Metadata-Version: 2.1
Name: mosaicperm
Version: 0.1.1
Summary: Exact inference via mosaic permutations
Home-page: https://github.com/amspector100/mosaicperm/
Author: Asher Spector
Author-email: amspector100@gmail.com
License: UNKNOWN
Description: A python implementation of the mosaic permutation testing framework.
        
        # Installation
        
        To install ``mosaicperm``, just use pip:
        
        ``pip install mosaicperm``
        
        # Documentation
        
        Documentation and tutorials are available at https://mosaicperm.readthedocs.io/.
        
        # Quickstart
        
        Below, we give a simple example showing how to use ``mosaicperm`` to test whether a set of factor exposures explain the correlations among a matrix of outcomes variables.
        
        ```
        	import numpy as np
        	import mosaicperm as mp
        
        	# synthetic outcomes and exposures
        	n_obs, n_subjects, n_factors = 100, 200, 20
        	outcomes = np.random.randn(n_obs, n_subjects)
        	exposures = np.random.randn(n_obs, n_subjects, n_factors)
        	# example of missing data
        	outcomes[0:10][:, 0:5] = np.nan
        	exposures[0:10][:, 0:5] = np.nan
        
        	# fit mosaic permutation test
        	mpt = mp.factor.MosaicFactorTest(
        		outcomes=outcomes,
        		exposures=exposures,
        		test_stat=mp.statistics.mean_maxcorr_stat,
        	)
        	print(mpt.fit().summary())
        
        	# produce a time series plot of this analysis
        	mpt.fit_tseries(
        		nrand=100, n_timepoints=20,
        	).plot_tseries()
        ```
        
        See the [documentation](https://mosaicperm.readthedocs.io/) for more details.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
