automatic import of python-pytwoway

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+%global _empty_manifest_terminate_build 0
+Name:		python-pytwoway
+Version:	0.3.21
+Release:	1
+Summary:	Estimate two way fixed effect labor models
+License:	MIT License
+URL:		https://github.com/tlamadon/pytwoway
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/23/9f/6d148b1568ce81513a49c86beed42155b130836882b362a4eae74ad6e437/pytwoway-0.3.21.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-numba
+Requires:	python3-paramsdict
+Requires:	python3-bipartitepandas
+Requires:	python3-scipy
+Requires:	python3-statsmodels
+Requires:	python3-pyamg
+Requires:	python3-qpsolvers
+Requires:	python3-quadprog
+Requires:	python3-ConfigArgParse
+Requires:	python3-matplotlib
+Requires:	python3-tqdm
+
+%description
+`PyTwoWay` is the Python package associated with the following paper:
+"`How Much Should we Trust Estimates of Firm Effects and Worker Sorting? <https://www.nber.org/system/files/working_papers/w27368/w27368.pdf>`_"
+by Stéphane Bonhomme, Kerstin Holzheu, Thibaut Lamadon, Elena Manresa, Magne Mogstad, and Bradley Setzler.
+No. w27368. National Bureau of Economic Research, 2020.
+The package provides implementations for a series of estimators for models with two sided heterogeneity:
+1. two way fixed effect estimator as proposed by `Abowd, Kramarz, and Margolis <https://doi.org/10.1111/1468-0262.00020>`_
+2. homoskedastic bias correction as in `Andrews, et al. <https://doi.org/10.1111/j.1467-985X.2007.00533.x>`_
+3. heteroskedastic bias correction as in `Kline, Saggio, and Sølvsten <https://doi.org/10.3982/ECTA16410>`_
+4. group fixed estimator as in `Bonhomme, Lamadon, and Manresa <https://doi.org/10.3982/ECTA15722>`_
+5. group correlated random effect as presented in the main paper
+6. fixed-point revealed preference estimator as in `Sorkin <https://doi.org/10.1093/qje/qjy001>`_
+7. non-parametric sorting estimator as in `Borovičková and Shimer <https://drive.google.com/file/d/1KW0sZ4nV9bIdVhcs-UW8yW_dzUr782v5/view>`_
+If you want to give it a try, you can start an example notebook for the FE estimator here: |binder_fe| for the CRE estimator here: |binder_cre| for the BLM estimator here: |binder_blm| for the Sorkin estimator here: |binder_sorkin| and for the Borovickova-Shimer estimator here: |binder_bs|. These start fully interactive notebooks with simple examples that simulate data and run the estimators.
+The package provides a Python interface. Installation is handled by `pip` or `Conda` (TBD). The source of the package is available on GitHub at `PyTwoWay <https://github.com/tlamadon/pytwoway>`_. The online documentation is hosted `here <https://tlamadon.github.io/pytwoway/>`_.
+The code is relatively efficient. A benchmark below compares `PyTwoWay`'s speed with that of `LeaveOutTwoWay <https://github.com/rsaggio87/LeaveOutTwoWay/>`_, a MATLAB package for estimating AKM and its bias corrections.
+
+%package -n python3-pytwoway
+Summary:	Estimate two way fixed effect labor models
+Provides:	python-pytwoway
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pytwoway
+`PyTwoWay` is the Python package associated with the following paper:
+"`How Much Should we Trust Estimates of Firm Effects and Worker Sorting? <https://www.nber.org/system/files/working_papers/w27368/w27368.pdf>`_"
+by Stéphane Bonhomme, Kerstin Holzheu, Thibaut Lamadon, Elena Manresa, Magne Mogstad, and Bradley Setzler.
+No. w27368. National Bureau of Economic Research, 2020.
