{
  "_id": "6a1be75b1d7bb097a0a1b142",
  "Package": "corrselect",
  "Title": "Correlation-Based and Model-Based Predictor Pruning",
  "Version": "3.2.2",
  "Authors@R": "person(\"Gilles\", \"Colling\", email = \"gilles.colling051@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0003-3070-6066\"))",
  "Description": "Provides functions for predictor pruning using\nassociation-based and model-based approaches. Includes\ncorrPrune() for fast correlation-based pruning, modelPrune()\nfor VIF-based regression pruning, and exact graph-theoretic\nalgorithms (Eppstein–Löffler–Strash, Bron–Kerbosch) for\nexhaustive subset enumeration. Supports linear models, GLMs,\nand mixed models ('lme4', 'glmmTMB').",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.3",
  "VignetteBuilder": "knitr",
  "URL": "https://gillescolling.com/corrselect/",
  "BugReports": "https://github.com/gcol33/corrselect/issues",
  "LazyData": "true",
  "Repository": "https://gcol33.r-universe.dev",
  "Date/Publication": "2026-05-31 05:33:46 UTC",
  "RemoteUrl": "https://github.com/gcol33/corrselect",
  "RemoteRef": "HEAD",
  "RemoteSha": "859c8e225212cb11b5ba996c01e83310d71f602b",
  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-05-31 07:02:27 UTC",
    "User": "root"
  },
  "Author": "Gilles Colling [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0003-3070-6066>)",
  "Maintainer": "Gilles Colling <gilles.colling051@gmail.com>",
  "MD5sum": "d8921259f84969e8eaea904b38d782b1",
  "_user": "gcol33",
  "_type": "src",
  "_file": "corrselect_3.2.2.tar.gz",
  "_fileid": "217dbac3affafb15f6cc57cde3a1bb9f5bcf5c68833aa639f527e63b00cf5bc0",
  "_filesize": 848162,
  "_sha256": "217dbac3affafb15f6cc57cde3a1bb9f5bcf5c68833aa639f527e63b00cf5bc0",
  "_created": "2026-05-31T07:02:27.000Z",
  "_published": "2026-05-31T07:46:35.714Z",
  "_distro": "noble",
  "_jobs": [
    {
      "job": 78707344435,
      "time": 198,
      "config": "linux-devel-arm64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "7314660103"
    },
    {
      "job": 78707344405,
      "time": 197,
      "config": "linux-devel-x86_64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "7314660064"
    },
    {
      "job": 78707344430,
      "time": 192,
      "config": "linux-release-arm64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7314659628"
    },
    {
      "job": 78707344436,
      "time": 224,
      "config": "linux-release-x86_64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7314662687"
    },
    {
      "job": 78707344451,
      "time": 151,
      "config": "macos-oldrel-arm64",
      "r": "4.5.3",
      "check": "OK",
      "artifact": "7314939907"
    },
    {
      "job": 78707344433,
      "time": 307,
      "config": "macos-oldrel-x86_64",
      "r": "4.5.3",
      "check": "OK",
      "artifact": "7314673017"
    },
    {
      "job": 78707344432,
      "time": 126,
      "config": "macos-release-arm64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7314935540"
    },
    {
      "job": 78707344431,
      "time": 365,
      "config": "macos-release-x86_64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7314685600"
    },
    {
      "job": 78707072682,
      "time": 302,
      "config": "source",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7314639395"
    },
    {
      "job": 78707344398,
      "time": 181,
      "config": "wasm-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7314658474"
    },
    {
      "job": 78707344403,
      "time": 813,
      "config": "windows-devel",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "7314725037"
    },
    {
      "job": 78707344409,
      "time": 727,
      "config": "windows-oldrel",
      "r": "4.5.3",
      "check": "OK",
      "artifact": "7314714935"
    },
    {
      "job": 78707344401,
