Metadata-Version: 1.2
Name: preposterous
Version: 0.0.1
Summary: A simple library for estimating the impact of an intervention, with humility
Home-page: https://github.com/matthewwritter/preposterous
Author: Matthew Ritter
Maintainer: Matthew Ritter
License: MIT
Description: Pre/Posterous
        =============
        
        Estimate the impact of an intervention, with simplicity and humility
        
        Installation
        ------------
        
        You can install with ``pip install preposterous``
        
        If you want to install from source, then clone this repository and run
        ``python setup.py install`` from the project root.
        
        **Pre/Posterous is under active development** and the current release
        can be considered a 'proof of concept'. The largest restriction is that
        it only imports data from the `Reporter
        app <http://www.reporter-app.com/>`__, which is my current default
        recommendation for anyone who is trying to track something.
        
        Use cases
        ---------
        
        Quantified Self data
        ~~~~~~~~~~~~~~~~~~~~
        
        The primary use case is for quantified self, where you have periodic
        measurements of the target metric (weight, categorical sleep quality,
        ect) and potential interventions (medications, diet shifts, ect). This
        library can organize these into 'natural experiments' that point the way
        towards a causal relationship
        
        *Warning* Python is pretty great, but nothing can replace a well powered
        `Double Blind Randomized Controlled
        Trial <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3196997/>`__ for
        establishing causality. That said, many (most?) situations do not lend
        themselves to RCTs, and yet we're still forced to make decisions. That's
        where tools like this, used with an appreciation for non-binary modes of
        belief, can be helpful.
        
        Example
        -------
        
        ::
        
            import preposterous.preposterous as ppl
            pdf = ppl.PrePostDF()
            pdf.add_outcome(filename='data/sample_reporter_output.csv')
            pdf.add_intervention(filename='data/sample_reporter_output.csv')
            print(pdf.basic_info())
            pdf.generate_confusion_matrix(intervention='Exercise')
        
Keywords: quantified_self statistics
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
