Metadata-Version: 1.1
Name: zepid
Version: 0.8.2
Summary: Tool package for epidemiologic analyses
Home-page: https://github.com/pzivich/zepid
Author: Paul Zivich
Author-email: zepidpy@gmail.com
License: MIT
Description: ![zepid](docs/images/zepid_logo.png)
        # zEpid
        
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        [![Build Status](https://travis-ci.com/pzivich/zEpid.svg?branch=master)](https://travis-ci.com/pzivich/zEpid)
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        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
        
Keywords: epidemiology inverse-probability-weights risk-ratio g-computation g-formula IPW AIPW TMLE
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