      "time": 433,
      "config": "windows-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7314683081"
    }
  ],
  "_buildurl": "https://github.com/r-universe/gcol33/actions/runs/26705924331",
  "_status": "success",
  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/gcol33/corrselect",
  "_commit": {
    "id": "859c8e225212cb11b5ba996c01e83310d71f602b",
    "author": "Gilles Colling <gilles.colling051@gmail.com>",
    "committer": "Gilles Colling <gilles.colling051@gmail.com>",
    "message": "docs(comparison): pin Boruta::getImpRfZ for reproducibility across Boruta 9->10\n\nBoruta 10 defaults to the stochastic fru importance backend. Pin getImpRfZ and gate the chunk on ranger so the seeded comparison vignette stays deterministic.\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>\n",
    "time": 1780205626
  },
  "_maintainer": {
    "name": "Gilles Colling",
    "email": "gilles.colling051@gmail.com",
    "login": "gcol33",
    "linkedin": "in/gilles-colling-0b3747306",
    "orcid": "0000-0003-3070-6066",
    "twitter": "@Gilles__Colling",
    "description": "Building tools for ecology in R, C++, and Rust.\nWhy did Microsoft have to buy it. Sadge :(",
    "uuid": 25009600
  },
  "_registered": true,
  "_dependencies": [
    {
      "package": "R",
      "version": ">= 3.5",
      "role": "Depends"
    },
    {
      "package": "Rcpp",
      "role": "LinkingTo"
    },
    {
      "package": "Rcpp",
      "role": "Imports"
    },
    {
      "package": "S7",
      "role": "Imports"
    },
    {
      "package": "stats",
      "role": "Imports"
    },
    {
      "package": "GO.db",
      "role": "Suggests"
    },
    {
      "package": "WGCNA",
      "role": "Suggests"
    },
    {
      "package": "preprocessCore",
      "role": "Suggests"
    },
    {
      "package": "impute",
      "role": "Suggests"
    },
    {
      "package": "energy",
      "role": "Suggests"
    },
    {
      "package": "minerva",
      "role": "Suggests"
    },
    {
      "package": "lme4",
      "role": "Suggests"
    },
    {
      "package": "glmmTMB",
      "role": "Suggests"
    },
    {
      "package": "MASS",
      "role": "Suggests"
    },
    {
      "package": "caret",
      "role": "Suggests"
    },
    {
      "package": "car",
      "role": "Suggests"
    },
    {
      "package": "carData",
      "role": "Suggests"
    },
    {
      "package": "microbenchmark",
      "role": "Suggests"
    },
    {
      "package": "igraph",
      "role": "Suggests"
    },
    {
      "package": "Boruta",
      "role": "Suggests"
    },
    {
      "package": "glmnet",
      "role": "Suggests"
    },
    {
      "package": "corrplot",
      "role": "Suggests"
    },
    {
      "package": "knitr",
      "role": "Suggests"
    },
    {
      "package": "rmarkdown",
      "role": "Suggests"
    },
    {
      "package": "testthat",
      "version": ">= 3.0.0",
      "role": "Suggests"
    },
    {
      "package": "tibble",
      "role": "Suggests"
    },
    {
      "package": "svglite",
      "role": "Suggests"
    },
    {
      "package": "data.table",
      "role": "Suggests"
    }
  ],
  "_owner": "gcol33",
  "_selfowned": true,
  "_usedby": 0,
  "_updates": [
    {
      "week": "2025-32",
      "n": 14
    },
    {
      "week": "2025-37",
      "n": 25
    },
    {
      "week": "2025-46",
      "n": 2
    },
    {
      "week": "2025-47",
      "n": 1
    },
    {
      "week": "2025-48",
      "n": 58
    },
    {
      "week": "2025-50",
      "n": 7
    },
    {
      "week": "2025-51",
      "n": 49
    },
    {
      "week": "2025-52",
      "n": 7
    },
    {
      "week": "2026-02",
      "n": 6
    },
    {
      "week": "2026-03",
      "n": 5
    },
    {
      "week": "2026-04",
      "n": 1