+The package provides implementations for a series of estimators for models with two sided heterogeneity:
+1. two way fixed effect estimator as proposed by `Abowd, Kramarz, and Margolis <https://doi.org/10.1111/1468-0262.00020>`_
+2. homoskedastic bias correction as in `Andrews, et al. <https://doi.org/10.1111/j.1467-985X.2007.00533.x>`_
+3. heteroskedastic bias correction as in `Kline, Saggio, and Sølvsten <https://doi.org/10.3982/ECTA16410>`_
+4. group fixed estimator as in `Bonhomme, Lamadon, and Manresa <https://doi.org/10.3982/ECTA15722>`_
+5. group correlated random effect as presented in the main paper
+6. fixed-point revealed preference estimator as in `Sorkin <https://doi.org/10.1093/qje/qjy001>`_
+7. non-parametric sorting estimator as in `Borovičková and Shimer <https://drive.google.com/file/d/1KW0sZ4nV9bIdVhcs-UW8yW_dzUr782v5/view>`_
+If you want to give it a try, you can start an example notebook for the FE estimator here: |binder_fe| for the CRE estimator here: |binder_cre| for the BLM estimator here: |binder_blm| for the Sorkin estimator here: |binder_sorkin| and for the Borovickova-Shimer estimator here: |binder_bs|. These start fully interactive notebooks with simple examples that simulate data and run the estimators.
+The package provides a Python interface. Installation is handled by `pip` or `Conda` (TBD). The source of the package is available on GitHub at `PyTwoWay <https://github.com/tlamadon/pytwoway>`_. The online documentation is hosted `here <https://tlamadon.github.io/pytwoway/>`_.
+The code is relatively efficient. A benchmark below compares `PyTwoWay`'s speed with that of `LeaveOutTwoWay <https://github.com/rsaggio87/LeaveOutTwoWay/>`_, a MATLAB package for estimating AKM and its bias corrections.
+
+%package help
+Summary:	Development documents and examples for pytwoway
+Provides:	python3-pytwoway-doc
+%description help
+`PyTwoWay` is the Python package associated with the following paper:
+"`How Much Should we Trust Estimates of Firm Effects and Worker Sorting? <https://www.nber.org/system/files/working_papers/w27368/w27368.pdf>`_"
+by Stéphane Bonhomme, Kerstin Holzheu, Thibaut Lamadon, Elena Manresa, Magne Mogstad, and Bradley Setzler.
+No. w27368. National Bureau of Economic Research, 2020.
+The package provides implementations for a series of estimators for models with two sided heterogeneity:
+1. two way fixed effect estimator as proposed by `Abowd, Kramarz, and Margolis <https://doi.org/10.1111/1468-0262.00020>`_
+2. homoskedastic bias correction as in `Andrews, et al. <https://doi.org/10.1111/j.1467-985X.2007.00533.x>`_
+3. heteroskedastic bias correction as in `Kline, Saggio, and Sølvsten <https://doi.org/10.3982/ECTA16410>`_
+4. group fixed estimator as in `Bonhomme, Lamadon, and Manresa <https://doi.org/10.3982/ECTA15722>`_
+5. group correlated random effect as presented in the main paper
+6. fixed-point revealed preference estimator as in `Sorkin <https://doi.org/10.1093/qje/qjy001>`_
+7. non-parametric sorting estimator as in `Borovičková and Shimer <https://drive.google.com/file/d/1KW0sZ4nV9bIdVhcs-UW8yW_dzUr782v5/view>`_
+If you want to give it a try, you can start an example notebook for the FE estimator here: |binder_fe| for the CRE estimator here: |binder_cre| for the BLM estimator here: |binder_blm| for the Sorkin estimator here: |binder_sorkin| and for the Borovickova-Shimer estimator here: |binder_bs|. These start fully interactive notebooks with simple examples that simulate data and run the estimators.
+The package provides a Python interface. Installation is handled by `pip` or `Conda` (TBD). The source of the package is available on GitHub at `PyTwoWay <https://github.com/tlamadon/pytwoway>`_. The online documentation is hosted `here <https://tlamadon.github.io/pytwoway/>`_.
+The code is relatively efficient. A benchmark below compares `PyTwoWay`'s speed with that of `LeaveOutTwoWay <https://github.com/rsaggio87/LeaveOutTwoWay/>`_, a MATLAB package for estimating AKM and its bias corrections.
+
+%prep
+%autosetup -n pytwoway-0.3.21
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-pytwoway -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.3.21-1
+- Package Spec generated