diff options
Diffstat (limited to 'python-zepid.spec')
| -rw-r--r-- | python-zepid.spec | 357 |
1 files changed, 357 insertions, 0 deletions
diff --git a/python-zepid.spec b/python-zepid.spec new file mode 100644 index 0000000..085beea --- /dev/null +++ b/python-zepid.spec @@ -0,0 +1,357 @@ +%global _empty_manifest_terminate_build 0 +Name: python-zepid +Version: 0.9.1 +Release: 1 +Summary: Tool package for epidemiologic analyses +License: MIT +URL: https://github.com/pzivich/zepid +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/2b/d8/a357673e807d3cc644f8a470a3c28d287788a128646b8fa7085e62f62f6c/zepid-0.9.1.tar.gz +BuildArch: noarch + +Requires: python3-pandas +Requires: python3-numpy +Requires: python3-statsmodels +Requires: python3-matplotlib +Requires: python3-scipy +Requires: python3-tabulate +Requires: python3-scikit-learn +Requires: python3-patsy +Requires: python3-networkx + +%description + +# zEpid + +[](https://badge.fury.io/py/zepid) +[](https://github.com/pzivich/zEpid/actions/workflows/python-package.yml) +[](https://zepid.readthedocs.io/en/latest/?badge=latest) +[](https://gitter.im/zEpid/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) + +zEpid is an epidemiology analysis package, providing easy to use tools for epidemiologists coding in Python 3.5+. The +purpose of this library is to provide a toolset to make epidemiology e-z. A variety of calculations and plots can be +generated through various functions. For a sample walkthrough of what this library is capable of, please look at the +tutorials available at https://github.com/pzivich/Python-for-Epidemiologists + +A few highlights: basic epidemiology calculations, easily create functional form assessment plots, +easily create effect measure plots, and causal inference tools. Implemented estimators include; inverse +probability of treatment weights, inverse probability of censoring weights, inverse probabilitiy of missing weights, +augmented inverse probability of treatment weights, time-fixed g-formula, Monte Carlo g-formula, Iterative conditional +g-formula, and targeted maximum likelihood (TMLE). Additionally, generalizability/transportability tools are available +including; inverse probability of sampling weights, g-transport formula, and doubly robust +generalizability/transportability formulas. + +If you have any requests for items to be included, please contact me and I will work on adding any requested features. +You can contact me either through GitHub (https://github.com/pzivich), email (gmail: zepidpy), or twitter (@zepidpy). + +# Installation + +## Installing: +You can install zEpid using `pip install zepid` + +## Dependencies: +pandas >= 0.18.0, numpy, statsmodels >= 0.7.0, matplotlib >= 2.0, scipy, tabulate + +# Module Features + +## Measures +Calculate measures directly from a pandas dataframe object. Implemented measures include; risk ratio, risk difference, +odds ratio, incidence rate ratio, incidence rate difference, number needed to treat, sensitivity, specificity, +population attributable fraction, attributable community risk + +Measures can be directly calculated from a pandas DataFrame object or using summary data. + +Other handy features include; splines, Table 1 generator, interaction contrast, interaction contrast ratio, positive +predictive value, negative predictive value, screening cost analyzer, counternull p-values, convert odds to +proportions, convert proportions to odds + +For guided tutorials with Jupyter Notebooks: +https://github.com/pzivich/Python-for-Epidemiologists/blob/master/3_Epidemiology_Analysis/a_basics/1_basic_measures.ipynb + +## Graphics +Uses matplotlib in the background to generate some useful plots. Implemented plots include; functional form assessment +(with statsmodels output), p-value function plots, spaghetti plot, effect measure plot (forest plot), receiver-operator +curve, dynamic risk plots, and L'Abbe plots + +For examples see: +http://zepid.readthedocs.io/en/latest/Graphics.html + +## Causal +The causal branch includes various estimators for causal inference with observational data. Details on currently +implemented estimators are below: + +### G-Computation Algorithm +Current implementation includes; time-fixed exposure g-formula, Monte Carlo g-formula, and iterative conditional +g-formula + +### Inverse Probability Weights +Current implementation includes; IP Treatment W, IP Censoring W, IP Missing W. Diagnostics are also available for IPTW. +IPMW supports monotone missing data + +### Augmented Inverse Probability Weights +Current implementation includes the augmented-IPTW estimator described by Funk et al 2011 AJE + +### Targeted Maximum Likelihood Estimator +TMLE can be estimated through standard logistic regression model, or through user-input functions. Alternatively, users +can input machine learning algorithms to estimate probabilities. Supported machine learning algorithms include `sklearn` + +### Generalizability / Transportability +For generalizing results or transporting to a different target population, several estimators are available. These +include inverse probability of sampling weights, g-transport formula, and doubly robust formulas + +Tutorials for the usage of these estimators are available at: +https://github.com/pzivich/Python-for-Epidemiologists/tree/master/3_Epidemiology_Analysis/c_causal_inference + +#### G-estimation of Structural Nested Mean Models +Single time-point g-estimation of structural nested mean models are supported. + +## Sensitivity Analyses +Includes trapezoidal distribution generator, corrected Risk Ratio + +Tutorials are available at: +https://github.com/pzivich/Python-for-Epidemiologists/tree/master/3_Epidemiology_Analysis/d_sensitivity_analyses + + + + +%package -n python3-zepid +Summary: Tool package for epidemiologic analyses +Provides: python-zepid +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-zepid + +# zEpid + +[](https://badge.fury.io/py/zepid) +[](https://github.com/pzivich/zEpid/actions/workflows/python-package.yml) +[](https://zepid.readthedocs.io/en/latest/?badge=latest) +[](https://gitter.im/zEpid/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) + +zEpid is an epidemiology analysis package, providing easy to use tools for epidemiologists coding in Python 3.5+. The +purpose of this library is to provide a toolset to make epidemiology e-z. A variety of calculations and plots can be +generated through various functions. For a sample walkthrough of what this library is capable of, please look at the +tutorials available at https://github.com/pzivich/Python-for-Epidemiologists + +A few highlights: basic epidemiology calculations, easily create functional form assessment plots, +easily create effect measure plots, and causal inference tools. Implemented estimators include; inverse +probability of treatment weights, inverse probability of censoring weights, inverse probabilitiy of missing weights, +augmented inverse probability of treatment weights, time-fixed g-formula, Monte Carlo g-formula, Iterative conditional +g-formula, and targeted maximum likelihood (TMLE). Additionally, generalizability/transportability tools are available +including; inverse probability of sampling weights, g-transport formula, and doubly robust +generalizability/transportability formulas. + +If you have any requests for items to be included, please contact me and I will work on adding any requested features. +You can contact me either through GitHub (https://github.com/pzivich), email (gmail: zepidpy), or twitter (@zepidpy). + +# Installation + +## Installing: +You can install zEpid using `pip install zepid` + +## Dependencies: +pandas >= 0.18.0, numpy, statsmodels >= 0.7.0, matplotlib >= 2.0, scipy, tabulate + +# Module Features + +## Measures +Calculate measures directly from a pandas dataframe object. Implemented measures include; risk ratio, risk difference, +odds ratio, incidence rate ratio, incidence rate difference, number needed to treat, sensitivity, specificity, +population attributable fraction, attributable community risk + +Measures can be directly calculated from a pandas DataFrame object or using summary data. + +Other handy features include; splines, Table 1 generator, interaction contrast, interaction contrast ratio, positive +predictive value, negative predictive value, screening cost analyzer, counternull p-values, convert odds to +proportions, convert proportions to odds + +For guided tutorials with Jupyter Notebooks: +https://github.com/pzivich/Python-for-Epidemiologists/blob/master/3_Epidemiology_Analysis/a_basics/1_basic_measures.ipynb + +## Graphics +Uses matplotlib in the background to generate some useful plots. Implemented plots include; functional form assessment +(with statsmodels output), p-value function plots, spaghetti plot, effect measure plot (forest plot), receiver-operator +curve, dynamic risk plots, and L'Abbe plots + +For examples see: +http://zepid.readthedocs.io/en/latest/Graphics.html + +## Causal +The causal branch includes various estimators for causal inference with observational data. Details on currently +implemented estimators are below: + +### G-Computation Algorithm +Current implementation includes; time-fixed exposure g-formula, Monte Carlo g-formula, and iterative conditional +g-formula + +### Inverse Probability Weights +Current implementation includes; IP Treatment W, IP Censoring W, IP Missing W. Diagnostics are also available for IPTW. +IPMW supports monotone missing data + +### Augmented Inverse Probability Weights +Current implementation includes the augmented-IPTW estimator described by Funk et al 2011 AJE + +### Targeted Maximum Likelihood Estimator +TMLE can be estimated through standard logistic regression model, or through user-input functions. Alternatively, users +can input machine learning algorithms to estimate probabilities. Supported machine learning algorithms include `sklearn` + +### Generalizability / Transportability +For generalizing results or transporting to a different target population, several estimators are available. These +include inverse probability of sampling weights, g-transport formula, and doubly robust formulas + +Tutorials for the usage of these estimators are available at: +https://github.com/pzivich/Python-for-Epidemiologists/tree/master/3_Epidemiology_Analysis/c_causal_inference + +#### G-estimation of Structural Nested Mean Models +Single time-point g-estimation of structural nested mean models are supported. + +## Sensitivity Analyses +Includes trapezoidal distribution generator, corrected Risk