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  "Package": "plmmr",
  "Title": "Penalized Linear Mixed Models for Correlated Data",
  "Version": "4.3.0.3",
  "Authors@R": "c(\nperson(\"Tabitha K.\", \"Peter\", , \"tabitha.peter15@gmail.com\", role = \"aut\",\ncomment = c(ORCID = \"0009-0005-2524-4751\")),\nperson(\"Anna C.\", \"Reisetter\", , \"anna-reisetter@uiowa.edu\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-8332-4585\")),\nperson(\"Yujing\", \"Lu\", role = \"aut\"),\nperson(\"Oscar A.\", \"Rysavy\", , \"oscar-rysavy@uiowa.edu\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-1019-5724\")),\nperson(\"Patrick J.\", \"Breheny\", , \"patrick-breheny@uiowa.edu\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-0650-1119\"))\n)",
  "Description": "Fits penalized linear mixed models that correct for\nunobserved confounding factors. 'plmmr' infers and corrects for\nthe presence of unobserved confounding effects such as\npopulation stratification and environmental heterogeneity. It\nthen fits a linear model via penalized maximum likelihood.\nOriginally designed for the multivariate analysis of single\nnucleotide polymorphisms (SNPs) measured in a genome-wide\nassociation study (GWAS), 'plmmr' eliminates the need for\nsubpopulation-specific analyses and post-analysis p-value\nadjustments. Functions for the appropriate processing of\n'PLINK' files are also supplied. For examples, see the package\nhomepage <https://pbreheny.github.io/plmmr/>.",
  "License": "GPL-3",
  "URL": "https://pbreheny.github.io/plmmr/,\nhttps://github.com/pbreheny/plmmr/",
  "BugReports": "https://github.com/pbreheny/plmmr/issues/",
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  "Repository": "https://pbreheny.r-universe.dev",
  "Date/Publication": "2026-08-13 16:21:49 UTC",
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  "Author": "Tabitha K. Peter [aut] (ORCID: <https://orcid.org/0009-0005-2524-4751>),\nAnna C. Reisetter [aut] (ORCID:\n<https://orcid.org/0000-0001-8332-4585>),\nYujing Lu [aut],\nOscar A. Rysavy [aut] (ORCID: <https://orcid.org/0000-0002-1019-5724>),\nPatrick J. Breheny [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-0650-1119>)",
  "Maintainer": "Patrick J. Breheny <patrick-breheny@uiowa.edu>",
  "_user": "pbreheny",
  "_type": "src",
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  "_expires": "2027-01-04T10:09:08.000Z",
  "_created": "2026-09-26T10:00:04.000Z",
  "_published": "2026-09-26T10:09:10.435Z",
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    "process_plink",
    "relatedness_mat",
    "unzip_example_data"
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      "table": false,
      "tojson": true
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      "title": "Admix: Semi-simulated SNP data",
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      "title": "Coef method for 'cv_plmm' class",
      "topics": [
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      "title": "Coef method for 'plmm' class",
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      "title": "A function to create a design for PLMM modeling",
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      "title": "Cross-validation for plmm",
      "topics": [
        "cv_plmm"
      ]
    },
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      "page": "find_example_data",
      "title": "A function to help with accessing example PLINK files.",
      "topics": [
        "find_example_data"
      ]
    },
    {
      "page": "plmm",
      "title": "Fit a linear mixed model via penalized maximum likelihood.",
      "topics": [
        "plmm"
      ]
    },
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      "title": "Loss method for 'plmm' class",
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        "plmm_loss"
      ]
    },
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      "title": "Plot method for 'cv_plmm' class",
      "topics": [
        "plot.cv_plmm"
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    },
    {
      "page": "plot.plmm",
      "title": "Plot method for 'plmm' class",
      "topics": [
        "plot.plmm"
      ]
    },
    {
      "page": "predict.cv_plmm",
      "title": "Predict method for 'cv_plmm' class",
      "topics": [
        "predict.cv_plmm"
      ]
    },
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      "page": "predict.plmm",
      "title": "Predict method for 'plmm' class",
      "topics": [
        "predict.plmm"
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      "topics": [
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      "title": "A function to print the summary of a 'plmm' model",
      "topics": [
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