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It also fixes some small future issues and ensures that the test suit passes against recent releases of upstream projects. @ text @$NetBSD: distinfo,v 1.16 2025/07/13 16:41:50 adam Exp $ BLAKE2s (statsmodels-0.14.6.tar.gz) = e8e6d698c452630d9e1e5846c33ad1acf96703ac72c9448f99ad3cad43920bf4 SHA512 (statsmodels-0.14.6.tar.gz) = 5bb28aea779236d5f48743db624c048f78117456e1752414bd156f650098a996a81d882fd271fc5e203298dc941a656450f252a94b2d05e86b571e5ebb0b29fd Size (statsmodels-0.14.6.tar.gz) = 20689085 bytes SHA1 (patch-statsmodels_tsa_exponential__smoothing___ets__smooth.pyx) = 7f16d85398a2dda6a11c3a4e0c47bad454194207 SHA1 (patch-statsmodels_tsa_statespace___filters___inversions.pyx.in) = 2d22542e9ce8122fe8d127907897fd33b310d610 SHA1 (patch-statsmodels_tsa_statespace___filters___univariate.pyx.in) = 38a00193937033bd7bd739cbf6859c67dd443c97 SHA1 (patch-statsmodels_tsa_statespace___filters___univariate__diffuse.pyx.in) = f9d4746a87f5d5f23e916f0b2369c9f90d96048c @ 1.16 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: distinfo,v 1.15 2025/01/05 11:10:48 adam Exp $ d3 3 a5 3 BLAKE2s (statsmodels-0.14.5.tar.gz) = 5a5db5b24d674d740c42b66c668efbd1cf6721dc7324596c68baf52b1b293219 SHA512 (statsmodels-0.14.5.tar.gz) = b36763d2ea3a1db2181838cc05834e96c9914789155b4723ee260f94a6f481fd8ea5c07294e375efd783b06d28cf9b71385c00b94ed0271189da551f562948f3 Size (statsmodels-0.14.5.tar.gz) = 20525016 bytes @ 1.15 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: distinfo,v 1.14 2024/09/16 19:25:59 adam Exp $ d3 3 a5 3 BLAKE2s (statsmodels-0.14.4.tar.gz) = 03cf866314aed79796234a595e48bb14027c2ca481d0f03da5289c2fddc82bf0 SHA512 (statsmodels-0.14.4.tar.gz) = db3bd19b8b47af453ad6fbd24449a621895e4237bbc8b7813810306748c32a3fbf005387a0e1e8db550c9c4a59666bdd50aeb49e24f0ecb86619bc90f166f5f9 Size (statsmodels-0.14.4.tar.gz) = 20354802 bytes @ 1.14 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: distinfo,v 1.13 2024/04/19 19:29:23 adam Exp $ d3 3 a5 3 BLAKE2s (statsmodels-0.14.3.tar.gz) = 727ef8fd94b449d0af0e2b9418601fd6aa0948bd404b58e1cc1f64dd90b3e5eb SHA512 (statsmodels-0.14.3.tar.gz) = c7b8684be83b179edcdbed7703005d054a1f16b9731add69b5079a62e5e858bef87c34e00575580fd48ef80cc9108d405edec9e6a2cf3eb3e5e8f7fed53f8fca Size (statsmodels-0.14.3.tar.gz) = 20354488 bytes @ 1.13 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: distinfo,v 1.12 2023/12/15 09:48:02 adam Exp $ d3 3 a5 3 BLAKE2s (statsmodels-0.14.2.tar.gz) = 281922992b300e777d4a87d5a1880504b9a6d1edbda0507511b51703209eca89 SHA512 (statsmodels-0.14.2.tar.gz) = 0a38482bccac61081ff0c8de3b9fcf81689ec15437c53d79a8949df058e0ac8a4e93136a50a37a06577108dc29f1a51e0d00e041ee7e8f9dff93b6e8cf1f0f16 Size (statsmodels-0.14.2.tar.gz) = 20352531 bytes @ 1.12 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: distinfo,v 1.11 2023/12/10 09:41:36 wiz Exp $ d3 3 a5 3 BLAKE2s (statsmodels-0.14.1.tar.gz) = 11d6c7d4f8c24fc9c24a4dc9025af637e1e208348ec177902211130c9cd8ac7e SHA512 (statsmodels-0.14.1.tar.gz) = e382ca807205e2aeff76dd22b42e7824914472588ea040d90835fd46e0c993e155828c58e81f53f9539ad9bdf195bbbd1a49a45658187498d60287374112fd68 Size (statsmodels-0.14.1.tar.gz) = 20309647 bytes @ 1.11 log @py-statsmodels: fix build with Cython 3. Bump PKGREVISION. @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.10 2023/05/08 08:51:03 adam Exp $ d3 3 a5 3 BLAKE2s (statsmodels-0.14.0.tar.gz) = 568fe2bf6f24d4c10d48571586766f895f6584ec3b35926cccf15b5c032e49ba SHA512 (statsmodels-0.14.0.tar.gz) = 876cc45eb4b5badee2ff859df8a45ce7c4f6ab2973d481f58c5b7906ebcdbb56a64769d5dd7a38c7b7415a4ee7cf98cf300b8c623bda9df001b982ff6844d1fd Size (statsmodels-0.14.0.tar.gz) = 19374614 bytes @ 1.10 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: distinfo,v 1.9 2022/11/21 09:40:59 wiz Exp $ d6 4 @ 1.9 