Log message:
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`.

Log message:
py-statsmodels: add py-setuptools_scm tool dependency to fix PLIST

Log message:
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

Log message:
Fix build breakage from py-scipy now being Python >= 3.8

Log message:
*: bump PKGREVISION for egg.mk users

They now have a tool dependency on py-setuptools instead of a DEPENDS

Log message:
Forget about Python 3.6

Log message:
math: Replace RMD160 checksums with BLAKE2s checksums

All checksums have been double-checked against existing RMD160 and
SHA512 hashes

Log message:
math: Remove SHA1 hashes for distfiles

Log message:
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.

Log message:
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