Metadata-Version: 2.1
Name: diproperm
Version: 0.0.3
Summary: DiProPerm for high dimensional hypothesis testing.
Home-page: https://github.com/idc9/diproperm
Author: Iain Carmichael
Author-email: idc9@cornell.edu
License: MIT
Description: 
        DiProPerm
        ----
        
        **author**: `Iain Carmichael`_
        
        Additional documentation, examples and code revisions are coming soon.
        For questions, issues or feature requests please reach out to Iain:
        iain@unc.edu.
        
        Overview
        ========
        
        This package implements Direction-Projection-Permutation for High Dimensional
        Hypothesis Tests (DiPoPerm). For details see Wei et al, 2016 (`paper link`_, `arxiv link`_). DiProPerm "rigorously assesses whether a binary linear classifier is detecting statistically significant differences between two high-dimensional distributions."
        
        
        
        Wei, S., Lee, C., Wichers, L., & Marron, J. S. (2016). Direction-projection-permutation for high-dimensional hypothesis tests. Journal of Computational and Graphical Statistics, 25(2), 549-569.
        
        Installation
        ============
        
        The diproperm package can be installed via pip or github. This package is currently only tested in python 3.6.
        
        ::
        
            pip install diproperm
        
        
        ::
        
            git clone https://github.com/idc9/diproperm.git
            python setup.py install
        
        Example
        =======
        
        .. code:: python
        
            from sklearn.datasets import make_blobs
            import numpy as np
            import matplotlib.pyplot as plt
            # %matplotlib inline
        
            from diproperm.DiProPerm import DiProPerm
        
            # toy binary class dataset (two isotropic Gaussians)
            X, y = make_blobs(n_samples=100, n_features=2, centers=2, cluster_std=2)
        
            # DiProPerm with mean difference classifier, mean difference summary
            # statistic, and 1000 permutation samples.
            dpp = DiProPerm(B=1000, stat='md', clf='md')
            dpp.fit(X, y)
        
            dpp.test_stats_['md']
        
        .. code:: python
        
            {'Z': 11.704865481794599,
             'cutoff_val': 1.2678333596648679,
             'obs': 4.542253375623943,
             'pval': 0.0,
             'rejected': True}
        
        .. code:: python
        
            dpp.hist('md')
        
        .. image:: doc/figures/dpp_hist.png
        
        
        For more example code see `these example notebooks`_.
        
        Help and Support
        ================
        
        Additional documentation, examples and code revisions are coming soon.
        For questions, issues or feature requests please reach out to Iain:
        iain@unc.edu.
        
        Documentation
        ^^^^^^^^^^^^^
        
        The source code is located on github: https://github.com/idc9/diproperm
        
        Testing
        ^^^^^^^
        
        Testing is done using `nose`.
        
        Contributing
        ^^^^^^^^^^^^
        
        We welcome contributions to make this a stronger package: data examples,
        bug fixes, spelling errors, new features, etc.
        
        
        
        .. _Iain Carmichael: https://idc9.github.io/
        .. _paper link: https://www.tandfonline.com/doi/abs/10.1080/10618600.2015.1027773
        .. _arxiv link: https://arxiv.org/pdf/1304.0796.pdf
        .. _these example notebooks: https://github.com/idc9/diproperm/tree/master/doc
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