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: distinfo,v 1.8 2021/10/26 10:56:05 nia Exp $ d3 3 a5 3 BLAKE2s (statsmodels-0.13.5.tar.gz) = 9d8c02dc5b0a78d3ffea3fdf119cac96e13b469f39e42598aaa8775d415fa9a5 SHA512 (statsmodels-0.13.5.tar.gz) = 9aeeea80c69f52459140179523a0155429834b2951325c9781b28f8c4cbbd0593ff1867e2212078f2b898e4da953689c2fe78183d3c7959caa874e9d758b4ea4 Size (statsmodels-0.13.5.tar.gz) = 18364957 bytes @ 1.8 log @math: Replace RMD160 checksums with BLAKE2s checksums All checksums have been double-checked against existing RMD160 and SHA512 hashes @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.7 2021/10/07 14:28:28 nia Exp $ d3 3 a5 3 BLAKE2s (statsmodels-0.12.2.tar.gz) = 358ad9df9c1f560312f9c98b0e89fb6a2eab486d462a0a5bba3433e8b9c99856 SHA512 (statsmodels-0.12.2.tar.gz) = ae4872bc7300ef564407daa8b4076fd70fc180965622ed2173871579e063e2143e000540089923fe171dbb191b7dd872077d8ba6794fe23390331375ec7ce810 Size (statsmodels-0.12.2.tar.gz) = 17470078 bytes @ 1.7 log @math: Remove SHA1 hashes for distfiles @ text @d1 1 a1 1 $NetBSD: distinfo,v 1.6 2021/04/06 12:16:47 prlw1 Exp $ d3 1 a3 1 RMD160 (statsmodels-0.12.2.tar.gz) = d8049f589996c1a4f9c443d2303cb466e013c033 @ 1.6 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: distinfo,v 1.5 2020/05/03 16:13:11 minskim Exp $ a2 1 SHA1 (statsmodels-0.12.2.tar.gz) = 3a653a3fbfe9b3c9083193ded85f4875bc9d5b05 @ 1.5 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: distinfo,v 1.4 2020/01/08 01:23:22 minskim Exp $ d3 4 a6 4 SHA1 (statsmodels-0.11.1.tar.gz) = 2b0ca6d66ec4415e8fe4b501149c901d92f73d9c RMD160 (statsmodels-0.11.1.tar.gz) = 86d2ea3a9f702c787686b5c7d3e79feb2dc0747d SHA512 (statsmodels-0.11.1.tar.gz) = 54afe55a23b431154c159f44d284aa093f3368988f0695c0f3fbb206046cdfb171ab2ba51ce94285d567b8536141f93a1ef404b5f7222f1e61264baf0541926d Size (statsmodels-0.11.1.tar.gz) = 15381516 bytes @ 1.4 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: distinfo,v 1.3 2018/07/05 13:09:11 adam Exp $ d3 4 a6 4 SHA1 (statsmodels-0.10.2.tar.gz) = 084998f7bb0b42d48e73820b7f84d5c37782ce2e RMD160 (statsmodels-0.10.2.tar.gz) = 9b437ff8df046770847ac865d4c4b55b7e1b4e3b SHA512 (statsmodels-0.10.2.tar.gz) = bd1c0784b0b17a3ca69fef5848f5eea8dcf76b1943599a5e5c285e45b7fcc7e44c0e388f007913d420ff6f3cb66a653d1c43e6e8addef534ff5572fa69ffb54a Size (statsmodels-0.10.2.tar.gz) = 14065612 bytes @ 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: distinfo,v 1.2 2017/05/21 09:07:37 adam Exp $ d3 4 a6 4 SHA1 (statsmodels-0.9.0.tar.gz) = a41d329cc0edf686904f230aa8bb3fa388217498 RMD160 (statsmodels-0.9.0.tar.gz) = cc59ed63528df7c0a6eeb27a3ac0439e3ef5f42f SHA512 (statsmodels-0.9.0.tar.gz) = a0310129ee915dce5006e4e40190d19c3a09facad398ff089fa4a244d51a035f9591267fd8d34a00ce82e4cab893df96787596f9d350d878e97a0bb3305f1bd5 Size (statsmodels-0.9.0.tar.gz) = 12658359 bytes @ 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: distinfo,v 1.1 2016/07/15 07:35:50 wiz Exp $ d3 4 a6 4 SHA1 (statsmodels-0.8.0.tar.gz) = bbd5241d5edea5ebfcf1d296005c2ce3b8e1106b RMD160 (statsmodels-0.8.0.tar.gz) = 9e4226caca648f5398a74776822701c42b09da05 SHA512 (statsmodels-0.8.0.tar.gz) = 32bb7f36acc16796c445e5f695d958af4525fbbb2d374376fb4a73c972e3796fad05532456cef4aa5ee59d6fce11921174e17bbfc2e05d2488ce1ceac5175239 Size (statsmodels-0.8.0.tar.gz) = 9464851 bytes @ 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: distinfo,v 1.1 2014/07/26 11:16:19 jihbed Exp $ d3 4 a6 4 SHA1 (statsmodels-0.8.0rc1.tar.gz) = 808bddc2a04e2429c76fb594da05fd2952996292 RMD160 (statsmodels-0.8.0rc1.tar.gz) = d2547e06657deac9270a9d6c972a4e042b4ffeb3 SHA512 (statsmodels-0.8.0rc1.tar.gz) = e963c7fc98a8d087df43ef0b6f7807dc25d67713fdf5a74391979604291f194e088ac0669465f6c94da867f58872fcee6cafdfb3ad9d85ce4c903316351734ee Size (statsmodels-0.8.0rc1.tar.gz) = 9384625 bytes @