    },
    {
      "week": "2026-05",
      "n": 2
    },
    {
      "week": "2026-09",
      "n": 1
    },
    {
      "week": "2026-10",
      "n": 1
    },
    {
      "week": "2026-13",
      "n": 3
    },
    {
      "week": "2026-21",
      "n": 1
    },
    {
      "week": "2026-22",
      "n": 3
    }
  ],
  "_tags": [
    {
      "name": "v1.0.0",
      "date": "2025-08-05"
    },
    {
      "name": "v1.0.1",
      "date": "2025-08-05"
    },
    {
      "name": "v2.0.0",
      "date": "2025-08-05"
    },
    {
      "name": "v2.0.1",
      "date": "2025-08-06"
    },
    {
      "name": "v3.0.0",
      "date": "2025-11-25"
    },
    {
      "name": "v3.0.1",
      "date": "2025-11-25"
    },
    {
      "name": "v3.0.2",
      "date": "2025-11-26"
    },
    {
      "name": "v3.0.3",
      "date": "2025-12-16"
    },
    {
      "name": "v3.0.4",
      "date": "2025-12-16"
    },
    {
      "name": "v3.0.5",
      "date": "2025-12-16"
    },
    {
      "name": "v3.0.6",
      "date": "2025-12-21"
    },
    {
      "name": "v3.0.7",
      "date": "2025-12-22"
    },
    {
      "name": "v3.1.0",
      "date": "2026-01-30"
    },
    {
      "name": "v3.2.0",
      "date": "2026-03-23"
    },
    {
      "name": "v3.2.2",
      "date": "2026-03-23"
    }
  ],
  "_topics": [
    "correlation",
    "enumeration",
    "feature-selection-",
    "glm",
    "graph-algorithms",
    "machine-learning",
    "mixed-models",
    "multicollinearity",
    "regression",
    "statistics",
    "variable-selection",
    "vif",
    "cpp"
  ],
  "_stars": 2,
  "_contributors": [
    {
      "user": "gcol33",
      "count": 188,
      "uuid": 25009600
    },
    {
      "user": "nikoleta-v3",
      "count": 1,
      "uuid": 19708408
    }
  ],
  "_userbio": {
    "uuid": 25009600,
    "type": "user",
    "name": "Gilles Colling",
    "description": "Building tools for ecology in R, C++, and Rust.\nWhy did Microsoft have to buy it. Sadge :("
  },
  "_downloads": {
    "count": 589,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/corrselect"
  },
  "_devurl": "https://github.com/gcol33/corrselect",
  "_pkgdown": "https://gillescolling.com/corrselect/",
  "_searchresults": 15,
  "_rbuild": "4.6.0",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/corrselect.html",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "manual.pdf"
  ],
  "_homeurl": "https://github.com/gcol33/corrselect",
  "_realowner": "gcol33",
  "_cranurl": true,
  "_releases": [
    {
      "version": "2.0.1",
      "date": "2025-09-08"
    },
    {
      "version": "3.0.2",
      "date": "2025-11-29"
    },
    {
      "version": "3.0.5",
      "date": "2025-12-16"
    },
    {
      "version": "3.1.0",
      "date": "2026-01-08"
    },
    {
      "version": "3.2.1",
      "date": "2026-03-23"
    }
  ],
  "_exports": [
    "assocSelect",
    "CorrCombo",
    "corrPrune",
    "corrSelect",
    "corrSubset",
    "MatSelect",
    "modelPrune"
  ],
  "_datasets": [
    {
      "name": "bioclim_example",
      "title": "Example Bioclimatic Data for Ecological Modeling",
      "object": "bioclim_example",
      "class": [
        "data.frame"
      ],
      "fields": [
        "species_richness",
        "BIO1",
        "BIO2",
        "BIO3",
        "BIO4",
        "BIO5",
        "BIO6",
        "BIO7",
        "BIO8",
        "BIO9",
        "BIO10",
        "BIO11",
        "BIO12",
        "BIO13",
        "BIO14",
        "BIO15",
        "BIO16",
        "BIO17",
        "BIO18",
        "BIO19"
      ],
      "rows": 100,
      "table": true,
      "tojson": true
    },
    {
      "name": "cor_example",
      "title": "Example Correlation Matrix with Block Structure",
      "object": "cor_example",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "V1",
