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        "author": "T.Ikeuchi, G.Haraoka, S.Shimizu",
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        "description": "# LiNGAM - Discovery of non-gaussian linear causal models\n\n[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/cdt15/lingam/blob/master/LICENSE)\n[![Read the Docs](https://readthedocs.org/projects/lingam/badge/?version=latest)](https://lingam.readthedocs.io/)\n\nLiNGAM is a new method for estimating structural equation models or linear Bayesian networks. It is based on using the non-Gaussianity of the data.\n\n* [The LiNGAM Project](https://sites.google.com/site/sshimizu06/lingam).\n\n## Requirements\n* Python3\n* numpy\n* scipy\n* scikit-learn\n\n## Installation\nTo install lingam package, use `pip` as follows:\n\n```\n$ pip install lingam\n```\n\n## Documentation\n[Tutrial and API reference](https://lingam.readthedocs.io/)\n\n## License\nThis project is licensed under the terms of the [MIT license](./LICENSE).\n\n## Reference Papers\n* S. Shimizu, P. O. Hoyer, A. Hyv\uff83\uff64rinen and A. Kerminen. A linear non-gaussian acyclic model for causal discovery. Journal of Machine Learning Research, 7: 2003--2030, 2006. [[PDF]](https://www.cs.helsinki.fi/group/neuroinf/lingam/JMLR06.pdf)\n* S. Shimizu, T. Inazumi, Y. Sogawa, A. Hyv\uff83\uff64rinen, Y. Kawahara, T. Washio, P. O. Hoyer and K. Bollen. DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model. Journal of Machine Learning Research, 12(Apr): 1225--1248, 2011. [[PDF]](http://www.jmlr.org/papers/volume12/shimizu11a/shimizu11a.pdf)\n* A. Hyv\uff83\uff64rinen and S. M. Smith. Pairwise likelihood ratios for estimation of non-Gaussian structural equation models. Journal of Machine Learning Research, 14(Jan): 111--152, 2013. [[PDF]](https://www.cs.helsinki.fi/u/ahyvarin/papers/JMLR13.pdf)\n\n\n",
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