{ "_id": "6a102de8acfb0bcc41c9407a", "Package": "SEset", "Type": "Package", "Title": "Computing Statistically-Equivalent Path Models", "Version": "1.0.1", "Author": "Oisín Ryan", "Maintainer": "Oisín Ryan ", "Description": "Tools to compute and analyze the set of\nstatistically-equivalent (Gaussian, linear) path models which\ngenerate the input precision or (partial) correlation matrix.\nThis procedure is useful for understanding how statistical\nnetwork models such as the Gaussian Graphical Model (GGM)\nperform as causal discovery tools. The statistical-equivalence\nset of a given GGM expresses the uncertainty we have about the\nsign, size and direction of directed relationships based on the\nweights matrix of the GGM alone. The derivation of the\nequivalence set and its use for understanding GGMs as causal\ndiscovery tools is described by Ryan, O., Bringmann, L.F., &\nSchuurman, N.K. 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