%global _empty_manifest_terminate_build 0
Name:		python-targeted
Version:	0.0.30
Release:	1
Summary:	Python package for targeted inference.
License:	Apache Software License
URL:		https://targetlib.org/python/
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/69/41/eb97302d2ab4912cfb04c6f4bb29e690321e4c52490c314ee5b4808df1c3/targeted-0.0.30.tar.gz
BuildArch:	noarch


%description
# Targeted Learning Library

Python package for targeted inference.

**targeted** provides a number of methods for semi-parametric
estimation.  The library also contains implementations of various
parametric models (including different discrete choice models) and
model diagnostics tools.

The implemention currently includes
- **Risk regression models** with binary exposure
  (Richardson et al., 2017, doi:10.1080/01621459.2016.1192546)
- **Augmented Inverse Probability Weighted** estimators for missing
  data and causal inference (Bang and Robins, 2005,
  doi:10.1111/j.1541-0420.2005.00377.x)
- Model diagnostics based on **cumulative residuals** methods
- Efficient weighted **Pooled Adjacent Violator Algorithms**
- **Nested multinomial logit** models

Documentation and tutorials can be found at https://targetlib.org/python/.

%package -n python3-targeted
Summary:	Python package for targeted inference.
Provides:	python-targeted
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-targeted
# Targeted Learning Library

Python package for targeted inference.

**targeted** provides a number of methods for semi-parametric
estimation.  The library also contains implementations of various
parametric models (including different discrete choice models) and
model diagnostics tools.

The implemention currently includes
- **Risk regression models** with binary exposure
  (Richardson et al., 2017, doi:10.1080/01621459.2016.1192546)
- **Augmented Inverse Probability Weighted** estimators for missing
  data and causal inference (Bang and Robins, 2005,
  doi:10.1111/j.1541-0420.2005.00377.x)
- Model diagnostics based on **cumulative residuals** methods
- Efficient weighted **Pooled Adjacent Violator Algorithms**
- **Nested multinomial logit** models

Documentation and tutorials can be found at https://targetlib.org/python/.

%package help
Summary:	Development documents and examples for targeted
Provides:	python3-targeted-doc
%description help
# Targeted Learning Library

Python package for targeted inference.

**targeted** provides a number of methods for semi-parametric
estimation.  The library also contains implementations of various
parametric models (including different discrete choice models) and
model diagnostics tools.

The implemention currently includes
- **Risk regression models** with binary exposure
  (Richardson et al., 2017, doi:10.1080/01621459.2016.1192546)
- **Augmented Inverse Probability Weighted** estimators for missing
  data and causal inference (Bang and Robins, 2005,
  doi:10.1111/j.1541-0420.2005.00377.x)
- Model diagnostics based on **cumulative residuals** methods
- Efficient weighted **Pooled Adjacent Violator Algorithms**
- **Nested multinomial logit** models

Documentation and tutorials can be found at https://targetlib.org/python/.

%prep
%autosetup -n targeted-0.0.30

%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-targeted -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.30-1
- Package Spec generated