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        "author": "D. Horta, P. Casale, R. Moore",
        "author_email": "rm18@sanger.ac.uk",
        "bugtrack_url": null,
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        "description": "# Struct-lmm\n\n[![Travis](https://img.shields.io/travis/com/limix/struct-lmm.svg?style=flat-square&label=linux%20%2F%20macos%20build)](https://travis-ci.com/limix/struct-lmm) [![AppVeyor](https://img.shields.io/appveyor/ci/Horta/struct-lmm-rxwgm.svg?style=flat-square&label=windows%20build)](https://ci.appveyor.com/project/Horta/struct-lmm-rxwgm)\n\nStructured Linear Mixed Model (StructLMM) is a computationally efficient method to\ntest for and characterize loci that interact with multiple environments [1].\n\nThis a standalone module that implements the basic functionalities of StructLMM.\nHowever, we recommend using StructLMM via\n[LIMIX2](https://limix.readthedocs.io/en/2.0.x/index.html) as this additionally\nimplements:\n\n- Multiple methods for GWAS;\n- Methods to characterize GxE at specific variants;\n- Command line interface.\n\n## Install\n\nFrom terminal, it can be installed using [pip](https://pypi.python.org/pypi/pip):\n\n```bash\npip install struct-lmm\n```\n\n## Usage\n\n```python\n>>> from numpy import ones, concatenate\n>>> from numpy.random import RandomState\n>>>\n>>> from struct_lmm import StructLMM\n>>>\n>>> random = RandomState(1)\n>>> n = 20\n>>> k = 4\n>>> y = random.randn(n, 1)\n>>> E = random.randn(n, k)\n>>> M = ones((n, 1))\n>>> x = 1.0 * (random.rand(n, 1) < 0.2)\n>>>\n>>> lmm = StructLMM(y, M, E)\n>>> lmm.fit(verbose=False)\n>>> pv = lmm.score_2dof_assoc(x)\n>>> print(pv)\n0.8470017194859742\n>>> M = concatenate([M, x], axis=1)\n>>> lmm = StructLMM(y, M, E)\n>>> lmm.fit(verbose=False)\n>>> pv = lmm.score_2dof_inter(x)\n>>> print(pv)\n0.6781100453132024\n```\n\n## Problems\n\nIf you encounter any problem, please, consider submitting a [new issue](https://github.com/limix/struct-lmm/issues/new).\n\n## Authors\n\n- [Danilo Horta](https://github.com/horta)\n- [Francesco Paolo Casale](https://github.com/fpcasale)\n- [Oliver Stegle](https://github.com/ostegle)\n- [Rachel Moore](https://github.com/rm18)\n\n## License\n\nThis project is licensed under the [MIT License](https://raw.githubusercontent.com/limix/struct-lmm/master/LICENSE.md).\n\n[1] Moore, R., Casale, F. P., Bonder, M. J., Horta, D., Franke, L., Barroso, I., &\n    Stegle, O. (2018). [A linear mixed-model approach to study multivariate\n    gene\u2013environment interactions](https://www.nature.com/articles/s41588-018-0271-0) (p. 1). Nature Publishing Group.",
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