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author adam; state Exp; branches; next 1.1; commitid BFNLJcca8w7HIeSz; 1.1 date 2016.07.15.07.35.50; author wiz; state Exp; branches; next ; commitid Ggmjfb9tjM83boez; desc @@ 1.31 log @py-statsmodels: updated to 0.14.6 0.14.6 This release fixes an import issue when using NumPy 2.4 or later or pandas 3 or later. It also fixes some small future issues and ensures that the test suit passes against recent releases of upstream projects. @ text @# $NetBSD: Makefile,v 1.30 2025/10/09 07:57:49 wiz Exp $ DISTNAME= statsmodels-0.14.6 PKGNAME= ${PYPKGPREFIX}-${DISTNAME} CATEGORIES= math python MASTER_SITES= ${MASTER_SITE_PYPI:=s/statsmodels/} MAINTAINER= jihbed.research@@gmail.com HOMEPAGE= https://www.statsmodels.org/ COMMENT= Statistical computations and models for Python LICENSE= modified-bsd TOOL_DEPENDS+= ${PYPKGPREFIX}-cython>=0.29.33:../../devel/py-cython TOOL_DEPENDS+= ${PYPKGPREFIX}-setuptools>=78:../../devel/py-setuptools TOOL_DEPENDS+= ${PYPKGPREFIX}-setuptools_scm>=8:../../devel/py-setuptools_scm DEPENDS+= ${PYPKGPREFIX}-packaging>=21.3:../../devel/py-packaging DEPENDS+= ${PYPKGPREFIX}-pandas>=2.1.1:../../math/py-pandas DEPENDS+= ${PYPKGPREFIX}-patsy>=0.5.6:../../math/py-patsy DEPENDS+= ${PYPKGPREFIX}-scipy>=1.13.0:../../math/py-scipy TEST_DEPENDS+= ${PYPKGPREFIX}-test-randomly-[0-9]*:../../devel/py-test-randomly TEST_DEPENDS+= ${PYPKGPREFIX}-test-xdist-[0-9]*:../../devel/py-test-xdist PYTHON_VERSIONS_INCOMPATIBLE= 310 .include "../../lang/python/wheel.mk" BUILDLINK_API_DEPENDS.py-numpy+= ${PYPKGPREFIX}-numpy>=1.18 .include "../../math/py-numpy/buildlink3.mk" .include "../../mk/bsd.pkg.mk" @ 1.30 log @*: remove reference to (removed) Python 3.9 @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.29 2025/07/13 16:41:50 adam Exp $ d3 1 a3 1 DISTNAME= statsmodels-0.14.5 @ 1.29 log @py-statsmodels: updated to 0.14.5 0.14.5 This release fixes an import issue when using SciPy 1.16 or later. It also fixes some small future issues and ensures that the test suit passes against recent releases of upstream projects. @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.28 2025/07/03 19:18:10 wiz Exp $ d23 1 a23 1 PYTHON_VERSIONS_INCOMPATIBLE= 39 310 @ 1.28 log @*: py-numpy needs Python >= 3.11 now @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.27 2025/04/15 16:31:39 adam Exp $ d3 1 a3 1 DISTNAME= statsmodels-0.14.4 a4 1 PKGREVISION= 1 @ 1.27 log @Fix PLIST after py-setuptools update; bump depends and revision @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.26 2025/01/05 11:10:48 adam Exp $ d24 1 a24 1 PYTHON_VERSIONS_INCOMPATIBLE= 39 @ 1.26 log @py-statsmodels: updated to 0.14.4 0.14.4 This release bring official Pyodide support to a statsmodel release. It is otherwise identical to the previous release. @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.25 2024/11/11 07:28:43 wiz Exp $ d5 1 d15 1 a15 1 TOOL_DEPENDS+= ${PYPKGPREFIX}-setuptools>=69.0.2:../../devel/py-setuptools @ 1.25 log @py-*: remove unused tool dependency py-setuptools includes the py-wheel functionality nowadays @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.24 2024/10/14 06:45:52 wiz Exp $ d3 1 a3 1 DISTNAME= statsmodels-0.14.3 @ 1.24 log @*: clean-up after python38 removal @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.23 2024/09/16 19:25:59 adam Exp $ a15 1 TOOL_DEPENDS+= ${PYPKGPREFIX}-wheel>=0:../../devel/py-wheel @ 1.23 log @py-statsmodels: updated to 0.14.3 0.14.3 This release if a packaging and modernization release. It solves two key issues: 1. Corrects the build procedure for MacOS on both x86_64 and arm64 