Metadata-Version: 2.1
Name: hierarch
Version: 1.1.3
Summary: Hierarchical hypothesis testing library
Home-page: https://github.com/rishi-kulkarni/hierarch
Author: Rishi Kulkarni
Author-email: rkulk@stanford.edu
License: UNKNOWN
Description: # hierarch
        
        ## A Hierarchical Resampling Package for Python
        
        Version 1.1.3
        
        hierarch is a package for hierarchical resampling (bootstrapping, permutation) of datasets in Python. Because for loops are ultimately intrinsic to cluster-aware resampling, hierarch uses Numba to accelerate many of its key functions.
        
        hierarch has several functions to assist in performing resampling-based (and therefore distribution-free) hypothesis tests, confidence interval calculations, and power analyses on hierarchical data.
        
        ## Table of Contents
        
        1. [Introduction](#introduction)
        2. [Setup](#setup)
        3. [Documentation](#documentation)
        4. [Citation](#citation)
        
        
        <a name="introduction"></a>
        ## Introduction 
        
        Design-based randomization tests represents the platinum standard for significance analyses [[1, 2, 3]](#1) - that is, they produce probability statements that depend only on the experimental design, not at all on less-than-verifiable assumptions about the probability distributions of the data-generating process. Researchers can use hierarch to quickly perform automated design-based randomization tests for experiments with arbitrary levels of hierarchy.
        
        
        <a id="1">[1]</a> Tukey, J.W. (1993). Tightening the Clinical Trial. Controlled Clinical Trials, 14(4), 266-285.
        
        <a id="1">[2]</a> Millard, S.P., Krause, A. (2001). Applied Statistics in the Pharmaceutical Industry. Springer.
        
        <a id="1">[3]</a> Berger, V.W. (2000). Pros and cons of permutation tests in clinical trials. Statistics in Medicine, 19(10), 1319-1328.
        
        
        <a name="setup"></a>
        ## Setup 
        
        ### Dependencies
        * numpy
        * pandas (for importing data)
        * numba
        * scipy (for power analysis)
        
        ### Installation
        
        The easiest way to install hierarch is via PyPi. 
        
        ```pip install hierarch```
        
        Alternatively, you can install from Anaconda.
        
        ```conda install -c rkulk111 hierarch```
        
        
        <a name="documentation"></a>
        ## Documentation
        Check out our user guide at [readthedocs](https://hierarch.readthedocs.io/).
        
        <a name="citation"></a>
        ## Citation
        If hierarch helps you analyze your data, please consider citing it. The manuscript also
        contains a set of simulations validating hierarchical randomization tests in a variety of
        conditions.
        
        Analyzing Nested Experimental Designs – A User-Friendly Resampling Method to Determine Experimental Significance. Rishikesh U. Kulkarni, Catherine L. Wang, Carolyn R. Bertozzi. bioRxiv 2021.06.29.450439; doi: https://doi.org/10.1101/2021.06.29.450439
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
