CVS commit: pkgsrc/math/py-statsmodels

To: pkgsrc-changes%NetBSD.org@localhost
Subject: CVS commit: pkgsrc/math/py-statsmodels
From: "Patrick Welche" <prlw1%netbsd.org@localhost>
Date: Tue, 6 Apr 2021 12:16:47 +0000

Module Name:    pkgsrc
Committed By:   prlw1
Date:           Tue Apr  6 12:16:47 UTC 2021

Modified Files:
        pkgsrc/math/py-statsmodels: Makefile PLIST distinfo

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.


To generate a diff of this commit:
cvs rdiff -u -r1.8 -r1.9 pkgsrc/math/py-statsmodels/Makefile
cvs rdiff -u -r1.6 -r1.7 pkgsrc/math/py-statsmodels/PLIST
cvs rdiff -u -r1.5 -r1.6 pkgsrc/math/py-statsmodels/distinfo

Please note that diffs are not public domain; they are subject to the
copyright notices on the relevant files.

Modified files:

Index: pkgsrc/math/py-statsmodels/Makefile
diff -u pkgsrc/math/py-statsmodels/Makefile:1.8 pkgsrc/math/py-statsmodels/Makefile:1.9
--- pkgsrc/math/py-statsmodels/Makefile:1.8     Mon Oct 12 21:52:04 2020
+++ pkgsrc/math/py-statsmodels/Makefile Tue Apr  6 12:16:47 2021
@@ -1,8 +1,7 @@
-# $NetBSD: Makefile,v 1.8 2020/10/12 21:52:04 bacon Exp $
+# $NetBSD: Makefile,v 1.9 2021/04/06 12:16:47 prlw1 Exp $
 
-DISTNAME=      statsmodels-0.11.1
+DISTNAME=      statsmodels-0.12.2
 PKGNAME=       ${PYPKGPREFIX}-${DISTNAME}
-PKGREVISION=   1
 CATEGORIES=    math python
 MASTER_SITES=  ${MASTER_SITE_PYPI:=s/statsmodels/}
 
@@ -11,15 +10,19 @@ HOMEPAGE=   https://www.statsmodels.org/
 COMMENT=       Statistical computations and models for Python
 LICENSE=       modified-bsd
 
-BUILD_DEPENDS+=        ${PYPKGPREFIX}-cython>=0.24:../../devel/py-cython
-DEPENDS+=      ${PYPKGPREFIX}-pandas>=0.19:../../math/py-pandas
-DEPENDS+=      ${PYPKGPREFIX}-patsy>=0.4.0:../../math/py-patsy
-DEPENDS+=      ${PYPKGPREFIX}-scipy>=0.18:../../math/py-scipy
-
-PYTHON_VERSIONS_INCOMPATIBLE=  27      # py-matplotlib, py-scipy
+PYTHON_VERSIONS_INCOMPATIBLE=  36 27   # py-scipy
 USE_LANGUAGES=                 c
 
+BUILD_DEPENDS+=        ${PYPKGPREFIX}-cython>=0.29:../../devel/py-cython
+DEPENDS+=      ${PYPKGPREFIX}-pandas>=0.21:../../math/py-pandas
+DEPENDS+=      ${PYPKGPREFIX}-patsy>=0.5:../../math/py-patsy
+DEPENDS+=      ${PYPKGPREFIX}-scipy>=1.1:../../math/py-scipy
+
+post-extract:
+       ${CHMOD} -R o-w,g-w ${WRKSRC}
+       ${FIND} ${WRKSRC} -type f -printx | ${XARGS} ${CHMOD} a-x
+
 .include "../../lang/python/egg.mk"
-BUILDLINK_API_DEPENDS.py-numpy+=       ${PYPKGPREFIX}-numpy>=1.11
+BUILDLINK_API_DEPENDS.py-numpy+=       ${PYPKGPREFIX}-numpy>=1.15
 .include "../../math/py-numpy/buildlink3.mk"
 .include "../../mk/bsd.pkg.mk"

