{
  "_id": "6a211a51cd65a98ecbd1d242",
  "Package": "MiRKAT",
  "Title": "Microbiome Regression-Based Kernel Association Tests",
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  "Authors@R": "c(person(\"Anna\", \"Plantinga\", role = c(\"aut\", \"cre\"), email = \"amp9@williams.edu\"),\nperson(\"Nehemiah\", \"Wilson\", role = c(\"aut\", \"ctb\")),\nperson(\"Haotian\", \"Zheng\", role = c(\"aut\", \"ctb\")),\nperson(\"Tianying\", \"Wang\", role = c(\"aut\", \"ctb\")),\nperson(\"Xiang\", \"Zhan\", role = c(\"aut\", \"ctb\")),\nperson(\"Michael\", \"Wu\", role = \"aut\"),\nperson(\"Ni\", \"Zhao\", role = c(\"aut\", \"ctb\")),\nperson(\"Jun\", \"Chen\", role = \"aut\"))",
  "Maintainer": "Anna Plantinga <amp9@williams.edu>",
  "Description": "Test for overall association between microbiome\ncomposition data and phenotypes via phylogenetic kernels. The\nphenotype can be univariate continuous or binary (Zhao et al.\n(2015) <doi:10.1016/j.ajhg.2015.04.003>), survival outcomes\n(Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>),\nmultivariate (Zhan et al. (2017) <doi:10.1002/gepi.22030>) and\nstructured phenotypes (Zhan et al. (2017)\n<doi:10.1111/biom.12684>). The package can also use robust\nregression (unpublished work) and integrated quantile\nregression (Wang et al. (2021)\n<doi:10.1093/bioinformatics/btab668>). In each case, the\nmicrobiome community effect is modeled nonparametrically\nthrough a kernel function, which can incorporate phylogenetic\ntree information.",
  "License": "GPL (>= 2)",
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  "NeedsCompilation": "no",
  "VignetteBuilder": "knitr, rmarkdown",
  "Packaged": {
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  "Author": "Anna Plantinga [aut, cre], Nehemiah Wilson [aut, ctb], Haotian\nZheng [aut, ctb], Tianying Wang [aut, ctb], Xiang Zhan [aut,\nctb], Michael Wu [aut], Ni Zhao [aut, ctb], Jun Chen [aut]",
  "Config/pak/sysreqs": "cmake make libicu-dev libuv1-dev libssl-dev",
  "Repository": "https://aplantin.r-universe.dev",
  "Date/Publication": "2023-02-17 13:50:02 UTC",
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  "_type": "src",
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  "_created": "2026-06-03T06:38:18.000Z",
  "_published": "2026-06-04T06:25:21.716Z",
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    "description": "Assistant professor of statistics at Williams College. Interested in the human microbiome, longitudinal analysis, and undergraduate statistics education. "
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/MiRKAT"
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  "_mentions": 30,
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  "_rbuild": "4.6.0",
  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
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    "extra/contents.json",
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  "_realowner": "aplantin",
  "_cranurl": false,
  "_releases": [
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      "date": "2017-07-25"
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      "date": "2020-08-27"
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      "date": "2021-01-22"
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      "date": "2021-05-26"
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    "MiRKAT",
    "MiRKAT.iQ",
    "MiRKAT.Q",
    "MiRKAT.R",
    "MiRKATS",
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    {
      "page": "D2K",
      "title": "D2K",
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    {
      "page": "GLMMMiRKAT",
      "title": "The Microbiome Regression-based Kernel Association Test Based on the Generalized Linear Mixed Model",
      "topics": [
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      ]
    },
    {
      "page": "inner.CSKAT",
      "title": "Inner Function for CSKAT, Correlated Sequence Kernel Association Test",
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      "title": "Microbiome Regression-based Kernel Association Test",
      "topics": [
        "MiRKAT"
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      "filename": "MiRKAT_Vignette.html",
      "title": "MiRKAT Package Vignette",
      "author": "Anna Plantinga, Nehemiah Wilson, Haotian Zheng, Xiang Zhan, Michael Wu, Jun Chen, Ni Zhao",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "Changes from v1.1.0",
        "Dependencies",
        "MiRKAT: Microbiome Regression-Based Kernel Association Test",
        "Example Dataset",
        "Data Preparation",
        "Create the UniFrac Distances",
        "Convert Distance to Kernel Matrices",
        "Testing Using a Single Kernel",
        "Measures of Effect Size",
        "Properties of Different Kernels",
        "Testing Using Multiple Kernels",
        "Testing with a Continuous Outcome Variable",
        "MiRKAT-S: Survival Outcome",
        "Simulate time to event data",
        "MMiRKAT: Multivariate Continuous Outcome",
        "KRV: High-Dimensional Structured Outcome",
        "Create the Kernel Matrix of Simulated Data",
        "Testing Using an Outcome Kernel Matrix",
        "Testing with Multiple Kernels, Using the Covariate Option",
        "Robust MiRKAT",
        "MiRKAT.R: Continuous Outcomes with Robust Linear Regression",
        "MiRKAT.Q: Continuous Outcomes with Quantile Regression",
        "GLMM-MiRKAT: Longitudinal/Correlated Continuous, Binary, or Count Data (Generalized Linear Mixed Model)",
        "Prepare the Data",
        "Example 1. Gaussian traits (e.g., body mass index)",
        "Example 2. Binomial traits (e.g., disease status, treatment/placebo)",
        "Example 3. Poisson traits (e.g., number of tumors)"
      ],
      "created": "2020-04-14 07:50:16",
      "modified": "2023-02-17 13:50:02",
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