2. Improves compatibility with recent pandas releases This release is NumPy 2.0 compatible. NumPy 2.0 is only available for Python 3.9+. This means that the minimum Python has been increased to 3.9 to match. NumPy 2 is only required to build statsmodels, and statsmodels will continue to run on NumPy 1.22.3+. Note that when running using NumPy 2, all dependencies that use build against NumPy (e.g., Scipy and pandas) must be NumPy 2 compatible. You can continue to run against NumPy 1.22 - 1.26 along with other components of the scientific Python stack until all required dependencies have been updated. @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.22 2024/08/06 06:44:34 adam Exp $ d24 1 a24 1 PYTHON_VERSIONS_INCOMPATIBLE= 38 39 @ 1.22 log @py-statsmodels: not for Python 3.9 anymore @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.21 2024/04/19 19:29:23 adam Exp $ d3 1 a3 1 DISTNAME= statsmodels-0.14.2 d24 1 a24 1 PYTHON_VERSIONS_INCOMPATIBLE= 27 38 39 @ 1.21 log @py-statsmodels: updated to 0.14.2 0.14.2 This is a compatibility release that will allow statsmodels to run in environments using NumPy 2. Full compatibility with NumPy 2 Improved future proofing against pandas 3 changes @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.20 2023/12/17 08:34:02 wiz Exp $ d24 1 a24 1 PYTHON_VERSIONS_INCOMPATIBLE= 27 38 @ 1.20 log @py-statsmodels: add missing tool @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.19 2023/12/15 09:48:02 adam Exp $ d3 1 a3 1 DISTNAME= statsmodels-0.14.1 d13 3 a15 2 TOOL_DEPENDS+= ${PYPKGPREFIX}-cython>=0.29.28:../../devel/py-cython TOOL_DEPENDS+= ${PYPKGPREFIX}-setuptools_scm>=7.0.0:../../devel/py-setuptools_scm d18 3 a20 3 DEPENDS+= ${PYPKGPREFIX}-pandas>=0.21:../../math/py-pandas DEPENDS+= ${PYPKGPREFIX}-patsy>=0.5.2:../../math/py-patsy DEPENDS+= ${PYPKGPREFIX}-scipy>=1.9.3:../../math/py-scipy @ 1.19 log @py-statsmodels: updated to 0.14.1 Release 0.14.1 This is a bug-fix and compatability focused release. There are two enhancements to the graphics module. @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.18 2023/12/10 09:41:36 wiz Exp $ d15 1 @ 1.18 log @py-statsmodels: fix build with Cython 3. Bump PKGREVISION. @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.17 2023/08/01 23:20:49 wiz Exp $ d3 1 a3 1 DISTNAME= statsmodels-0.14.0 a4 1 PKGREVISION= 1 a18 1 TEST_DEPENDS+= ${PYPKGPREFIX}-test>=7.0.1:../../devel/py-test d24 1 a24 5 # FIXME: conflicts with installed tests do-test: cd ${WRKSRC} && ${SETENV} ${TEST_ENV} pytest-${PYVERSSUFFIX} statsmodels .include "../../lang/python/egg.mk" @ 1.17 log @*: remove more references to Python 3.7 @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.16 2023/07/01 08:37:41 wiz Exp $ d5 1 @ 1.16 log @*: restrict py-numpy users to 3.9+ in preparation for update @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.15 2023/05/08 08:51:03 adam Exp $ d23 1 a23 1 PYTHON_VERSIONS_INCOMPATIBLE= 27 37 38 @ 1.15 log @py-statsmodels: updated to 0.14.0 Release 0.14.0 The Highlights ============== New cross-sectional models and extensions to models --------------------------------------------------- Treatment Effect ~~~~~~~~~~~~~~~~ :class:`~statsmodels.treatment.TreatmentEffect` estimates treatment effect for a binary treatment and potential outcome for a continuous outcome variable using 5 different methods, ipw, ra, aipw, aipw-wls, ipw-ra. Standard errors and inference are based on the joint GMM representation of selection or treatment model, outcome model and effect functions. Hurdle and Truncated Count Regression ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :class:`statsmodels.discrete.truncated_model.HurdleCountModel` implements