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%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
|
