./math/py-statsmodels, Statistical computations and models for Python

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Branch: CURRENT, Version: 0.12.2, Package name: py38-statsmodels-0.12.2, Maintainer: jihbed.research

statsmodels is a Python module that provides classes and functions for
the estimation of many different statistical models, as well as for
conducting statistical tests, and statistical data exploration. An
extensive list of result statistics are available for each
estimator. The results are tested against existing statistical
packages to ensure that they are correct.

Required to run:
[devel/py-setuptools] [math/py-scipy] [math/py-numpy] [math/py-pandas] [math/py-patsy] [lang/python37]

Required to build:
[devel/py-cython] [pkgtools/cwrappers]

Master sites:

SHA1: 3a653a3fbfe9b3c9083193ded85f4875bc9d5b05
RMD160: d8049f589996c1a4f9c443d2303cb466e013c033
Filesize: 17060.623 KB

Version history: (Expand)

CVS history: (Expand)

   2021-04-06 14:16:47 by Patrick Welche | Files touched by this commit (3) | Package updated
Log message:
Update py-statsmodels to 0.12.2

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_2indep` and confidence
intervals for one-sample multivariate mean
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 \ 
- 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.
   2020-10-12 23:52:05 by Jason Bacon | Files touched by this commit (87) | Package updated
Log message:
math/blas, math/lapack: Install interchangeable BLAS system

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


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
   2020-05-03 18:13:11 by Min Sik Kim | Files touched by this commit (3) | Package updated
Log message:
math/py-statsmodels: Update to 0.11.1

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
   2020-01-08 02:23:22 by Min Sik Kim | Files touched by this commit (4) | Package updated
Log message:
math/py-statsmodels: Update to 0.10.2

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
   2019-09-27 11:00:38 by Thomas Klausner | Files touched by this commit (2) | Package updated
Log message:
py-statsmodels: update to 0.9.0nb2.

Remove some .so files from the PLIST that are not built for me
nor in mef's 9.0 bulk build.
   2019-06-17 07:29:43 by Adam Ciarcinski | Files touched by this commit (1)
Log message:
py-statsmodels: fix for newer SciPy
   2018-07-05 15:09:11 by Adam Ciarcinski | Files touched by this commit (3) | Package updated
Log message:
py-statsmodels: updated to 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
   2017-05-21 11:07:37 by Adam Ciarcinski | Files touched by this commit (3) | Package updated
Log message:
Release 0.8.0

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.