hurdle models for count data with either Poisson or NegativeBinomialP as submodels. Three left truncated models used for zero truncation are available, :class:`statsmodels.discrete.truncated_model.TruncatedLFPoisson`, :class:`statsmodels.discrete.truncated_model.TruncatedLFNegativeBinomialP` and :class:`statsmodels.discrete.truncated_model.TruncatedLFGeneralizedPoisson`. Models for right censoring at one are implemented but only as support for the hurdle models. Extended postestimation methods for models ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Results methods for post-estimation have been added or extended. ``get_distribution`` returns a scipy or scipy compatible distribution instance with parameters based on the estimated model. This is available for GLM, discrete models and BetaModel. ``get_prediction`` returns predicted statistics including inferential statistics, standard errors and confidence intervals. The ``which`` keyword selects which statistic is predicted. Inference for statistics that are nonlinear in the estimated parameters are based on the delta-method for standard errors. ``get_diagnostic`` returns a Diagnostic class with additional specification statistics, tests and plots. Currently only available for count models. ``get_influence`` returns a class with outlier and influence diagnostics. (This was mostly added in previous releases.) ``score_test`` makes score (LM) test available as alternative to Wald tests. This is currently available for GLM and some discrete models. The score tests can optionally be robust to misspecification similar to ``cov_type`` for wald tests. Stats ~~~~~ Hypothesis tests, confidence intervals and other inferential statistics are now available for one and two sample Poisson rates. Distributions ~~~~~~~~~~~~~ Methods of Archimedean copulas have been extended to multivariate copulas with dimension larger than 2. The ``pdf`` method of Frank and Gumbel has been extended only to dimensions 3 and 4. New class ECDFDiscrete for empirical distribution function when observations are not unique as in discrete distributions. Multiseason STL decomposition (MSTL) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The existing :class:`~statsmodels.tsa.seasonal.STL` class has been extended to handle multiple seasonal components in :class:`~statsmodels.tsa.seasonal.MSTL`. @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.14 2022/11/23 23:48:02 wiz Exp $ d23 1 a23 1 PYTHON_VERSIONS_INCOMPATIBLE= 27 37 # py-scipy @ 1.14 log @py-statsmodels: add py-setuptools_scm tool dependency to fix PLIST @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.13 2022/11/21 09:40:59 wiz Exp $ d3 1 a3 1 DISTNAME= statsmodels-0.13.5 d13 10 a23 7 USE_LANGUAGES= c TOOL_DEPENDS+= ${PYPKGPREFIX}-cython>=0.29:../../devel/py-cython TOOL_DEPENDS+= ${PYPKGPREFIX}-setuptools_scm-[0-9]*:../../devel/py-setuptools_scm DEPENDS+= ${PYPKGPREFIX}-pandas>=0.21:../../math/py-pandas DEPENDS+= ${PYPKGPREFIX}-patsy>=0.5:../../math/py-patsy DEPENDS+= ${PYPKGPREFIX}-scipy>=1.1:../../math/py-scipy d25 3 a27 3 post-extract: ${CHMOD} -R o-w,g-w ${WRKSRC} ${FIND} ${WRKSRC} -type f -printx | ${XARGS} ${CHMOD} a-x d30 1 a30 1 BUILDLINK_API_DEPENDS.py-numpy+= ${PYPKGPREFIX}-numpy>=1.15 @ 1.13 log @py-statsmodels: update to 0.13.5. Major news: New cross-sectional models Beta Regression BetaModel estimates a regression model for dependent variable in the unit interval such as fractions and proportions based on the Beta distribution. The Model is parameterized by mean and precision, where both can depend on explanatory