%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 [![Python package](https://github.com/jamalsenouci/causalimpact/actions/workflows/main.yml/badge.svg)](https://github.com/jamalsenouci/causalimpact/actions/workflows/main.yml) [![codecov](https://codecov.io/gh/jamalsenouci/causalimpact/branch/master/graph/badge.svg?token=EIPC36VQHS)](https://codecov.io/gh/jamalsenouci/causalimpact) ![monthly downloads](https://pepy.tech/badge/causalimpact/month) [![DeepSource](https://deepsource.io/gh/jamalsenouci/causalimpact.svg/?label=active+issues&show_trend=true&token=R5aIDSkIId_5THWTAPKccjcH)](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 [![Binder](https://mybinder.org/badge_logo.svg)](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 [![Python package](https://github.com/jamalsenouci/causalimpact/actions/workflows/main.yml/badge.svg)](https://github.com/jamalsenouci/causalimpact/actions/workflows/main.yml) [![codecov](https://codecov.io/gh/jamalsenouci/causalimpact/branch/master/graph/badge.svg?token=EIPC36VQHS)](https://codecov.io/gh/jamalsenouci/causalimpact) ![monthly downloads](https://pepy.tech/badge/causalimpact/month) [![DeepSource](https://deepsource.io/gh/jamalsenouci/causalimpact.svg/?label=active+issues&show_trend=true&token=R5aIDSkIId_5THWTAPKccjcH)](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 [![Binder](https://mybinder.org/badge_logo.svg)](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 [![Python package](https://github.com/jamalsenouci/causalimpact/actions/workflows/main.yml/badge.svg)](https://github.com/jamalsenouci/causalimpact/actions/workflows/main.yml) [![codecov](https://codecov.io/gh/jamalsenouci/causalimpact/branch/master/graph/badge.svg?token=EIPC36VQHS)](https://codecov.io/gh/jamalsenouci/causalimpact) ![monthly downloads](https://pepy.tech/badge/causalimpact/month) [![DeepSource](https://deepsource.io/gh/jamalsenouci/causalimpact.svg/?label=active+issues&show_trend=true&token=R5aIDSkIId_5THWTAPKccjcH)](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 [![Binder](https://mybinder.org/badge_logo.svg)](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 25 2023 Python_Bot - 0.2.6-1 - Package Spec generated