Index: pkgsrc/math/py-statsmodels/PLIST
diff -u pkgsrc/math/py-statsmodels/PLIST:1.6 pkgsrc/math/py-statsmodels/PLIST:1.7
--- pkgsrc/math/py-statsmodels/PLIST:1.6        Sun May  3 16:13:11 2020
+++ pkgsrc/math/py-statsmodels/PLIST    Tue Apr  6 12:16:47 2021
@@ -1,4 +1,4 @@
-@comment $NetBSD: PLIST,v 1.6 2020/05/03 16:13:11 minskim Exp $
+@comment $NetBSD: PLIST,v 1.7 2021/04/06 12:16:47 prlw1 Exp $
 ${PYSITELIB}/${EGG_INFODIR}/PKG-INFO
 ${PYSITELIB}/${EGG_INFODIR}/SOURCES.txt
 ${PYSITELIB}/${EGG_INFODIR}/dependency_links.txt
@@ -1209,12 +1209,10 @@ ${PYSITELIB}/statsmodels/regression/test
 ${PYSITELIB}/statsmodels/regression/tests/test_tools.py
 ${PYSITELIB}/statsmodels/regression/tests/test_tools.pyc
 ${PYSITELIB}/statsmodels/regression/tests/test_tools.pyo
-${PYSITELIB}/statsmodels/resampling/__init__.py
-${PYSITELIB}/statsmodels/resampling/__init__.pyc
-${PYSITELIB}/statsmodels/resampling/__init__.pyo
 ${PYSITELIB}/statsmodels/robust/__init__.py
 ${PYSITELIB}/statsmodels/robust/__init__.pyc
 ${PYSITELIB}/statsmodels/robust/__init__.pyo
+${PYSITELIB}/statsmodels/robust/_qn.so
 ${PYSITELIB}/statsmodels/robust/norms.py
 ${PYSITELIB}/statsmodels/robust/norms.pyc
 ${PYSITELIB}/statsmodels/robust/norms.pyo
@@ -1710,6 +1708,9 @@ ${PYSITELIB}/statsmodels/stats/libqsturn
 ${PYSITELIB}/statsmodels/stats/mediation.py
 ${PYSITELIB}/statsmodels/stats/mediation.pyc
 ${PYSITELIB}/statsmodels/stats/mediation.pyo
+${PYSITELIB}/statsmodels/stats/meta_analysis.py
+${PYSITELIB}/statsmodels/stats/meta_analysis.pyc
+${PYSITELIB}/statsmodels/stats/meta_analysis.pyo
 ${PYSITELIB}/statsmodels/stats/moment_helpers.py
 ${PYSITELIB}/statsmodels/stats/moment_helpers.pyc
 ${PYSITELIB}/statsmodels/stats/moment_helpers.pyo
@@ -1719,12 +1720,18 @@ ${PYSITELIB}/statsmodels/stats/multicomp
 ${PYSITELIB}/statsmodels/stats/multitest.py
 ${PYSITELIB}/statsmodels/stats/multitest.pyc
 ${PYSITELIB}/statsmodels/stats/multitest.pyo
+${PYSITELIB}/statsmodels/stats/multivariate.py
+${PYSITELIB}/statsmodels/stats/multivariate.pyc
+${PYSITELIB}/statsmodels/stats/multivariate.pyo
 ${PYSITELIB}/statsmodels/stats/multivariate_tools.py
 ${PYSITELIB}/statsmodels/stats/multivariate_tools.pyc
 ${PYSITELIB}/statsmodels/stats/multivariate_tools.pyo
 ${PYSITELIB}/statsmodels/stats/oaxaca.py
 ${PYSITELIB}/statsmodels/stats/oaxaca.pyc
 ${PYSITELIB}/statsmodels/stats/oaxaca.pyo
+${PYSITELIB}/statsmodels/stats/oneway.py
+${PYSITELIB}/statsmodels/stats/oneway.pyc
+${PYSITELIB}/statsmodels/stats/oneway.pyo
 ${PYSITELIB}/statsmodels/stats/outliers_influence.py
 ${PYSITELIB}/statsmodels/stats/outliers_influence.pyc
 ${PYSITELIB}/statsmodels/stats/outliers_influence.pyo
@@ -1734,9 +1741,15 @@ ${PYSITELIB}/statsmodels/stats/power.pyo
 ${PYSITELIB}/statsmodels/stats/proportion.py
 ${PYSITELIB}/statsmodels/stats/proportion.pyc
 ${PYSITELIB}/statsmodels/stats/proportion.pyo
+${PYSITELIB}/statsmodels/stats/rates.py
+${PYSITELIB}/statsmodels/stats/rates.pyc
+${PYSITELIB}/statsmodels/stats/rates.pyo
 ${PYSITELIB}/statsmodels/stats/regularized_covariance.py
 ${PYSITELIB}/statsmodels/stats/regularized_covariance.pyc
 ${PYSITELIB}/statsmodels/stats/regularized_covariance.pyo
+${PYSITELIB}/statsmodels/stats/robust_compare.py
+${PYSITELIB}/statsmodels/stats/robust_compare.pyc
+${PYSITELIB}/statsmodels/stats/robust_compare.pyo
 ${PYSITELIB}/statsmodels/stats/sandwich_covariance.py
 ${PYSITELIB}/statsmodels/stats/sandwich_covariance.pyc
 ${PYSITELIB}/statsmodels/stats/sandwich_covariance.pyo
@@ -1764,6 +1777,9 @@ ${PYSITELIB}/statsmodels/stats/tests/res
 ${PYSITELIB}/statsmodels/stats/tests/results/lilliefors_critical_value_simulation.pyc
