---
- branch: MAIN
  date: Mon Nov 21 09:40:59 UTC 2022
  files:
  - new: '1.13'
    old: '1.12'
    path: pkgsrc/math/py-statsmodels/Makefile
    pathrev: pkgsrc/math/py-statsmodels/Makefile@1.13
    type: modified
  - new: '1.8'
    old: '1.7'
    path: pkgsrc/math/py-statsmodels/PLIST
    pathrev: pkgsrc/math/py-statsmodels/PLIST@1.8
    type: modified
  - new: '1.9'
    old: '1.8'
    path: pkgsrc/math/py-statsmodels/distinfo
    pathrev: pkgsrc/math/py-statsmodels/distinfo@1.9
    type: modified
  id: 20221121T094059Z.8360efcf69cffc715d8212815fe4e4ab2c7f0960
  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
  module: pkgsrc
  subject: 'CVS commit: pkgsrc/math/py-statsmodels'
  unixtime: '1669023659'
  user: wiz