{
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  "Package": "malp",
  "Version": "1.1-1",
  "Date": "2026-09-21",
  "Title": "Maximum Agreement Linear Prediction",
  "Authors@R": "c(person(given=\"Pierre\", family=\"Chausse\", role=c(\"aut\",\"cre\"),\nemail=\"pchausse@uwaterloo.ca\"),\nperson(given=\"Taeho\", family=\"Kim\", role=\"aut\",\nemail=\"tak422@lehigh.edu\"),\nperson(given=\"Edsel A.\", family=\"Pena\", role = \"aut\",\nemail = \"pena@stat.sc.edu\"),\nperson(given=\"George\", family=\"Luta\", role = \"ctb\",\nemail = \"George.Luta@georgetown.edu\"))",
  "Description": "Provides tools for estimation and prediction using Maximum\nAgreement Linear Predictors (MALPs). MALPs provide an\nalternative to least squares linear predictors when agreement\nbetween predicted and observed values, as measured by Lin's\nConcordance Correlation Coefficient (CCC), is of primary\ninterest. Applications include missing value imputation and\ncalibration studies. The package includes functions for model\nestimation, prediction, statistical inference,\ncross-validation, and model diagnostics. The implemented\nmethodology is described in Kim et al. (2026)\n<doi:10.1214/26-EJS2550>.",
  "License": "GPL (>= 2)",
  "URL": "https://codeberg.org/pchausse/malp,\nhttps://pchausse.r-universe.dev/malp",
  "BugReports": "https://codeberg.org/pchausse/malp/issues",
  "Repository": "https://pchausse.r-universe.dev",
  "Date/Publication": "2026-09-22 18:40:41 UTC",
  "RemoteUrl": "https://codeberg.org/pchausse/malp",
  "RemoteRef": "HEAD",
  "RemoteSha": "52c54c1e2bcdc550668d812d69c161efd8626764",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-09-22 19:09:18 UTC",
    "User": "root"
  },
  "Author": "Pierre Chausse [aut, cre],\nTaeho Kim [aut],\nEdsel A. Pena [aut],\nGeorge Luta [ctb]",
  "Maintainer": "Pierre Chausse <pchausse@uwaterloo.ca>",
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  "_created": "2026-09-22T19:09:18.000Z",
  "_published": "2026-09-22T19:12:26.703Z",
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      "n": 5
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      "title": "Data on body fat",
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      "title": "Maximum Agreement Linear Predictor",
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