variables through link functions. Ordinal Regression statsmodels.miscmodels.ordinal_model.OrderedModel implements cumulative link models for ordinal data, based on Logit, Probit or a userprovided CDF link. Distributions Copulas Statsmodels includes now basic support for mainly bivariate copulas. Currently, 10 copulas are available, Archimedean, elliptical and asymmetric extreme value copulas. CopulaDistribution combines a copula with marginal distributions to create multivariate distributions. Count distribution based on discretization DiscretizedCount provides count distributions generated by discretizing continuous distributions available in scipy. The parameters of the distribution can be estimated by maximum likelihood with DiscretizedModel. Bernstein Distribution BernsteinDistribution creates nonparametric univariate and multivariate distributions using Bernstein polynomials on a regular grid. This can be used to smooth histograms or approximate distributions on the unit hypercube. When the marginal distributions are uniform, then the BernsteinDistribution is a copula. Statistics Brunner Munzel rank comparison Brunner-Munzel test is nonparametric comparison of two samples and is an extension of Wilcoxon-Mann-Whitney and Fligner-Policello tests that requires only ordinal information without further assumption on the distributions of the samples. Statsmodels provides the Brunner Munzel hypothesis test for stochastic equality in rank_compare_2indep but also confidence intervals and equivalence testing (TOST) for the stochastically larger statistic, also known as Common Language effect size. Nonparametric Asymmetric kernels Asymmetric kernels can nonparametrically estimate density and cumulative distribution function for random variables that have limited support, either unit interval or positive or nonnegative real line. Beta kernels are available for data in the unit interval. The available kernels for positive data are “gamma”, “gamma2”, “bs”, “invgamma”, “invgauss”, “lognorm”, “recipinvgauss” and “weibull” pdf_kernel_asym estimates a kernel density given a bandwidth parameter. cdf_kernel_asym estimates a kernel cdf. Time series analysis Autoregressive Distributed Lag Models ARDL adds support for specifying and estimating ARDL models, and UECM support specifying models in error correction form. ardl_select_order simplifies selecting both AR and DL model orders. bounds_test implements the bounds test of Peseran, Shin and Smith (2001) for testing whether there is a levels relationship without knowing teh orders of integration of the variables. Fixed parameters in ARIMA estimators Allow fixing parameters in ARIMA estimator Hannan-Rissanen (hannan_rissanen) through the new fixed_params argument @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.12 2022/04/10 00:57:15 gutteridge Exp $ d16 2 a17 1 BUILD_DEPENDS+= ${PYPKGPREFIX}-cython>=0.29:../../devel/py-cython @ 1.12 log @Fix build breakage from py-scipy now being Python >= 3.8 @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.11 2022/01/04 20:54:17 wiz Exp $ d3 1 a3 1 DISTNAME= statsmodels-0.12.2 a4 1 PKGREVISION= 1 @ 1.11 log @*: bump PKGREVISION for egg.mk users They now have a tool dependency on py-setuptools instead of a DEPENDS @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.10 2021/12/30 13:05:39 adam Exp $ d14 1 a14 1 PYTHON_VERSIONS_INCOMPATIBLE= 27 # py-scipy @ 1.10 log @Forget about Python 3.6 @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.9 2021/04/06 12:16:47 prlw1 Exp $ d5 1 @ 1.9 log @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`, :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. @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.8 