Skip to content


Subversion checkout URL

You can clone with HTTPS or Subversion.

Download ZIP
Fetching contributors…

Cannot retrieve contributors at this time

238 lines (178 sloc) 6.816 kb
Release History
*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
The only change compared to 0.4.2 is for compatibility with python 3.2.3
(changed behavior of 2to3)
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.
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
- ABLine plot
- interaction plot
*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
* 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.
* -> distributions.ECDF
* -> distributions.monotone_fn_inverter
* -> distributions.StepFunction
* Removed academic-only WFS dataset.
* Fix easy_install issue on Windows.
*Changes that break backwards compatibility*
Added 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/ -> iolib/
* lib/ -> iolib/ (Now contains Stata .dta format reader)
* family -> families
* families.links.inverse -> families.links.inverse_power
* Datasets' Load class is now load function.
* -> regression/
* -> discrete/
* -> robust/
* -> genmod/
* -> base/
* 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 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 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*
* 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 -- 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
* 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
* add qqplot in
* add sandbox.tsa (time series analysis) and sandbox.regression (anova)
* add principal component analysis in
* add Seemingly Unrelated Regression (SUR) and Two-Stage Least Squares
for systems of equations in sandbox.sysreg.Sem2SLS
* add restricted least squares (RLS)
* initial release
Jump to Line
Something went wrong with that request. Please try again.