%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](docs/images/zepid_logo.png) # zEpid [![PyPI version](https://badge.fury.io/py/zepid.svg)](https://badge.fury.io/py/zepid) [![Python package](https://github.com/pzivich/zEpid/actions/workflows/python-package.yml/badge.svg)](https://github.com/pzivich/zEpid/actions/workflows/python-package.yml) [![Documentation Status](https://readthedocs.org/projects/zepid/badge/?version=latest)](https://zepid.readthedocs.io/en/latest/?badge=latest) [![Join the chat at https://gitter.im/zEpid/community](https://badges.gitter.im/zEpid/community.svg)](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](docs/images/zepid_logo.png) # zEpid [![PyPI version](https://badge.fury.io/py/zepid.svg)](https://badge.fury.io/py/zepid) [![Python package](https://github.com/pzivich/zEpid/actions/workflows/python-package.yml/badge.svg)](https://github.com/pzivich/zEpid/actions/workflows/python-package.yml) [![Documentation Status](https://readthedocs.org/projects/zepid/badge/?version=latest)](https://zepid.readthedocs.io/en/latest/?badge=latest) [![Join the chat at https://gitter.im/zEpid/community](https://badges.gitter.im/zEpid/community.svg)](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](docs/images/zepid_logo.png) # zEpid [![PyPI version](https://badge.fury.io/py/zepid.svg)](https://badge.fury.io/py/zepid) [![Python package](https://github.com/pzivich/zEpid/actions/workflows/python-package.yml/badge.svg)](https://github.com/pzivich/zEpid/actions/workflows/python-package.yml) [![Documentation Status](https://readthedocs.org/projects/zepid/badge/?version=latest)](https://zepid.readthedocs.io/en/latest/?badge=latest) [![Join the chat at https://gitter.im/zEpid/community](https://badges.gitter.im/zEpid/community.svg)](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 - 0.9.1-1 - Package Spec generated