Ratio + +Tutorials are available at: +https://github.com/pzivich/Python-for-Epidemiologists/tree/master/3_Epidemiology_Analysis/d_sensitivity_analyses + + + + +%package help +Summary: Development documents and examples for zepid +Provides: python3-zepid-doc +%description help + +# zEpid + +[](https://badge.fury.io/py/zepid) +[](https://github.com/pzivich/zEpid/actions/workflows/python-package.yml) +[](https://zepid.readthedocs.io/en/latest/?badge=latest) +[](https://gitter.im/zEpid/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) + +zEpid is an epidemiology analysis package, providing easy to use tools for epidemiologists coding in Python 3.5+. The +purpose of this library is to provide a toolset to make epidemiology e-z. A variety of calculations and plots can be +generated through various functions. For a sample walkthrough of what this library is capable of, please look at the +tutorials available at https://github.com/pzivich/Python-for-Epidemiologists + +A few highlights: basic epidemiology calculations, easily create functional form assessment plots, +easily create effect measure plots, and causal inference tools. Implemented estimators include; inverse +probability of treatment weights, inverse probability of censoring weights, inverse probabilitiy of missing weights, +augmented inverse probability of treatment weights, time-fixed g-formula, Monte Carlo g-formula, Iterative conditional +g-formula, and targeted maximum likelihood (TMLE). Additionally, generalizability/transportability tools are available +including; inverse probability of sampling weights, g-transport formula, and doubly robust +generalizability/transportability formulas. + +If you have any requests for items to be included, please contact me and I will work on adding any requested features. +You can contact me either through GitHub (https://github.com/pzivich), email (gmail: zepidpy), or twitter (@zepidpy). + +# Installation + +## Installing: +You can install zEpid using `pip install zepid` + +## Dependencies: +pandas >= 0.18.0, numpy, statsmodels >= 0.7.0, matplotlib >= 2.0, scipy, tabulate + +# Module Features + +## Measures +Calculate measures directly from a pandas dataframe object. Implemented measures include; risk ratio, risk difference, +odds ratio, incidence rate ratio, incidence rate difference, number needed to treat, sensitivity, specificity, +population attributable fraction, attributable community risk + +Measures can be directly calculated from a pandas DataFrame object or using summary data. + +Other handy features include; splines, Table 1 generator, interaction contrast, interaction contrast ratio, positive +predictive value, negative predictive value, screening cost analyzer, counternull p-values, convert odds to +proportions, convert proportions to odds + +For guided tutorials with Jupyter Notebooks: +https://github.com/pzivich/Python-for-Epidemiologists/blob/master/3_Epidemiology_Analysis/a_basics/1_basic_measures.ipynb + +## Graphics +Uses matplotlib in the background to generate some useful plots. Implemented plots include; functional form assessment +(with statsmodels output), p-value function plots, spaghetti plot, effect measure plot (forest plot), receiver-operator +curve, dynamic risk plots, and L'Abbe plots + +For examples see: +http://zepid.readthedocs.io/en/latest/Graphics.html + +## Causal +The causal branch includes various estimators for causal inference with observational data. Details on currently +implemented estimators are below: + +### G-Computation Algorithm +Current implementation includes; time-fixed exposure g-formula, Monte Carlo g-formula, and iterative conditional +g-formula + +### Inverse Probability Weights +Current implementation includes; IP Treatment W, IP Censoring W, IP Missing W. Diagnostics are also available for IPTW. +IPMW supports monotone missing data + +### Augmented Inverse Probability Weights +Current implementation includes the augmented-IPTW estimator described by Funk et al 2011 AJE + +### Targeted Maximum Likelihood Estimator +TMLE can be estimated through standard logistic regression model, or through user-input functions. Alternatively, users +can input machine learning algorithms to estimate probabilities. Supported machine learning algorithms include `sklearn` + +### Generalizability / Transportability +For generalizing results or transporting to a different target population, several estimators are available. These +include inverse probability of sampling weights, g-transport formula, and doubly robust formulas + +Tutorials for the usage of these estimators are available at: +https://github.com/pzivich/Python-for-Epidemiologists/tree/master/3_Epidemiology_Analysis/c_causal_inference + +#### G-estimation of Structural Nested Mean Models +Single time-point g-estimation of structural nested mean models are supported. + +## Sensitivity Analyses +Includes trapezoidal distribution generator, corrected Risk Ratio + +Tutorials are available at: +https://github.com/pzivich/Python-for-Epidemiologists/tree/master/3_Epidemiology_Analysis/d_sensitivity_analyses + + + + +%prep +%autosetup -n zepid-0.9.1 + +%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-zepid -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9.1-1 +- Package Spec generated |