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%global _empty_manifest_terminate_build 0
Name: python-causalimpact
Version: 0.2.6
Release: 1
Summary: Python Package for causal inference using Bayesian structural time-series models
License: MIT
URL: https://github.com/jamalsenouci/causalimpact/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f3/d7/891fcea5579f47477b320c1c8e9f8625d0daa659ad7f09654dfee7eef3f4/causalimpact-0.2.6.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-numpy
Requires: python3-statsmodels
Requires: python3-matplotlib
Requires: python3-pymc
Requires: python3-pytensor
Requires: python3-importlib-metadata
Requires: python3-setuptools
Requires: python3-pytest
Requires: python3-pytest-cov
%description
## CausalImpact
[](https://github.com/jamalsenouci/causalimpact/actions/workflows/main.yml)
[](https://codecov.io/gh/jamalsenouci/causalimpact)
[](https://deepsource.io/gh/jamalsenouci/causalimpact/?ref=repository-badge)
#### A Python package for causal inference using Bayesian structural time-series models
This is a port of the R package CausalImpact, see: https://github.com/google/CausalImpact.
This package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package aims to address this difficulty using a structural Bayesian time-series model to estimate how the response metric might have evolved after the intervention if the intervention had not occurred.
As with all approaches to causal inference on non-experimental data, valid conclusions require strong assumptions. The CausalImpact package, in particular, assumes that the outcome time series can be explained in terms of a set of control time series that were themselves not affected by the intervention. Furthermore, the relation between treated series and control series is assumed to be stable during the post-intervention period. Understanding and checking these assumptions for any given application is critical for obtaining valid conclusions.
#### Try it out in the browser
[](https://mybinder.org/v2/gh/jamalsenouci/causalimpact/HEAD?labpath=GettingStarted.ipynb)
#### Installation
install the latest release via pip
```bash
pip install causalimpact
```
#### Getting started
[Documentation and examples](https://nbviewer.org/github/jamalsenouci/causalimpact/blob/master/GettingStarted.ipynb)
#### Further resources
- Manuscript: [Brodersen et al., Annals of Applied Statistics (2015)](http://research.google.com/pubs/pub41854.html)
#### Bugs
The issue tracker is at https://github.com/jamalsenouci/causalimpact/issues. Please report any bugs that you find. Or, even better, fork the repository on GitHub and create a pull request.
%package -n python3-causalimpact
Summary: Python Package for causal inference using Bayesian structural time-series models
Provides: python-causalimpact
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-causalimpact
## CausalImpact
[](https://github.com/jamalsenouci/causalimpact/actions/workflows/main.yml)
[](https://codecov.io/gh/jamalsenouci/causalimpact)
[](https://deepsource.io/gh/jamalsenouci/causalimpact/?ref=repository-badge)
#### A Python package for causal inference using Bayesian structural time-series models
This is a port of the R package CausalImpact, see: https://github.com/google/CausalImpact.
This package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package aims to address this difficulty using a structural Bayesian time-series model to estimate how the response metric might have evolved after the intervention if the intervention had not occurred.
As with all approaches to causal inference on non-experimental data, valid conclusions require strong assumptions. The CausalImpact package, in particular, assumes that the outcome time series can be explained in terms of a set of control time series that were themselves not affected by the intervention. Furthermore, the relation between treated series and control series is assumed to be stable during the post-intervention period. Understanding and checking these assumptions for any given application is critical for obtaining valid conclusions.
#### Try it out in the browser
[](https://mybinder.org/v2/gh/jamalsenouci/causalimpact/HEAD?labpath=GettingStarted.ipynb)
#### Installation
install the latest release via pip
```bash
pip install causalimpact
```
#### Getting started
[Documentation and examples](https://nbviewer.org/github/jamalsenouci/causalimpact/blob/master/GettingStarted.ipynb)
#### Further resources
- Manuscript: [Brodersen et al., Annals of Applied Statistics (2015)](http://research.google.com/pubs/pub41854.html)
#### Bugs
The issue tracker is at https://github.com/jamalsenouci/causalimpact/issues. Please report any bugs that you find. Or, even better, fork the repository on GitHub and create a pull request.
%package help
Summary: Development documents and examples for causalimpact
Provides: python3-causalimpact-doc
%description help
## CausalImpact
[](https://github.com/jamalsenouci/causalimpact/actions/workflows/main.yml)
[](https://codecov.io/gh/jamalsenouci/causalimpact)
[](https://deepsource.io/gh/jamalsenouci/causalimpact/?ref=repository-badge)
#### A Python package for causal inference using Bayesian structural time-series models
This is a port of the R package CausalImpact, see: https://github.com/google/CausalImpact.
This package implements an approach to estimating the causal effect of a designed intervention on a time series. For example, how many additional daily clicks were generated by an advertising campaign? Answering a question like this can be difficult when a randomized experiment is not available. The package aims to address this difficulty using a structural Bayesian time-series model to estimate how the response metric might have evolved after the intervention if the intervention had not occurred.
As with all approaches to causal inference on non-experimental data, valid conclusions require strong assumptions. The CausalImpact package, in particular, assumes that the outcome time series can be explained in terms of a set of control time series that were themselves not affected by the intervention. Furthermore, the relation between treated series and control series is assumed to be stable during the post-intervention period. Understanding and checking these assumptions for any given application is critical for obtaining valid conclusions.
#### Try it out in the browser
[](https://mybinder.org/v2/gh/jamalsenouci/causalimpact/HEAD?labpath=GettingStarted.ipynb)
#### Installation
install the latest release via pip
```bash
pip install causalimpact
```
#### Getting started
[Documentation and examples](https://nbviewer.org/github/jamalsenouci/causalimpact/blob/master/GettingStarted.ipynb)
#### Further resources
- Manuscript: [Brodersen et al., Annals of Applied Statistics (2015)](http://research.google.com/pubs/pub41854.html)
#### Bugs
The issue tracker is at https://github.com/jamalsenouci/causalimpact/issues. Please report any bugs that you find. Or, even better, fork the repository on GitHub and create a pull request.
%prep
%autosetup -n causalimpact-0.2.6
%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-causalimpact -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.6-1
- Package Spec generated
|
