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  "Title": "Recursive Partitioning of Network Models",
  "Version": "1.0.1.9000",
  "Date": "2021-2-4",
  "Authors@R": "c(person(\"Payton\", \"Jones\", email = \"paytonjjones@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-6513-8498\")),\nperson(given = \"Thorsten\", family = \"Simon\", role = \"aut\", email = \"Thorsten.Simon@uibk.ac.at\", comment = c(ORCID = \"0000-0002-3778-7738\")),\nperson(given = \"Achim\", family = \"Zeileis\", role = \"aut\", email = \"Achim.Zeileis@R-project.org\", comment = c(ORCID = \"0000-0003-0918-3766\")))",
  "Description": "Network trees recursively partition the data with respect\nto covariates. Two network tree algorithms are available:\nmodel-based trees based on a multivariate normal model and\nnonparametric trees based on covariance structures. After\npartitioning, correlation-based networks (psychometric\nnetworks) can be fit on the partitioned data. For details see\nJones, Mair, Simon, & Zeileis (2020)\n<doi:10.1007/s11336-020-09731-4>.",
  "License": "GPL-2 | GPL-3",
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  "URL": "https://paytonjjones.github.io/networktree/",
  "BugReports": "https://github.com/paytonjjones/networktree/issues",
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  "Repository": "https://paytonjjones.r-universe.dev",
  "Date/Publication": "2022-09-05 18:40:46 UTC",
  "RemoteUrl": "https://github.com/paytonjjones/networktree",
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      "fileid": "https://r2.ropensci.org/84b0c7d923bf332b8e10029c1611b2ba2607a732de0801fa27da6494945c26f2",
      "status": "success",
      "check": "NOTE",
      "buildurl": "https://github.com/r-universe/paytonjjones/actions/runs/28674094776"
    }
  ]
}