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They now have a tool dependency on py-setuptools instead of a DEPENDS
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All checksums have been double-checked against existing RMD160 and
SHA512 hashes
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Many many changes including
Oneway ANOVA-type analysis
~~~~~~~~~~~~~~~~~~~~~~~~~~
Several statistical methods for ANOVA-type analysis of k independent samples
have been added in module :mod:`~statsmodels.stats.oneway`. This includes
standard Anova, Anova for unequal variances (Welch, Brown-Forsythe for mean),
Anova based on trimmed samples (Yuen anova) and equivalence testing using
the method of Wellek.
Anova for equality of variances or dispersion are available for several
transformations. This includes Levene test and Browne-Forsythe test for equal
variances as special cases. It uses the `anova_oneway` function, so unequal
variance and trimming options are also available for tests on variances.
Several functions for effect size measures have been added, that can be used
for reporting or for power and sample size computation.
Multivariate statistics
~~~~~~~~~~~~~~~~~~~~~~~
The new module :mod:`~statsmodels.stats.multivariate` includes one and
two sample tests for multivariate means, Hotelling's t-tests',
:func:`~statsmodels.stats.multivariate.test_mvmean`,
:func:`~statsmodels.stats.multivariate.test_mvmean_2indep` and confidence
intervals for one-sample multivariate mean
:func:`~statsmodels.stats.multivariate.confint_mvmean`
Additionally, hypothesis tests for covariance patterns, and for oneway equality
of covariances are now available in several ``test_cov`` functions.
New exponential smoothing model: ETS (Error, Trend, Seasonal)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- Class implementing ETS models :class:`~statsmodels.tsa.exponential_smoothing.ets.ETSModel`.
- Includes linear and non-linear exponential smoothing models
- Supports parameter fitting, in-sample prediction and out-of-sample
forecasting, prediction intervals, simulation, and more.
- Based on the innovations state space approach.
Forecasting Methods
~~~~~~~~~~~~~~~~~~~
Two popular methods for forecasting time series, forecasting after
STL decomposition (:class:`~statsmodels.tsa.forecasting.stl.STLForecast`)
and the Theta model
(:class:`~statsmodels.tsa.forecasting.theta.ThetaModel`) have been added.
See 0.12.0-0.12.2 at https://www.statsmodels.org/stable/release/
for the full story, including deprecations.
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Install the new interchangeable BLAS system created by Thomas Orgis,
currently supporting Netlib BLAS/LAPACK, OpenBLAS, cblas, lapacke, and
Apple's Accelerate.framework. This system allows the user to select any
BLAS implementation without modifying packages or using package options, by
setting PKGSRC_BLAS_TYPES in mk.conf. See mk/blas.buildlink3.mk for details.
This commit should not alter behavior of existing packages as the system
defaults to Netlib BLAS/LAPACK, which until now has been the only supported
implementation.
Details:
Add new mk/blas.buildlink3.mk for inclusion in dependent packages
Install compatible Netlib math/blas and math/lapack packages
Update math/blas and math/lapack MAINTAINER approved by adam@
OpenBLAS, cblas, and lapacke will follow in separate commits
Update direct dependents to use mk/blas.buildlink3.mk
Perform recursive revbump
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Major Features:
- Allow fixing parameters in state space models
- Add new version of ARIMA-type estimators (AR, ARIMA, SARIMAX)
- Add STL decomposition for time series
- Functional SIR
- Zivot Andrews test
- Added Oaxaca-Blinder Decomposition
- Add rolling WLS and OLS
- Replacement for AR
Performance Improvements:
- Cythonize innovations algo and filter
- Only perform required predict iterations in state space models
- State space: Improve low memory usability; allow in fit, loglike
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This is a major release from 0.9.0 and includes a number new
statistical models and many bug fixes.
Highlights include:
* Generalized Additive Models. This major feature is experimental and
may change.
* Conditional Models such as ConditionalLogit, which are known as
fixed effect models in Econometrics.
* Dimension Reduction Methods include Sliced Inverse Regression,
Principal Hessian Directions and Sliced Avg. Variance Estimation
* Regression using Quadratic Inference Functions (QIF)
* Gaussian Process Regression
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Remove some .so files from the PLIST that are not built for me
nor in mef's 9.0 bulk build.
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0.9.0:
The Highlights
--------------
statespace refactoring, Markov Switching Kim smoother
3 Google summer of code (GSOC) projects merged - distributed estimation - VECM and enhancements to VAR (including cointegration test) - new count models: GeneralizedPoisson, zero inflated models
Bayesian mixed GLM
Gaussian Imputation
new multivariate methods: factor analysis, MANOVA, repeated measures within ANOVA
GLM var_weights in addition to freq_weights
Holt-Winters and Exponential Smoothing
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The main features of this release are several new time series models based on the statespace framework, multiple imputation using MICE as well as many other enhancements. The codebase also has been updated to be compatible with recent numpy and pandas releases.
Statsmodels is using now github to store the updated documentation which is available under http://www.statsmodels.org/stable for the last release, and http://www.statsmodels.org/dev/ for the development version.
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Packaged for wip by Kamel Ibn Aziz Derouiche and myself.
Statsmodels is a Python package that provides a complement to scipy
for statistical computations including descriptive statistics and
estimation and inference for statistical models
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