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¶
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