Metadata-Version: 1.1
Name: zepid
Version: 0.5.2
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
        
        [![PyPI version](https://badge.fury.io/py/zepid.svg)](https://badge.fury.io/py/zepid)
        [![Build Status](https://travis-ci.com/pzivich/zEpid.svg?branch=master)](https://travis-ci.com/pzivich/zEpid)
        [![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 python3. 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 to the 
        introduction to Python 3 for epidemiologists 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, generate and conduct diagnostic tests. Implemented estimators include; inverse 
        probability of treatment weights, inverse probability of censoring weights, inverse probabilitiy of missing weights, 
        augmented inverse probability weights, time-fixed g-formula, Monte Carlo g-formula, Iterative conditional g-formula, 
        and targeted maximum likelihood (TMLE)
        
        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, standardized mean difference
        
        Other handy features include; splines, Table 1 generator, interaction contrast, interaction contrast ratio
        
        For a narrative description:
        http://zepid.readthedocs.io/en/latest/Measures.html
        
        For guided tutorials with Jupyter Notebooks:
        https://github.com/pzivich/Python-for-Epidemiologists/blob/master/3_Epidemiology_Analysis/a_basics/1_basic_measures.ipynb
        
        ## Calculator
        Calculate measures from summary data. Functions that calculate summary measures from the pandas dataframe use these 
        functions in the background. Implemented measures include; risk ratio, risk difference, odds ratio, incidence rate 
        ratio, incidence rate difference, number needed to treat, sensitivity, specificity, positive predictive value, negative 
        predictive value, screening cost analyzer, counternull p-values, convert odds to proportions, convert proportions to 
        odds, population attributable fraction, attributable community risk, standardized mean difference
        
        For a narrative description:
        http://zepid.readthedocs.io/en/latest/Calculator.html
        
        ## Graphics
        Uses matplotlib in the background to generate some useful plots. Implemented plots include; functional form assessment 
        (with statsmodels output), p-value plots/functions, spaghetti plot, effect measure plot (forest plot), receiver-operator 
        curve, dynamic risk plot
        
        For a narrative description:
        http://zepid.readthedocs.io/en/latest/Graphics.html
        
        ## Causal
        Causal is a new branch that houses all the causal inference methods implemented. 
        
        For a narrative description:
        http://zepid.readthedocs.io/en/latest/Causal.html
        
        #### 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 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. 
        
        ## Sensitivity Analyses
        Includes trapezoidal distribution generator, corrected Risk Ratio
        
        For a narrative description:
        http://zepid.readthedocs.io/en/latest/Sensitivity%20Analyses.html
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
