{
    "info": {
        "author": "Rachel Moore, Danilo Horta",
        "author_email": "rm18@sanger.ac.uk, horta@ebi.ac.uk",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python"
        ],
        "description": "# chiscore\n\n[![Travis](https://img.shields.io/travis/com/limix/chiscore.svg?style=flat-square&label=linux%20%2F%20macos%20build)](https://travis-ci.com/limix/chiscore) [![AppVeyor](https://img.shields.io/appveyor/ci/Horta/chiscore.svg?style=flat-square&label=windows%20build)](https://ci.appveyor.com/project/Horta/chiscore)\n\nEstimate the joint significance of test statistics derived from linear combination\nof chi-squared distributions.\n\n## Install\n\nWe recommend installing it via\n[conda](http://conda.pydata.org/docs/index.html):\n\n```bash\nconda install -c conda-forge chiscore\n```\n\nAlternatively, chiscore can also be installed using\n[pip](https://pypi.python.org/pypi/pip):\n\n```bash\npip install chiscore\n```\n\n## Running the tests\n\nAfter installation, you can test it\n\n```bash\npython -c \"import chiscore; chiscore.test()\"\n```\n\nas long as you have [pytest](https://docs.pytest.org/en/latest/).\n\n## Usage\n\n### Davies\n\n```python\n>>> from chiscore import davies_pvalue\n>>> q = 1.5\n>>> w = [[0.3, 5.0], [5.0, 1.5]]\n>>> davies_pvalue(q, w)\n{'p_value': 0.6151796819770086, 'param': {'liu_pval': 0.6151796819770086, 'Is_Converged': 1.0}, 'p_value_resampling': None, 'pval_zero_msg': None}\n```\n\n### Liu\n\nLet us approximate\n\n    \ud835\udc4b = 0.5\u22c5\u03c7\u00b2(1, 1) + 0.4\u22c5\u03c7\u00b2(2, 0.6) + 0.1\u22c5\u03c7\u00b2(1, 0.8),\n\nand evaluate Pr(\ud835\udc4b > 2).\n\n```python\n>>> from chiscore import liu_sf\n>>>\n>>> w = [0.5, 0.4, 0.1]\n>>> dofs = [1, 2, 1]\n>>> deltas = [1, 0.6, 0.8]\n>>> (q, dof, delta, _) = liu_sf(2, w, dofs, deltas)\n>>> q\n0.4577529852208846\n>>> dof\n3.5556138890755395\n>>> delta\n0.7491921870025307\n```\n\nTherefore, we have\n\n    Pr(\ud835\udc4b > 2) \u2248 Pr(\u03c7\u00b2(3.56, 0.75) > \ud835\udc61\u207a\ud835\udf0e\u2093 + \ud835\udf07\u2093) = 0.458.\n\n### P-value\n\n```python\n>>> from chiscore import optimal_davies_pvalue\n>>> q = [1.5, 3.0]\n>>> mu = -0.5\n>>> var = 1.0\n>>> kur = 3.0\n>>> w = [10.0, 0.2, 0.1, 0.3]\n>>> remain_var = 0.5\n>>> df = 3.4\n>>> trho = [5.1, 0.2]\n>>> grid = [0., 0.01]\n>>> optimal_davies_pvalue(q, mu, var, kur, w, remain_var, df, trho, grid)\n0.966039962464624\n```\n\n## Authors\n\n* [Danilo Horta](https://github.com/horta)\n\n## References\n\n* Lee, Seunggeun, Michael C. Wu, and Xihong Lin. \"Optimal tests for rare variant\n  effects in sequencing association studies.\" Biostatistics 13.4 (2012): 762-775.\n* Liu, H., Tang, Y., & Zhang, H. H. (2009). A new chi-square approximation to the\n  distribution of non-negative definite quadratic forms in non-central normal\n  variables. Computational Statistics & Data Analysis, 53(4), 853-856.\n\n## License\n\nThis project is licensed under the [MIT License](https://raw.githubusercontent.com/limix/chiscore/master/LICENSE.md).",
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