Package: scikits.statsmodels
What it isStatsmodels is a Python package that provides a complement to scipy for
statistical computations including descriptive statistics and
estimation of statistical models.NoteThe name ofscikits.statsmodelshas been changed tostatsmodels,
the new version is athttp://pypi.python.org/pypi/statsmodelsMain Featuresregression: 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 logitrlm: Robust linear models with support for several M-estimators.tsa: Time series analysis models, including ARMA, AR, VARnonparametric : (Univariate) kernel density estimatorsdatasets: Datasets to be distributed and used for examples and in testing.PyDTA: Tools for reading Stata .dta files into numpy arrays.stats: a wide range of statistical testssandbox: 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”.Where to get itDevelopment 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.https://www.github.com/statsmodels/statsmodelsSource download of stable tags will be on SourceForge.https://sourceforge.net/projects/statsmodels/orPyPi:http://pypi.python.org/pypi/scikits.statsmodels/Installation from sourcesIn the top directory, just do:python setup.py installSee INSTALL.txt for requirements orhttp://statsmodels.sourceforge.net/For more information.LicenseSimplified BSDDocumentationThe official documentation is hosted on SourceForge.http://statsmodels.sourceforge.net/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.http://scipystats.blogspot.comWindows HelpThe source distribution for Windows includes a htmlhelp file (statsmodels.chm).
This can be opened from the python interpreter>>> import scikits.statsmodels.api as sm
>>> sm.open_help()Discussion and DevelopmentAll 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 athttps://github.com/statsmodels/statsmodels/issuesThere is also a google group athttp://groups.google.com/group/pystatsmodelsto discuss development and design issues that are deemed to be too specialized
for the scipy-dev/user list.Python 3scikits.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.Release History0.3.1Removed academic-only WFS dataset.Fix easy_install issue on Windows.0.3.0Changes that break backwards compatibilityAdded api.py for importing. So the new convention for importing is:import scikits.statsmodels.api as smImporting 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.pylib/io.py -> iolib/foreign.py (Now contains Stata .dta format reader)family -> familiesfamilies.links.inverse -> families.links.inverse_powerDatasets’ Load class is now load function.regression.py -> regression/linear_model.pydiscretemod.py -> discrete/discrete_model.pyrlm.py -> robust/robust_linear_model.pyglm.py -> genmod/generalized_linear_model.pymodel.py -> base/model.pyt() method -> tvalues attribute (t() still exists but raises a warning)Main changes and additionsNumerous bugfixes.Time Series Analysis model (tsa)Vector Autoregression Models VAR (tsa.VAR)Autogressive Models AR (tsa.AR)Autoregressive Moving Average Models ARMA (tsa.ARMA)
optionally uses Cython for Kalman Filtering
use setup.py install with option –with-cythonBaxter-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 solversRefactor of the datasets sub-package.Added more datasets for examples.Removed RPy dependency for running the test suite.Refactored 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 testsMultiple test correctionGeneral Method of Moment framework in sandboxImproved documentationand other additions0.2.0Main changesrenames for more consistency
RLM.fitted_values -> RLM.fittedvalues
GLMResults.resid_dev -> GLMResults.resid_devianceGLMResults, RegressionResults:
lazy calculations, convert attributes to properties with _cachefix tests to run without rpyexpanded examples in examples directoryadd PyDTA to lib.io – functions for reading Stata .dta binary files
and converting
them to numpy arraysmade tools.categorical much more robustadd_constant now takes a prepend argumentfix GLS to work with only a one column designNewadd four new datasetsA dataset from the American National Election Studies (1996)Grunfeld (1950) investment dataSpector and Mazzeo (1980) program effectiveness dataA US macroeconomic datasetadd four new Maximum Likelihood Estimators for models with a discrete
dependent variables with examplesLogitProbitMNLogit (multinomial logit)PoissonSandboxadd qqplot in sandbox.graphicsadd sandbox.tsa (time series analysis) and sandbox.regression (anova)add principal component analysis in sandbox.toolsadd Seemingly Unrelated Regression (SUR) and Two-Stage Least Squares
for systems of equations in sandbox.sysreg.Sem2SLSadd restricted least squares (RLS)0.1.0b1initial release