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        "author": "Akimitsu INOUE",
        "author_email": "akimitsu.inoue@gmail.com",
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            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "# cyclicmodel\nStatistical causal discovery based on cyclic model.  \nThis project is under development.\n\n## Summary\nPython package that performs statistical causal discovery\nunder the following condition:\n1. there are unobserved common factors\n2. two-way causal relationship exists\n\n`cyclicmodel` has been developed based on\n[`bmlingam`][4670f282], which implemented bayesian mixed LiNGAM.\n\n  [4670f282]: https://github.com/taku-y/bmlingam \"bmlingam\"\n\n## Example\n```Python\nimport numpy as np\nimport pymc3 as pm\nimport cyclicmodel as cym\n\n# Generate synthetic data,\n# which assumes causal relation from x1 to x2\nn = 200\nx1 = np.random.randn(n)\nx2 = x1 + np.random.uniform(low=-0.5, high=0.5, size=n)\nxs = np.vstack([x1, x2]).T\n\n# Model settings\nhyper_params = cym.define_model.CyclicModelParams(\n    dist_std_noise='log_normal',\n    df_indvdl=8.0,\n    dist_l_cov_21='uniform, -0.9, 0.9',\n    dist_scale_indvdl='uniform, 0.1, 1.0',\n    dist_beta_noise='uniform, 0.5, 6.0')\n\n# Generate PyMC3 model\nmodel = cym.define_model.get_pm3_model(xs, hyper_params, verbose=10)\n\n# Run variational inference with PyMC3\nwith model:\n  fit = pm.FullRankADVI().fit(n=100000)\n  trace = fit.sample(1000, include_transformed=True)\n\n# Check the posterior mean of the coefficients\nprint(np.mean(trace['b_21']))  # from x1 to x2\nprint(np.mean(trace['b_12']))  # from x2 to x1\n```\n\n## Installation\n```bash\npip install cyclicmodel\n```\n\n## References\n-  [LiNGAM - Discovery of non-gaussian linear causal models](https://sites.google.com/site/sshimizu06/lingam)\n- [Shimizu, S., & Bollen, K. (2014). Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions. Journal of Machine Learning Research, 15(1), 2629-2652.](http://jmlr.org/papers/volume15/shimizu14a/shimizu14a.pdf)\n\n\n",
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