[no description] https://git.osdyson.ru/mirror/pkgsrc/atom?h=trunk 2022-11-23T23:48:02Z 2022-11-23T23:48:02Z wiz wiz@pkgsrc.org 2022-11-23T23:48:02Z urn:sha1:e6d0037396fc63540d5ee9f4a1773f0d27c7c76d 2022-11-21T09:40:59Z wiz wiz@pkgsrc.org 2022-11-21T09:40:59Z urn:sha1:4f6e2823ff362a28131634638514b04450856fdd 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 2022-04-10T00:57:13Z gutteridge gutteridge@pkgsrc.org 2022-04-10T00:57:13Z urn:sha1:8677ad2fe58c3497b77044e78b60b5427303c898 2022-01-04T20:53:26Z wiz wiz@pkgsrc.org 2022-01-04T20:53:26Z urn:sha1:c7c6245bef7e5ca621e710024a67808158545477 They now have a tool dependency on py-setuptools instead of a DEPENDS 2021-12-30T13:05:27Z adam adam@pkgsrc.org 2021-12-30T13:05:27Z urn:sha1:2a4aa624ba67ab6f387f6ea74ed58116f72f8029 2021-10-26T10:55:21Z nia nia@pkgsrc.org 2021-10-26T10:55:21Z urn:sha1:35050bad02b57ed8ca4a6430799b6800b9846072 All checksums have been double-checked against existing RMD160 and SHA512 hashes 2021-10-07T14:27:43Z nia nia@pkgsrc.org 2021-10-07T14:27:43Z urn:sha1:a4dc7f45228012c1f678c7bbf0d0693040642bf6 2021-04-06T12:16:47Z prlw1 prlw1@pkgsrc.org 2021-04-06T12:16:47Z urn:sha1:d20049c3a58ab73bc1052c57ab0980448c89e0c8 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. 2020-10-12T21:51:57Z bacon bacon@pkgsrc.org 2020-10-12T21:51:57Z urn:sha1:afefcaa6e54efd2e5b8f95abf655d5d040ae36be 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 2020-05-03T16:13:11Z minskim minskim@pkgsrc.org 2020-05-03T16:13:11Z urn:sha1:c3f54ca81b815ab468106242ad73bf5fde4b0166 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