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  "Title": "Estimate Causal Polytree from Data",
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  "Description": "Given a data matrix with rows representing data vectors\nand columns representing variables, produces a directed\npolytree for the underlying causal structure. Based on the\nalgorithm developed in Chatterjee and Vidyasagar (2022)\n<arxiv:2209.07028>. The method is fully nonparametric, making\nno use of linearity assumptions, and especially useful when the\nnumber of variables is large.",
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  "Author": "Sourav Chatterjee [aut, cre]\n(<https://orcid.org/0000-0003-4460-209X>)",
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      "title": "This is the function that computes the skeletion tree from data. The input is a matrix x whose rows are the data vectors. The sample size n is the number of rows. The number of variables p is the number of columns The function outputs the skeleton tree g.",
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