Metadata-Version: 2.1
Name: boostsa
Version: 0.1.0
Summary: A package to compute bootstrap sampling significance test
Home-page: https://github.com/fornaciari/bootstrap
Author: Tommaso Fornaciari
Author-email: fornaciari@unibocconi.it
License: MIT license
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: pip (==20.2.4)
Requires-Dist: wheel (==0.36.2)
Requires-Dist: twine (==3.3.0)
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: tqdm
Requires-Dist: sklearn

BooStSa - BOOtSTrap SAmpling in pyhton
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.. image:: https://img.shields.io/github/license/fornaciari/boostsa
        :target: https://lbesson.mit-license.org/
        :alt: License

This is a tool to compute bootstrap sampling significance test, 
even in the pipeline of a complex experimental design.










For the theoretical aspects of Bootstrap sampling, please refer to the paper:

 Søgaard, A., Johannsen, A., Plank, B., Hovy, D., & Alonso, H. M. (2014, June). 
 *What’s in a p-value in NLP?*. 
 In Proceedings of the eighteenth conference on computational natural language learning (pp. 1-10).






