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.. _old_changes:
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 do not 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
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* 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)
- Autoregressive 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