scikits.statsmodels 0.3.0rc1

Main changes for 0.3.0

Changes that break backwards compatibility

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

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)

• 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.baxter_king)

  • 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

Main Changes in 0.2.0

• Improved documentation and expanded and more examples

• Added four discrete choice models: Poisson, Probit, Logit, and Multinomial Logit.

• Added PyDTA. Tools for reading Stata binary datasets (*.dta) and putting them into numpy arrays.

• Added four new datasets for examples and tests.

• Results classes have been refactored to use lazy evaluation.

• Improved support for maximum likelihood estimation.

• bugfixes

• renames for more consistency

  • RLM.fitted_values -> RLM.fittedvalues

  • GLMResults.resid_dev -> GLMResults.resid_deviance

Python 3

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.

Sandbox

We are continuing to work on support for systems of equations models, panel data models, time series analysis, and information and entropy econometrics in the sandbox. This code is often merged into trunk as it becomes more robust.

Windows Help

The 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()