Metadata-Version: 1.1
Name: statsmodels
Version: 0.4.3
Summary: Statistical computations and models for use with SciPy
Home-page: http://statsmodels.sourceforge.net/
Author: Skipper Seabold, Josef Perktold
Author-email: pystatsmodels@googlegroups.com
License: BSD License
Description: 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
        ============
        We are providing a Windows htmlhelp file (statsmodels.chm) that is now separately
        distributed. It can be copied or moved to the installation directory of
        statsmodels (site-packages\statsmodels in a typical installation), and can then
        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
        
        
        Release History
        ===============
        
        0.4.2
        -----
        
        This is a bug-fix release that affects mainly Big-Endian machines.
        
        *Bug Fixes*
        
        * discrete_model.MNLogit: fix summary method
        * examples in documentation: correct file path
        * tsa.filters.hp_filter: don't use umfpack on Big-Endian machine (scipy bug)
        * the remaining fixes are in the test suite, either precision problems
          on some machines or incorrect testing on Big-Endian machines.
        
        
        
        0.4.1
        -----
        
        This is a backwards compatible (according to our test suite) release with
        bug fixes and code cleanup.
        
        *Bug Fixes*
        
        * build and distribution fixes
        * lowess correct distance calculation
        * genmod correction CDFlink derivative
        * adfuller _autolag correct calculation of optimal lag
        * het_arch, het_lm : fix autolag and store options
        * GLSAR: incorrect whitening for lag>1
        
        *Other Changes*
        
        * add lowess and other functions to api and documentation
        * rename lowess module (old import path will be removed at next release)
        * new robust sandwich covariance estimators, moved out of sandbox
        * compatibility with pandas 0.8
        * new plots in statsmodels.graphics
          - ABLine plot
          - interaction plot
        
        
        0.4.0
        -----
        
        *Main Changes and Additions*
        
        * Added pandas dependency.
        * Cython source is built automatically if cython and compiler are present
        * Support use of dates in timeseries models
        * Improved plots
          - Violin plots
          - Bean Plots
          - QQ Plots
        * Added lowess function
        * Support for pandas Series and DataFrame objects. Results instances return
          pandas objects if the models are fit using pandas objects.
        * Full Python 3 compatibility
        * Fix bugs in genfromdta. Convert Stata .dta format to structured array 
          preserving all types. Conversion is much faster now.
        * Improved documentation
        * Models and results are pickleable via save/load, optionally saving the model 
          data.
        * Kernel Density Estimation now uses Cython and is considerably faster.
        * Diagnostics for outlier and influence statistics in OLS
        * Added El Nino Sea Surface Temperatures dataset
        * Numerous bug fixes
        * Internal code refactoring
        * Improved documentation including examples as part of HTML
        
        *Changes that break backwards compatibility*
        
        * Deprecated scikits namespace. The recommended import is now::
        
              import statsmodels.api as sm
        
        * model.predict methods signature is now (params, exog, ...) where before
          it assumed that the model had been fit and omitted the params argument.
        * For consistency with other multi-equation models, the parameters of MNLogit
          are now transposed.
        * tools.tools.ECDF -> distributions.ECDF
        * tools.tools.monotone_fn_inverter -> distributions.monotone_fn_inverter
        * tools.tools.StepFunction -> distributions.StepFunction
        
        
        0.3.1
        -----
        
        * Removed academic-only WFS dataset.
        * Fix easy_install issue on Windows.
        
        0.3.0
        -----
        
        *Changes that break backwards compatibility*
        
        Added api.py for importing. So the new convention for importing is::
        
            import 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)
        * family -> families
        * families.links.inverse -> families.links.inverse_power
        * 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
        * model.py -> base/model.py
        * t() method -> tvalues attribute (t() still exists but raises a warning)
        
        *Main changes and additions*
        
        * Numerous 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-cython
          - 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.
        * 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 tests
        * Multiple test correction
        * General Method of Moment framework in sandbox
        * Improved documentation
        * and other additions
        
        
        0.2.0
        -----
        
        *Main changes*
        
         * 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
         * fix tests to run without rpy
         * expanded examples in examples directory
         * add PyDTA to lib.io -- functions for reading Stata .dta binary files
           and converting
           them to numpy arrays
         * made tools.categorical much more robust
         * add_constant now takes a prepend argument
         * fix GLS to work with only a one column design
        
        *New*
        
         * add four new datasets
        
           - A dataset from the American National Election Studies (1996)
           - Grunfeld (1950) investment data
           - Spector and Mazzeo (1980) program effectiveness data
           - A US macroeconomic dataset
         * add four new Maximum Likelihood Estimators for models with a discrete
           dependent variables with examples
        
           - Logit
           - Probit
           - MNLogit (multinomial logit)
           - Poisson
        
        *Sandbox*
        
         * add qqplot in sandbox.graphics
         * 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
         * add restricted least squares (RLS)
        
        
        0.1.0b1
        -------
         * initial release
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Programming Language :: Python :: 2.5
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.2
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Scientific/Engineering
