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        "author": "Paul Zivich",
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        "description": "![zepid](docs/images/zepid_logo.png)\n# zEpid\n\n[![PyPI version](https://badge.fury.io/py/zepid.svg)](https://badge.fury.io/py/zepid)\n[![Build Status](https://travis-ci.com/pzivich/zEpid.svg?branch=master)](https://travis-ci.com/pzivich/zEpid)\n[![Documentation Status](https://readthedocs.org/projects/zepid/badge/?version=latest)](https://zepid.readthedocs.io/en/latest/?badge=latest)\n[![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)\n\nzEpid is an epidemiology analysis package, providing easy to use tools for epidemiologists coding in Python 3.5+. The \npurpose of this library is to provide a toolset to make epidemiology e-z. A variety of calculations and plots can be \ngenerated through various functions. For a sample walkthrough of what this library is capable of, please look at the \ntutorials available at https://github.com/pzivich/Python-for-Epidemiologists\n\nA few highlights: basic epidemiology calculations, easily create functional form assessment plots, \neasily create effect measure plots, and causal inference tools. Implemented estimators include; inverse \nprobability of treatment weights, inverse probability of censoring weights, inverse probabilitiy of missing weights, \naugmented inverse probability of treatment weights, time-fixed g-formula, Monte Carlo g-formula, Iterative conditional \ng-formula, and targeted maximum likelihood (TMLE). Additionally, generalizability/transportability tools are available \nincluding; inverse probability of sampling weights, g-transport formula, and doubly robust \ngeneralizability/transportability formulas.\n\nIf you have any requests for items to be included, please contact me and I will work on adding any requested features. \nYou can contact me either through GitHub (https://github.com/pzivich), email (gmail: zepidpy), or twitter (@zepidpy).\n\n# Installation\n\n## Installing:\nYou can install zEpid using `pip install zepid`\n\n## Dependencies:\npandas >= 0.18.0, numpy, statsmodels >= 0.7.0, matplotlib >= 2.0, scipy, tabulate\n\n# Module Features\n\n## Measures\nCalculate measures directly from a pandas dataframe object. Implemented measures include; risk ratio, risk difference, \nodds ratio, incidence rate ratio, incidence rate difference, number needed to treat, sensitivity, specificity, \npopulation attributable fraction, attributable community risk\n\nMeasures can be directly calculated from a pandas DataFrame object or using summary data.\n\nOther handy features include; splines, Table 1 generator, interaction contrast, interaction contrast ratio, positive \npredictive value, negative predictive value, screening cost analyzer, counternull p-values, convert odds to \nproportions, convert proportions to odds\n\nFor guided tutorials with Jupyter Notebooks:\nhttps://github.com/pzivich/Python-for-Epidemiologists/blob/master/3_Epidemiology_Analysis/a_basics/1_basic_measures.ipynb\n\n## Graphics\nUses matplotlib in the background to generate some useful plots. Implemented plots include; functional form assessment \n(with statsmodels output), p-value function plots, spaghetti plot, effect measure plot (forest plot), receiver-operator \ncurve, dynamic risk plots, and L'Abbe plots\n\nFor examples see:\nhttp://zepid.readthedocs.io/en/latest/Graphics.html\n\n## Causal\nThe causal branch includes various estimators for causal inference with observational data. Details on currently \nimplemented estimators are below:\n\n### G-Computation Algorithm\nCurrent implementation includes; time-fixed exposure g-formula, Monte Carlo g-formula, and iterative conditional \ng-formula\n\n### Inverse Probability Weights \nCurrent implementation includes; IP Treatment W, IP Censoring W, IP Missing W. Diagnostics are also available for IPTW. \nIPMW supports monotone missing data\n\n### Augmented Inverse Probability Weights\nCurrent implementation includes the augmented-IPTW estimator described by Funk et al 2011 AJE\n\n### Targeted Maximum Likelihood Estimator\nTMLE can be estimated through standard logistic regression model, or through user-input functions. Alternatively, users \ncan input machine learning algorithms to estimate probabilities. Supported machine learning algorithms include `sklearn`\n\n### Generalizability / Transportability\nFor generalizing results or transporting to a different target population, several estimators are available. These \ninclude inverse probability of sampling weights, g-transport formula, and doubly robust formulas\n\nTutorials for the usage of these estimators are available at:\nhttps://github.com/pzivich/Python-for-Epidemiologists/tree/master/3_Epidemiology_Analysis/c_causal_inference\n\n#### G-estimation of Structural Nested Mean Models\nSingle time-point g-estimation of structural nested mean models are supported.\n\n## Sensitivity Analyses\nIncludes trapezoidal distribution generator, corrected Risk Ratio\n\nTutorials are available at:\nhttps://github.com/pzivich/Python-for-Epidemiologists/tree/master/3_Epidemiology_Analysis/d_sensitivity_analyses\n\n\n",
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