---
- branch: MAIN
  date: Tue Apr  6 12:16:47 UTC 2021
  files:
  - new: '1.9'
    old: '1.8'
    path: pkgsrc/math/py-statsmodels/Makefile
    pathrev: pkgsrc/math/py-statsmodels/Makefile@1.9
    type: modified
  - new: '1.7'
    old: '1.6'
    path: pkgsrc/math/py-statsmodels/PLIST
    pathrev: pkgsrc/math/py-statsmodels/PLIST@1.7
    type: modified
  - new: '1.6'
    old: '1.5'
    path: pkgsrc/math/py-statsmodels/distinfo
    pathrev: pkgsrc/math/py-statsmodels/distinfo@1.6
    type: modified
  id: 20210406T121647Z.5031678cbdcea91b4a7eb4f74b5a23afcfa71295
  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.
  module: pkgsrc
  subject: 'CVS commit: pkgsrc/math/py-statsmodels'
  unixtime: '1617711407'
  user: prlw1