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        "author": "Ulf Hamster",
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        "description": "[![Build Status](https://travis-ci.org/kmedian/korr.svg?branch=master)](https://travis-ci.org/kmedian/korr)\n[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/kmedian/korr/master?urlpath=lab)\n\n# korr\ncollection of utility functions for correlation analysis\n\n\n## Table of Contents\n* [Installation](#installation)\n* [Usage](#usage)\n* [Commands](#commands)\n* [Support](#support)\n* [Contributing](#contributing)\n\n\n## Installation\nThe `korr` [git repo](http://github.com/kmedian/korr) is available as [PyPi package](https://pypi.org/project/korr)\n\n```\npip install korr\n```\n\n\n## Usage\nCheck the [examples](https://github.com/kmedian/korr/tree/master/examples) folder for notebooks.\n\nCompute correlation matrix and its p-values\n\n* [pearson](https://github.com/kmedian/korr/blob/master/examples/pearson.ipynb) -- Pearson/Sample correlation (interval- and ratio-scale data)\n* [kendall](https://github.com/kmedian/korr/blob/master/examples/kendall.ipynb) -- Kendall's tau rank correlation (ordinal data)\n* [spearman](https://github.com/kmedian/korr/blob/master/examples/spearman.ipynb) -- Spearman rho rank correlation (ordinal data)\n* [mcc](https://github.com/kmedian/korr/blob/master/examples/mcc%20(Matthews%20correlation).ipynb) -- Matthews correlation coefficient between binary variables \n\nEDA, Dig deeper into results\n\n* [flatten](https://github.com/kmedian/korr/blob/master/examples/flatten.ipynb) -- A table (pandas) with one row for each correlation pairs with the variable indicies, corr., p-value. For example, try to find \"good\" cutoffs with `corr_vs_pval` and then look up the variable indicies with `flatten` afterwards.\n* [slice_yx](https://github.com/kmedian/korr/blob/master/examples/slice_yx.ipynb) -- slice a correlation and p-value matrix of a (y,X) dataset into a (y,x_i) vector and (x_j, x_k) matrices\n* [corr_vs_pval](https://github.com/kmedian/korr/blob/master/examples/corr_vs_pval.ipynb)  -- Histogram to find p-value cutoffs (alpha) for a) highly correlated pairs, b) unrelated pairs, c) the mixed results. \n* [bracket_pval](hhttps://github.com/kmedian/korr/blob/master/examples/bracket_pval.ipynb) -- Histogram with more fine-grained p-value brackets. \n* [corrgram](https://github.com/kmedian/korr/blob/master/examples/corrgram.ipynb) -- Correlogram, heatmap of correlations with p-values in brackets\n\nUtility functions\n\n* [confusion](https://github.com/kmedian/korr/blob/master/examples/confusion.ipynb) -- Confusion matrix. Required for Matthews correlation (mcc) and is a bitter faster than sklearn's \n\nParameter Stability\n\n* [bootcorr](https://github.com/kmedian/korr/blob/master/examples/bootcorr.ipynb) -- Estimate multiple correlation matrices based on bootstrapped samples. From there you can assess how stable correlation estimates are (how sensitive against in-sample variation). For example, stable estimates are good candidates for modeling, and unstable correlation pairs are good candidates for P-hacking and non-reproducibility.\n\nVariable Selection, Search Functions\n\n* [mincorr](https://github.com/kmedian/korr/blob/master/examples/mincorr.ipynb) -- From all estimated correlation pairs, pick a given `n=3,5,..` of variables with low and insignificant correlations among each other. (See [binsel](https://github.com/kmedian/binsel) package for an application.)\n* `find_best` -- Find the N \"best\", i.e. high and most significant, correlations\n* `find_worst` -- Find the N \"worst\", i.e. insignificant/random and low, correlations\n* [find_unrelated](https://github.com/kmedian/korr/blob/master/examples/find_unrelated.ipynb) -- Return variable indicies of unrelated pairs (in terms of insignificant p-value)\n\n\n## Commands\n* Check syntax: `flake8 --ignore=F401`\n* Run Unit Tests: `python -W ignore -m unittest discover`\n* Remove `.pyc` files: `find . -type f -name \"*.pyc\" | xargs rm`\n* Remove `__pycache__` folders: `find . -type d -name \"__pycache__\" | xargs rm -rf`\n* Upload to PyPi with twine: `python setup.py sdist && twine upload -r pypi dist/*`\n\n\n## Debugging\n* Notebooks to profile python code are in the [profile](profile) folder\n\n\n## Support\nPlease [open an issue](https://github.com/kmedian/korr/issues/new) for support.\n\n\n## Contributing\nPlease contribute using [Github Flow](https://guides.github.com/introduction/flow/). Create a branch, add commits, and [open a pull request](https://github.com/kmedian/korr/compare/).",
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