 ${PYSITELIB}/statsmodels/stats/tests/results/lilliefors_critical_value_simulation.pyo
 ${PYSITELIB}/statsmodels/stats/tests/results/results_influence_logit.csv
+${PYSITELIB}/statsmodels/stats/tests/results/results_meta.py
+${PYSITELIB}/statsmodels/stats/tests/results/results_meta.pyc
+${PYSITELIB}/statsmodels/stats/tests/results/results_meta.pyo
 ${PYSITELIB}/statsmodels/stats/tests/results/results_multinomial_proportions.py
 ${PYSITELIB}/statsmodels/stats/tests/results/results_multinomial_proportions.pyc
 ${PYSITELIB}/statsmodels/stats/tests/results/results_multinomial_proportions.pyo
@@ -1776,6 +1792,9 @@ ${PYSITELIB}/statsmodels/stats/tests/res
 ${PYSITELIB}/statsmodels/stats/tests/results/results_proportion.py
 ${PYSITELIB}/statsmodels/stats/tests/results/results_proportion.pyc
 ${PYSITELIB}/statsmodels/stats/tests/results/results_proportion.pyo
+${PYSITELIB}/statsmodels/stats/tests/results/results_rates.py
+${PYSITELIB}/statsmodels/stats/tests/results/results_rates.pyc
+${PYSITELIB}/statsmodels/stats/tests/results/results_rates.pyo
 ${PYSITELIB}/statsmodels/stats/tests/results/wspec1.csv
 ${PYSITELIB}/statsmodels/stats/tests/results/wspec2.csv
 ${PYSITELIB}/statsmodels/stats/tests/results/wspec3.csv
@@ -1786,6 +1805,9 @@ ${PYSITELIB}/statsmodels/stats/tests/tes
 ${PYSITELIB}/statsmodels/stats/tests/test_anova_rm.py
 ${PYSITELIB}/statsmodels/stats/tests/test_anova_rm.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_anova_rm.pyo
+${PYSITELIB}/statsmodels/stats/tests/test_base.py
+${PYSITELIB}/statsmodels/stats/tests/test_base.pyc
+${PYSITELIB}/statsmodels/stats/tests/test_base.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_contingency_tables.py
 ${PYSITELIB}/statsmodels/stats/tests/test_contingency_tables.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_contingency_tables.pyo
@@ -1832,18 +1854,27 @@ ${PYSITELIB}/statsmodels/stats/tests/tes
 ${PYSITELIB}/statsmodels/stats/tests/test_mediation.py
 ${PYSITELIB}/statsmodels/stats/tests/test_mediation.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_mediation.pyo
+${PYSITELIB}/statsmodels/stats/tests/test_meta.py
+${PYSITELIB}/statsmodels/stats/tests/test_meta.pyc
+${PYSITELIB}/statsmodels/stats/tests/test_meta.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_moment_helpers.py
 ${PYSITELIB}/statsmodels/stats/tests/test_moment_helpers.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_moment_helpers.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_multi.py
 ${PYSITELIB}/statsmodels/stats/tests/test_multi.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_multi.pyo
+${PYSITELIB}/statsmodels/stats/tests/test_multivariate.py
+${PYSITELIB}/statsmodels/stats/tests/test_multivariate.pyc
+${PYSITELIB}/statsmodels/stats/tests/test_multivariate.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_nonparametric.py
 ${PYSITELIB}/statsmodels/stats/tests/test_nonparametric.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_nonparametric.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_oaxaca.py
 ${PYSITELIB}/statsmodels/stats/tests/test_oaxaca.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_oaxaca.pyo
+${PYSITELIB}/statsmodels/stats/tests/test_oneway.py
+${PYSITELIB}/statsmodels/stats/tests/test_oneway.pyc
+${PYSITELIB}/statsmodels/stats/tests/test_oneway.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_outliers_influence.py
 ${PYSITELIB}/statsmodels/stats/tests/test_outliers_influence.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_outliers_influence.pyo
@@ -1862,9 +1893,15 @@ ${PYSITELIB}/statsmodels/stats/tests/tes
