Metadata-Version: 1.0
Name: bootstrapped
Version: 0.0.2
Summary: Implementations of the percentile based bootstrap
Home-page: https://github.com/facebookincubator/bootstrapped
Author: Spencer Beecher
Author-email: spencebeecher@gmail.com
License: UNKNOWN
Description: bootstrapped - confidence intervals made easy
        =============================================
        
        **bootstrapped** is a Python library that allows you to build confidence
        intervals from data. This is useful in a variety of contexts - including
        during ad-hoc a/b test analysis.
        
        Motivating Example - A/B Test
        -----------------------------
        
        Imagine we own a website and think changing the color of a 'subscribe'
        button will improve signups. One method to measure the improvement is to
        conduct an A/B test where we show 50% of people the old version and 50%
        of the people the new version. We can use the bootstrap to understand
        how much the button color improves responses and give us the error bars
        associated with the test - this will give us lower and upper bounds on
        how good we should expect the change to be!
        
        The Gist - Mean of a Sample
        ---------------------------
        
        Given a sample of data - we can generate a bunch of new samples by
        're-sampling' from what we have gathered. We calculate the mean for each
        generated sample. We can use the means from the generated samples to
        understand the variation in the larger population and can construct
        error bars for the true mean.
        
        bootstrapped - Benefits
        -----------------------
        
        -  Efficient computation of confidence intervals
        -  Functions to handle single populations and a/b tests
        -  Functions to understand `statistical
           power <https://en.wikipedia.org/wiki/Statistical_power>`__
        -  Multithreaded support to speed-up bootstrap computations
        -  Dense and sparse array support
        
        Example Usage
        -------------
        
        .. code:: python
        
            import numpy as np
            import bootstrapped.bootstrap as bs
            import bootstrapped.stats_functions as bs_stats
        
            mean = 100
            stdev = 10
        
            population = np.random.normal(loc=mean, scale=stdev, size=50000)
        
            # take 1k 'samples' from the larger population
            samples = population[:1000]
        
            print(bs.bootstrap(samples, stat_func=bs_stats.mean))
            >> 100.08  (99.46, 100.69)
        
            print(bs.bootstrap(samples, stat_func=bs_stats.std))
            >> 9.49  (9.92, 10.36)
        
        Extended Examples
        ^^^^^^^^^^^^^^^^^
        
        -  `Bootstrap
           Intro <https://github.com/facebookincubator/bootstrapped/blob/master/examples/bootstrap_intro.ipynb>`__
        -  `Bootstrap A/B
           Testing <https://github.com/facebookincubator/bootstrapped/blob/master/examples/bootstrap_ab_testing.ipynb>`__
        -  More notebooks can be found in the
           `examples/ <https://github.com/facebookincubator/bootstrapped/tree/master/examples>`__
           directory
        
        Requirements
        ------------
        
        **bootstrapped** requires numpy. The power analysis functions require
        matplotlib and pandas.
        
        Installation
        ------------
        
        .. code:: bash
        
            pip install bootstrapped
        
        How bootstrapped works
        ----------------------
        
        **bootstrapped** provides pivotal (aka empirical) based confidence
        intervals based on bootstrap re-sampling with replacement. The
        percentile method is also available.
        
        For more information please see:
        
        1. `Bootstrap confidence
           intervals <https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/readings/MIT18_05S14_Reading24.pdf>`__
           (good intro)
        2. `An introduction to Bootstrap
           Methods <http://www.stat-athens.aueb.gr/~karlis/lefkada/boot.pdf>`__
        3. `The Bootstrap, Advanced Data
           Analysis <http://www.stat.cmu.edu/~cshalizi/402/lectures/08-bootstrap/lecture-08.pdf>`__
        4. `When the bootstrap dosen't
           work <http://notstatschat.tumblr.com/post/156650638586/when-the-bootstrap-doesnt-work>`__
        5. (book) `An Introduction to the
           Bootstrap <https://www.amazon.com/Introduction-Bootstrap-Monographs-Statistics-Probability/dp/0412042312/>`__
        6. (book) `Bootstrap Methods and their
           Application <https://www.amazon.com/Bootstrap-Application-Statistical-Probabilistic-Mathematics-ebook/dp/B00D2WQ02U/>`__
        
        See the CONTRIBUTING file for how to help out.
        
        Contributors
        ^^^^^^^^^^^^
        
        Spencer Beecher, Don van der Drift, David Martin, Lindsay Vass, Sergey
        Goder, Benedict Lim, and Matt Langner.
        
        Special thanks to Eytan Bakshy.
        
        License
        -------
        
        **bootstrapped** is BSD-licensed. We also provide an additional patent
        grant.
        
Platform: UNKNOWN