2020/10/12 21:52:04 bacon Exp $ d13 1 a13 1 PYTHON_VERSIONS_INCOMPATIBLE= 36 27 # py-scipy @ 1.8 log @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 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 @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.7 2020/05/03 16:13:11 minskim Exp $ d3 1 a3 1 DISTNAME= statsmodels-0.11.1 a4 1 PKGREVISION= 1 d13 7 a19 4 BUILD_DEPENDS+= ${PYPKGPREFIX}-cython>=0.24:../../devel/py-cython DEPENDS+= ${PYPKGPREFIX}-pandas>=0.19:../../math/py-pandas DEPENDS+= ${PYPKGPREFIX}-patsy>=0.4.0:../../math/py-patsy DEPENDS+= ${PYPKGPREFIX}-scipy>=0.18:../../math/py-scipy d21 3 a23 2 PYTHON_VERSIONS_INCOMPATIBLE= 27 # py-matplotlib, py-scipy USE_LANGUAGES= c d26 1 a26 1 BUILDLINK_API_DEPENDS.py-numpy+= ${PYPKGPREFIX}-numpy>=1.11 @ 1.7 log @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 @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.6 2020/01/08 01:23:22 minskim Exp $ d5 1 @ 1.6 log @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 @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.5 2019/09/27 09:00:38 wiz Exp $ d3 1 a3 1 DISTNAME= statsmodels-0.10.2 @ 1.5 log @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. @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.4 2019/06/17 05:29:43 adam Exp $ d3 1 a3 2 DISTNAME= statsmodels-0.9.0 PKGREVISION= 2 d9 2 a10 2 HOMEPAGE= http://www.statsmodels.org/stable/index.html COMMENT= Statistical computations and models for use with SciPy d13 4 a16 4 DEPENDS+= ${PYPKGPREFIX}-cython>=0.14.1:../../devel/py-cython DEPENDS+= ${PYPKGPREFIX}-pandas>=0.14.1:../../math/py-pandas DEPENDS+= ${PYPKGPREFIX}-patsy>=0.3.0:../../math/py-patsy DEPENDS+= ${PYPKGPREFIX}-scipy>=0.12.0:../../math/py-scipy a20 16 SUBST_CLASSES+= scipy SUBST_STAGE.scipy= pre-configure SUBST_MESSAGE.scipy= Fix for newer SciPy SUBST_FILES.scipy= statsmodels/distributions/edgeworth.py SUBST_FILES.scipy+= statsmodels/distributions/tests/test_edgeworth.py SUBST_FILES.scipy+= statsmodels/graphics/functional.py SUBST_FILES.scipy+= statsmodels/miscmodels/count.py SUBST_FILES.scipy+= statsmodels/sandbox/distributions/genpareto.py SUBST_FILES.scipy+= statsmodels/sandbox/infotheo.py SUBST_FILES.scipy+= statsmodels/sandbox/nonparametric/densityorthopoly.py SUBST_FILES.scipy+= statsmodels/sandbox/nonparametric/kernels.py SUBST_FILES.scipy+= statsmodels/sandbox/stats/runs.py SUBST_FILES.scipy+= statsmodels/stats/moment_helpers.py SUBST_FILES.scipy+= statsmodels/tsa/regime_switching/markov_switching.py SUBST_SED.scipy= -e 's,\(from scipy\).misc,\1.special,' d22 1 a22 1 BUILDLINK_API_DEPENDS.py-numpy+= ${PYPKGPREFIX}-numpy>=0.12.0 @ 1.4 log @py-statsmodels: fix for newer SciPy @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.3 2018/07/05 13:09:11 adam Exp $ d4 1 a4 1 PKGREVISION= 1 d20 1 @ 1.3 log @py-statsmodels: updated to 0.9.0 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 @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.2 2017/05/21 09:07:37 adam Exp $ d4 1 d19 18 d38 1 @ 1.2 log @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. @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.1 2016/07/15 07:35:50 wiz Exp $ d3 1 a3 1 DISTNAME= statsmodels-0.8.0 d5 1 a5 1 CATEGORIES= math d9 1 a9 1 HOMEPAGE= http://statsmodels.sourceforge.net/ @ 1.1 log @Import py-statsmodels-0.8.0rc1 as math/py-statsmodels. 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 @ text @d1 1 a1 1 # $NetBSD: Makefile,v 1.1 2014/07/26 11:16:19 jihbed Exp $ d3 1 a3 1 DISTNAME= statsmodels-0.8.0rc1 d13 1 a17 1 .include "../../devel/py-cython/buildlink3.mk" @