 ${PYSITELIB}/statsmodels/stats/tests/test_qsturng.py
 ${PYSITELIB}/statsmodels/stats/tests/test_qsturng.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_qsturng.pyo
+${PYSITELIB}/statsmodels/stats/tests/test_rates_poisson.py
+${PYSITELIB}/statsmodels/stats/tests/test_rates_poisson.pyc
+${PYSITELIB}/statsmodels/stats/tests/test_rates_poisson.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_regularized_covariance.py
 ${PYSITELIB}/statsmodels/stats/tests/test_regularized_covariance.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_regularized_covariance.pyo
+${PYSITELIB}/statsmodels/stats/tests/test_robust_compare.py
+${PYSITELIB}/statsmodels/stats/tests/test_robust_compare.pyc
+${PYSITELIB}/statsmodels/stats/tests/test_robust_compare.pyo
 ${PYSITELIB}/statsmodels/stats/tests/test_sandwich.py
 ${PYSITELIB}/statsmodels/stats/tests/test_sandwich.pyc
 ${PYSITELIB}/statsmodels/stats/tests/test_sandwich.pyo
@@ -2015,7 +2052,6 @@ ${PYSITELIB}/statsmodels/tsa/__init__.py
 ${PYSITELIB}/statsmodels/tsa/_bds.py
 ${PYSITELIB}/statsmodels/tsa/_bds.pyc
 ${PYSITELIB}/statsmodels/tsa/_bds.pyo
-${PYSITELIB}/statsmodels/tsa/_exponential_smoothers.so
 ${PYSITELIB}/statsmodels/tsa/_innovations.so
 ${PYSITELIB}/statsmodels/tsa/_stl.so
 ${PYSITELIB}/statsmodels/tsa/adfvalues.py
@@ -2144,6 +2180,9 @@ ${PYSITELIB}/statsmodels/tsa/base/__init
 ${PYSITELIB}/statsmodels/tsa/base/datetools.py
 ${PYSITELIB}/statsmodels/tsa/base/datetools.pyc
 ${PYSITELIB}/statsmodels/tsa/base/datetools.pyo
+${PYSITELIB}/statsmodels/tsa/base/prediction.py
+${PYSITELIB}/statsmodels/tsa/base/prediction.pyc
+${PYSITELIB}/statsmodels/tsa/base/prediction.pyo
 ${PYSITELIB}/statsmodels/tsa/base/tests/__init__.py
 ${PYSITELIB}/statsmodels/tsa/base/tests/__init__.pyc
 ${PYSITELIB}/statsmodels/tsa/base/tests/__init__.pyo
@@ -2153,6 +2192,9 @@ ${PYSITELIB}/statsmodels/tsa/base/tests/
 ${PYSITELIB}/statsmodels/tsa/base/tests/test_datetools.py
 ${PYSITELIB}/statsmodels/tsa/base/tests/test_datetools.pyc
 ${PYSITELIB}/statsmodels/tsa/base/tests/test_datetools.pyo
+${PYSITELIB}/statsmodels/tsa/base/tests/test_prediction.py
+${PYSITELIB}/statsmodels/tsa/base/tests/test_prediction.pyc
+${PYSITELIB}/statsmodels/tsa/base/tests/test_prediction.pyo
 ${PYSITELIB}/statsmodels/tsa/base/tests/test_tsa_indexes.py
 ${PYSITELIB}/statsmodels/tsa/base/tests/test_tsa_indexes.pyc
 ${PYSITELIB}/statsmodels/tsa/base/tests/test_tsa_indexes.pyo
@@ -2165,9 +2207,19 @@ ${PYSITELIB}/statsmodels/tsa/coint_table
 ${PYSITELIB}/statsmodels/tsa/descriptivestats.py
 ${PYSITELIB}/statsmodels/tsa/descriptivestats.pyc
 ${PYSITELIB}/statsmodels/tsa/descriptivestats.pyo
+${PYSITELIB}/statsmodels/tsa/deterministic.py
+${PYSITELIB}/statsmodels/tsa/deterministic.pyc
+${PYSITELIB}/statsmodels/tsa/deterministic.pyo
 ${PYSITELIB}/statsmodels/tsa/exponential_smoothing/__init__.py
 ${PYSITELIB}/statsmodels/tsa/exponential_smoothing/__init__.pyc
 ${PYSITELIB}/statsmodels/tsa/exponential_smoothing/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/exponential_smoothing/_ets_smooth.so
+${PYSITELIB}/statsmodels/tsa/exponential_smoothing/base.py
+${PYSITELIB}/statsmodels/tsa/exponential_smoothing/base.pyc
+${PYSITELIB}/statsmodels/tsa/exponential_smoothing/base.pyo
+${PYSITELIB}/statsmodels/tsa/exponential_smoothing/ets.py
+${PYSITELIB}/statsmodels/tsa/exponential_smoothing/ets.pyc
+${PYSITELIB}/statsmodels/tsa/exponential_smoothing/ets.pyo
 ${PYSITELIB}/statsmodels/tsa/exponential_smoothing/initialization.py
 ${PYSITELIB}/statsmodels/tsa/exponential_smoothing/initialization.pyc
 ${PYSITELIB}/statsmodels/tsa/exponential_smoothing/initialization.pyo
