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  "Title": "Semiparametric Factor and Regression Models for Symmetric\nRelational Data",
  "Version": "1.12",
  "Date": "2026-01-16",
  "Authors@R": "person(given=\"Peter\",\nfamily=\"Hoff\",\nrole=c(\"aut\",\"cre\"),\nemail=\"peter.hoff@duke.edu\")",
  "Description": "Estimation of the parameters in a model for symmetric\nrelational data (e.g., the above-diagonal part of a square\nmatrix), using a model-based eigenvalue decomposition and\nregression. Missing data is accommodated, and a posterior mean\nfor missing data is calculated under the assumption that the\ndata are missing at random. The marginal distribution of the\nrelational data can be arbitrary, and is fit with an ordered\nprobit specification. See Hoff (2007)\n<doi:10.48550/arXiv.0711.1146>. for details on the model.",
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  "URL": "https://pdhoff.github.io/",
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  "Repository": "https://pdhoff.r-universe.dev",
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        "eigenmodel"
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