statsmodels 0.4.3

0.4.3

The only change compared to 0.4.2 is for compatibility with python 3.2.3 (changed behavior of 2to3).

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