@@ -2204,9 +2256,47 @@ ${PYSITELIB}/statsmodels/tsa/filters/tes
 ${PYSITELIB}/statsmodels/tsa/filters/tests/test_filters.py
 ${PYSITELIB}/statsmodels/tsa/filters/tests/test_filters.pyc
 ${PYSITELIB}/statsmodels/tsa/filters/tests/test_filters.pyo
-${PYSITELIB}/statsmodels/tsa/holtwinters.py
-${PYSITELIB}/statsmodels/tsa/holtwinters.pyc
-${PYSITELIB}/statsmodels/tsa/holtwinters.pyo
+${PYSITELIB}/statsmodels/tsa/forecasting/__init__.py
+${PYSITELIB}/statsmodels/tsa/forecasting/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/forecasting/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/forecasting/stl.py
+${PYSITELIB}/statsmodels/tsa/forecasting/stl.pyc
+${PYSITELIB}/statsmodels/tsa/forecasting/stl.pyo
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/__init__.py
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/test_stl.py
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/test_stl.pyc
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/test_stl.pyo
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/test_theta.py
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/test_theta.pyc
+${PYSITELIB}/statsmodels/tsa/forecasting/tests/test_theta.pyo
+${PYSITELIB}/statsmodels/tsa/forecasting/theta.py
+${PYSITELIB}/statsmodels/tsa/forecasting/theta.pyc
+${PYSITELIB}/statsmodels/tsa/forecasting/theta.pyo
+${PYSITELIB}/statsmodels/tsa/holtwinters/__init__.py
+${PYSITELIB}/statsmodels/tsa/holtwinters/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/holtwinters/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/holtwinters/_exponential_smoothers.so
+${PYSITELIB}/statsmodels/tsa/holtwinters/_smoothers.py
+${PYSITELIB}/statsmodels/tsa/holtwinters/_smoothers.pyc
+${PYSITELIB}/statsmodels/tsa/holtwinters/_smoothers.pyo
+${PYSITELIB}/statsmodels/tsa/holtwinters/model.py
+${PYSITELIB}/statsmodels/tsa/holtwinters/model.pyc
+${PYSITELIB}/statsmodels/tsa/holtwinters/model.pyo
+${PYSITELIB}/statsmodels/tsa/holtwinters/results.py
+${PYSITELIB}/statsmodels/tsa/holtwinters/results.pyc
+${PYSITELIB}/statsmodels/tsa/holtwinters/results.pyo
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/__init__.py
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/results/__init__.py
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/results/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/results/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/results/housing-data.csv
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/test_holtwinters.py
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/test_holtwinters.pyc
+${PYSITELIB}/statsmodels/tsa/holtwinters/tests/test_holtwinters.pyo
 ${PYSITELIB}/statsmodels/tsa/innovations/__init__.py
 ${PYSITELIB}/statsmodels/tsa/innovations/__init__.pyc
 ${PYSITELIB}/statsmodels/tsa/innovations/__init__.pyo
@@ -2286,6 +2376,7 @@ ${PYSITELIB}/statsmodels/tsa/seasonal.py
 ${PYSITELIB}/statsmodels/tsa/statespace/__init__.py
 ${PYSITELIB}/statsmodels/tsa/statespace/__init__.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/_cfa_simulation_smoother.so
 ${PYSITELIB}/statsmodels/tsa/statespace/_filters/__init__.py
 ${PYSITELIB}/statsmodels/tsa/statespace/_filters/__init__.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/_filters/__init__.pyo
@@ -2299,6 +2390,9 @@ ${PYSITELIB}/statsmodels/tsa/statespace/
 ${PYSITELIB}/statsmodels/tsa/statespace/_pykalman_smoother.py
 ${PYSITELIB}/statsmodels/tsa/statespace/_pykalman_smoother.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/_pykalman_smoother.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/_quarterly_ar1.py
