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  "Package": "BCDAG",
  "Title": "Bayesian Structure and Causal Learning of Gaussian Directed\nGraphs",
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  "Description": "A collection of functions for structure learning of causal\nnetworks and estimation of joint causal effects from\nobservational Gaussian data. Main algorithm consists of a\nMarkov chain Monte Carlo scheme for posterior inference of\ncausal structures, parameters and causal effects between\nvariables. References: F. Castelletti and A. Mascaro (2021)\n<doi:10.1007/s10260-021-00579-1>, F. Castelletti and A. Mascaro\n(2022) <doi:10.48550/arXiv.2201.12003>.",
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      "title": "Protein levels for 68 diagnosed AML patients of subtype M2",
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      ],
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        "AKT.p308",
        "AKT.p473",
        "BAD",
        "BAD.p112",
        "BAD.p136",
        "BAD.p155",
        "BAX",
        "BCL2",
        "BCLXL",
        "CCND1",
        "GSK3",
        "GSK3.p",
        "MYC",
        "PTEN",
        "PTEN.p",
        "TP53",
        "XIAP"
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      "title": "Compute causal effects between variables",
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        "causaleffect"
      ]
    },
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      "page": "confint.bcdagCE",
      "title": "Credible Intervals for bcdagCE Object",
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        "confint.bcdagCE"
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    },
    {
      "page": "get_causaleffect",
      "title": "Estimate total causal effects from the MCMC output",
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    },
    {
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      "page": "leukemia",
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    },
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