{
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  "Date": "2026-04-07",
  "Title": "Robust Covariance Matrix Estimators",
  "Authors@R": "c(person(given = \"Achim\", family = \"Zeileis\", role = c(\"aut\", \"cre\"), email = \"Achim.Zeileis@R-project.org\", comment = c(ORCID = \"0000-0003-0918-3766\")),\nperson(given = \"Thomas\", family = \"Lumley\", role = \"aut\", email = \"t.lumley@auckland.ac.nz\", comment = c(ORCID = \"0000-0003-4255-5437\")),\nperson(given = \"Nathaniel\", family = \"Graham\", role = \"ctb\", email = \"npgraham1@gmail.com\", comment = c(ORCID = \"0009-0002-1215-5256\")),\nperson(given = \"Susanne\", family = \"Koell\", role = \"ctb\"))",
  "Description": "Object-oriented software for model-robust covariance\nmatrix estimators. Starting out from the basic robust\nEicker-Huber-White sandwich covariance methods include:\nheteroscedasticity-consistent (HC) covariances for\ncross-section data; heteroscedasticity- and\nautocorrelation-consistent (HAC) covariances for time series\ndata (such as Andrews' kernel HAC, Newey-West, and WEAVE\nestimators); clustered covariances (one-way and multi-way);\npanel and panel-corrected covariances;\nouter-product-of-gradients covariances; and (clustered)\nbootstrap covariances. All methods are applicable to\n(generalized) linear model objects fitted by lm() and glm() but\ncan also be adapted to other classes through S3 methods.\nDetails can be found in Zeileis et al. (2020)\n<doi:10.18637/jss.v095.i01>, Zeileis (2004)\n<doi:10.18637/jss.v011.i10> and Zeileis (2006)\n<doi:10.18637/jss.v016.i09>.",
  "License": "GPL-2 | GPL-3",
  "URL": "https://sandwich.R-Forge.R-project.org/",
  "BugReports": "https://sandwich.R-Forge.R-project.org/contact.html",
  "Repository": "https://r-forge.r-universe.dev",
  "Date/Publication": "2026-04-07 02:00:47 UTC",
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  "RemoteSubdir": "pkg",
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  "Packaged": {
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    "User": "root"
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  "Author": "Achim Zeileis [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-0918-3766>),\nThomas Lumley [aut] (ORCID: <https://orcid.org/0000-0003-4255-5437>),\nNathaniel Graham [ctb] (ORCID: <https://orcid.org/0009-0002-1215-5256>),\nSusanne Koell [ctb]",
  "Maintainer": "Achim Zeileis <Achim.Zeileis@R-project.org>",
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    "kweights",
    "lrvar",
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    "vcovPL",
    "weave",
    "weightsAndrews",
    "weightsLumley"
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      "title": "Innovation and Institutional Ownership",
      "object": "InstInnovation",
      "file": "InstInnovation.rda",
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        "patents",
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        "RealInt"
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      "topics": [
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      "title": "Isotonic Autocorrelation Function",
      "topics": [
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      "topics": [
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      "topics": [
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      "title": "A Simple Meat Matrix Estimator",
      "topics": [
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      "title": "Newey-West HAC Covariance Matrix Estimation",
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        "NeweyWest"
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        "vcovCL"
      ]
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      "title": "Heteroscedasticity and Autocorrelation Consistent (HAC) Covariance Matrix Estimation",
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        "vcovHAC",
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      "page": "vcovHC",
      "title": "Heteroscedasticity-Consistent Covariance Matrix Estimation",
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        "vcovHC",
        "vcovHC.default",
        "vcovHC.mlm"
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      "title": "Panel-Corrected Covariance Matrix Estimation",
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        "vcovPC"
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      "title": "Clustered Covariance Matrix Estimation for Panel Data",
      "topics": [
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        "vcovPL"
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      "title": "Kernel-based HAC Covariance Matrix Estimation",
      "topics": [
        "bwAndrews",
        "kernHAC",
        "weightsAndrews"
      ]
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    {
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      "title": "Weighted Empirical Adaptive Variance Estimation",
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