+${PYSITELIB}/statsmodels/tsa/statespace/_quarterly_ar1.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/_quarterly_ar1.pyo
 ${PYSITELIB}/statsmodels/tsa/statespace/_representation.so
 ${PYSITELIB}/statsmodels/tsa/statespace/_simulation_smoother.so
 ${PYSITELIB}/statsmodels/tsa/statespace/_smoothers/__init__.py
@@ -2313,9 +2407,15 @@ ${PYSITELIB}/statsmodels/tsa/statespace/
 ${PYSITELIB}/statsmodels/tsa/statespace/api.py
 ${PYSITELIB}/statsmodels/tsa/statespace/api.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/api.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/cfa_simulation_smoother.py
+${PYSITELIB}/statsmodels/tsa/statespace/cfa_simulation_smoother.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/cfa_simulation_smoother.pyo
 ${PYSITELIB}/statsmodels/tsa/statespace/dynamic_factor.py
 ${PYSITELIB}/statsmodels/tsa/statespace/dynamic_factor.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/dynamic_factor.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/dynamic_factor_mq.py
+${PYSITELIB}/statsmodels/tsa/statespace/dynamic_factor_mq.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/dynamic_factor_mq.pyo
 ${PYSITELIB}/statsmodels/tsa/statespace/exponential_smoothing.py
 ${PYSITELIB}/statsmodels/tsa/statespace/exponential_smoothing.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/exponential_smoothing.pyo
@@ -2331,6 +2431,9 @@ ${PYSITELIB}/statsmodels/tsa/statespace/
 ${PYSITELIB}/statsmodels/tsa/statespace/mlemodel.py
 ${PYSITELIB}/statsmodels/tsa/statespace/mlemodel.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/mlemodel.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/news.py
+${PYSITELIB}/statsmodels/tsa/statespace/news.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/news.pyo
 ${PYSITELIB}/statsmodels/tsa/statespace/representation.py
 ${PYSITELIB}/statsmodels/tsa/statespace/representation.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/representation.pyo
@@ -2352,10 +2455,51 @@ ${PYSITELIB}/statsmodels/tsa/statespace/
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/results/__init__.py
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/results/__init__.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/results/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Omega_11.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Omega_22.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_S10.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_Si0.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_beta.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_invP.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_posterior_mean.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_state_variates.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_v10.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/cfa_tvpvar_vi0.csv
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/results/clark1989.csv
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/results/exponential_smoothing_params.csv
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/results/exponential_smoothing_predict.csv
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/results/exponential_smoothing_states.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/__init__.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/2016-06-29.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/2016-07-29.csv
