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|
%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.aliyun.com/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
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9.1-1
- Package Spec generated
|
