Pre 0.5.0 Release History

0.5.0

Main Changes and Additions * Add patsy dependency

Compatibility and Deprecation

  • cleanup of import paths (lowess)

Bug Fixes

  • input shapes of tools.isestimable

Enhancements and Additions

  • formula integration based on patsy (new dependency)
  • Time series analysis - ARIMA modeling - enhanced forecasting based on pandas datetime handling
  • expanded margins for discrete models
  • OLS outlier test
  • empirical likelihood - Google Summer of Code 2012 project - inference for descriptive statistics - inference for regression models - accelerated failure time models
  • expanded probability plots
  • improved graphics - plotcorr - acf and pacf
  • new datasets
  • new and improved tools - numdiff numerical differentiation

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
  • 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