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/__init__.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/US/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/__init__.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/data/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions/__init__.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/Nowcasting/functions/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/__init__.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/__init__.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/__init__.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_111.mat
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_112.mat
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_11F.mat
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_221.mat
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_222.mat
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_22F.mat
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_111.mat
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_112.mat
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_221.mat
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_dfm_blocks_222.mat
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_112.mat
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_222.mat
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_blocks_112.mat
+${PYSITELIB}/statsmodels/tsa/statespace/tests/results/frbny_nowcast/test_news_blocks_222.mat
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/results/manufac.dta
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_clark1989_R.csv
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_dynamic_factor.py
@@ -2412,6 +2556,15 @@ ${PYSITELIB}/statsmodels/tsa/statespace/
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_wpi1_ar3_stata.csv
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/results/results_wpi1_missing_ar3_matlab_ssm.csv
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/results/sm-0.9-sarimax.pkl
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_cfa_simulation_smoothing.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_cfa_simulation_smoothing.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_cfa_simulation_smoothing.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_cfa_tvpvar.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_cfa_tvpvar.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_cfa_tvpvar.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_chandrasekhar.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_chandrasekhar.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_chandrasekhar.pyo
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_collapsed.py
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_collapsed.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_collapsed.pyo
@@ -2424,6 +2577,15 @@ ${PYSITELIB}/statsmodels/tsa/statespace/
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_dynamic_factor.py
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_dynamic_factor.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_dynamic_factor.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_frbny_nowcast.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_dynamic_factor_mq_monte_carlo.pyo
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.py
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_exact_diffuse_filtering.pyo
@@ -2433,6 +2595,9 @@ ${PYSITELIB}/statsmodels/tsa/statespace/
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_fixed_params.py
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_fixed_params.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_fixed_params.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_forecasting.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_forecasting.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_forecasting.pyo
