

Our blog will continue to be updated as we make progress on the code.

The source distribution for Windows includes a htmlhelp file (statsmodels.chm). This can be opened from the python interpreter

All chatter will take place on the or scipy-user mailing list. We are very interested in receiving feedback about usability, suggestions for improvements, and bug reports via the mailing list or the bug tracker at

to discuss development and design issues that are deemed to be too specialized for the scipy-dev/user list.

scikits.statsmodels has been ported and tested for Python 3.2. Python 3 version of the code can be obtained by running 2to3.py over the entire statsmodels source. The numerical core of statsmodels worked almost without changes, however there can be problems with data input and plotting. The STATA file reader and writer in iolib.foreign has not been ported yet. And there are still some problems with the matplotlib version for Python 3 that was used in testing. Running the test suite with Python 3.2 shows some errors related to foreign and matplotlib.

Changes that break backwards compatibility

Added api.py for importing. So the new convention for importing is

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import scikits.statsmodels.api as sm

Importing from modules directly now avoids unnecessary imports and increases the import speed if a library or user only needs specific functions.

sandbox/output.py – iolib/table.py

lib/io.py – iolib/foreign.py (Now contains Stata .dta format reader)

Datasets Load class is now load function.

regression.py – regression/linear_model.py

discretemod.py – discrete/discrete_model.py

rlm.py – robust/robust_linear_model.py

glm.py – genmod/generalized_linear_model.py

t() method – tvalues attribute (t() still exists but raises a warning)

Time Series Analysis model (tsa) – Vector Autoregression Models VAR (tsa.VAR) – Autogressive Models AR (tsa.AR) – Autoregressive Moving Average Models ARMA (tsa.ARMA) :

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optionally uses Cython for Kalman Filtering use setup.py install with option –with-cython

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Baxter-King band-pass filter (tsa.filters.bkfilter)

Hodrick-Prescott filter (tsa.filters.hpfilter)

Christiano-Fitzgerald filter (tsa.filters.cffilter)

Improved maximum likelihood framework uses all available scipy.optimize solvers

Refactor of the datasets sub-package.

Removed RPy dependency for running the test suite.

Refactored codebase/directory structure.

Support for offset and exposure in GLM.

Removed data_weights argument to GLM.fit for Binomial models.

New statistical tests, especially diagnostic and specification tests

General Method of Moment framework in sandbox

renames for more consistency RLM.fitted_values – RLM.fittedvalues GLMResults.resid_dev – GLMResults.resid_deviance

GLMResults, RegressionResults: lazy calculations, convert attributes to properties with _cache

expanded examples in examples directory

add PyDTA to lib.io — functions for reading Stata*.dta binary files and converting them to numpy arrays

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made tools.categorical much more robust

add_constant now takes a prepend argument

fix GLS to work with only a one column design

add four new Maximum Likelihood Estimators for models with a discrete dependent variables with examples

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add sandbox.tsa (time series analysis) and sandbox.regression (anova)

add principal component analysis in sandbox.tools

add Seemingly Unrelated Regression (SUR) and Two-Stage Least Squares for systems of equations in sandbox.sysreg.Sem2SLS

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