:orphan: .. _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 ----- * 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