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_impulse_responses.py
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_impulse_responses.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_impulse_responses.pyo
@@ -2448,6 +2613,12 @@ ${PYSITELIB}/statsmodels/tsa/statespace/
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_models.py
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_models.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_models.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_multivariate_switch_univariate.pyo
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_news.py
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_news.pyc
+${PYSITELIB}/statsmodels/tsa/statespace/tests/test_news.pyo
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_options.py
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_options.pyc
 ${PYSITELIB}/statsmodels/tsa/statespace/tests/test_options.pyo
@@ -2551,8 +2722,10 @@ ${PYSITELIB}/statsmodels/tsa/tests/resul
 ${PYSITELIB}/statsmodels/tsa/tests/results/datamlw_tls.py
 ${PYSITELIB}/statsmodels/tsa/tests/results/datamlw_tls.pyc
 ${PYSITELIB}/statsmodels/tsa/tests/results/datamlw_tls.pyo
+${PYSITELIB}/statsmodels/tsa/tests/results/fit_ets_results.json
+${PYSITELIB}/statsmodels/tsa/tests/results/fit_ets_results_nonseasonal.json
+${PYSITELIB}/statsmodels/tsa/tests/results/fit_ets_results_seasonal.json
 ${PYSITELIB}/statsmodels/tsa/tests/results/gnpdef.csv
-${PYSITELIB}/statsmodels/tsa/tests/results/housing-data.csv
 ${PYSITELIB}/statsmodels/tsa/tests/results/lutkepohl2.dta
 ${PYSITELIB}/statsmodels/tsa/tests/results/make_arma.py
 ${PYSITELIB}/statsmodels/tsa/tests/results/make_arma.pyc
@@ -2615,9 +2788,12 @@ ${PYSITELIB}/statsmodels/tsa/tests/test_
 ${PYSITELIB}/statsmodels/tsa/tests/test_bds.py
 ${PYSITELIB}/statsmodels/tsa/tests/test_bds.pyc
 ${PYSITELIB}/statsmodels/tsa/tests/test_bds.pyo
-${PYSITELIB}/statsmodels/tsa/tests/test_holtwinters.py
-${PYSITELIB}/statsmodels/tsa/tests/test_holtwinters.pyc
-${PYSITELIB}/statsmodels/tsa/tests/test_holtwinters.pyo
+${PYSITELIB}/statsmodels/tsa/tests/test_deterministic.py
+${PYSITELIB}/statsmodels/tsa/tests/test_deterministic.pyc
+${PYSITELIB}/statsmodels/tsa/tests/test_deterministic.pyo
+${PYSITELIB}/statsmodels/tsa/tests/test_exponential_smoothing.py
+${PYSITELIB}/statsmodels/tsa/tests/test_exponential_smoothing.pyc
+${PYSITELIB}/statsmodels/tsa/tests/test_exponential_smoothing.pyo
 ${PYSITELIB}/statsmodels/tsa/tests/test_seasonal.py
 ${PYSITELIB}/statsmodels/tsa/tests/test_seasonal.pyc
 ${PYSITELIB}/statsmodels/tsa/tests/test_seasonal.pyo

Index: pkgsrc/math/py-statsmodels/distinfo
diff -u pkgsrc/math/py-statsmodels/distinfo:1.5 pkgsrc/math/py-statsmodels/distinfo:1.6
--- pkgsrc/math/py-statsmodels/distinfo:1.5     Sun May  3 16:13:11 2020
+++ pkgsrc/math/py-statsmodels/distinfo Tue Apr  6 12:16:47 2021
@@ -1,6 +1,6 @@
-$NetBSD: distinfo,v 1.5 2020/05/03 16:13:11 minskim Exp $
+$NetBSD: distinfo,v 1.6 2021/04/06 12:16:47 prlw1 Exp $
 
-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
+SHA1 (statsmodels-0.12.2.tar.gz) = 3a653a3fbfe9b3c9083193ded85f4875bc9d5b05
+RMD160 (statsmodels-0.12.2.tar.gz) = d8049f589996c1a4f9c443d2303cb466e013c033
+SHA512 (statsmodels-0.12.2.tar.gz) = ae4872bc7300ef564407daa8b4076fd70fc180965622ed2173871579e063e2143e000540089923fe171dbb191b7dd872077d8ba6794fe23390331375ec7ce810
+Size (statsmodels-0.12.2.tar.gz) = 17470078 bytes

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