        "V2",
        "V3",
        "V4",
        "V5",
        "V6",
        "V7",
        "V8",
        "V9",
        "V10",
        "V11",
        "V12",
        "V13",
        "V14",
        "V15",
        "V16",
        "V17",
        "V18",
        "V19",
        "V20"
      ],
      "rows": 20,
      "table": true,
      "tojson": true
    },
    {
      "name": "genes_example",
      "title": "Example Gene Expression Data for Bioinformatics",
      "object": "genes_example",
      "class": [
        "data.frame"
      ],
      "fields": [
        "sample_id",
        "disease_status",
        "GENE001",
        "GENE002",
        "GENE003",
        "GENE004",
        "GENE005",
        "GENE006",
        "GENE007",
        "GENE008",
        "GENE009",
        "GENE010",
        "GENE011",
        "GENE012",
        "GENE013",
        "GENE014",
        "GENE015",
        "GENE016",
        "GENE017",
        "GENE018",
        "GENE019",
        "GENE020",
        "GENE021",
        "GENE022",
        "GENE023",
        "GENE024",
        "GENE025",
        "GENE026",
        "GENE027",
        "GENE028",
        "GENE029",
        "GENE030",
        "GENE031",
        "GENE032",
        "GENE033",
        "GENE034",
        "GENE035",
        "GENE036",
        "GENE037",
        "GENE038",
        "GENE039",
        "GENE040",
        "GENE041",
        "GENE042",
        "GENE043",
        "GENE044",
        "GENE045",
        "GENE046",
        "GENE047",
        "GENE048",
        "GENE049",
        "GENE050",
        "GENE051",
        "GENE052",
        "GENE053",
        "GENE054",
        "GENE055",
        "GENE056",
        "GENE057",
        "GENE058",
        "GENE059",
        "GENE060",
        "GENE061",
        "GENE062",
        "GENE063",
        "GENE064",
        "GENE065",
        "GENE066",
        "GENE067",
        "GENE068",
        "GENE069",
        "GENE070",
        "GENE071",
        "GENE072",
        "GENE073",
        "GENE074",
        "GENE075",
        "GENE076",
        "GENE077",
        "GENE078",
        "GENE079",
        "GENE080",
        "GENE081",
        "GENE082",
        "GENE083",
        "GENE084",
        "GENE085",
        "GENE086",
        "GENE087",
        "GENE088",
        "GENE089",
        "GENE090",
        "GENE091",
        "GENE092",
        "GENE093",
        "GENE094",
        "GENE095",
        "GENE096",
        "GENE097",
        "GENE098",
        "GENE099",
        "GENE100",
        "GENE101",
        "GENE102",
        "GENE103",
        "GENE104",
        "GENE105",
        "GENE106",
        "GENE107",
        "GENE108",
        "GENE109",
        "GENE110",
        "GENE111",
        "GENE112",
        "GENE113",
        "GENE114",
        "GENE115",
        "GENE116",
        "GENE117",
        "GENE118",
        "GENE119",
        "GENE120",
        "GENE121",
        "GENE122",
        "GENE123",
        "GENE124",
        "GENE125",
        "GENE126",
        "GENE127",
        "GENE128",
        "GENE129",
        "GENE130",
        "GENE131",
        "GENE132",
        "GENE133",
        "GENE134",
        "GENE135",
        "GENE136",
        "GENE137",
        "GENE138",
        "GENE139",
        "GENE140",
        "GENE141",
        "GENE142",
        "GENE143",
        "GENE144",
        "GENE145",
        "GENE146",
        "GENE147",
        "GENE148",
        "GENE149",
        "GENE150",
        "GENE151",
        "GENE152",
        "GENE153",
        "GENE154",
        "GENE155",
        "GENE156",
        "GENE157",
        "GENE158",
        "GENE159",
        "GENE160",
        "GENE161",
        "GENE162",
        "GENE163",
        "GENE164",
        "GENE165",
        "GENE166",
        "GENE167",
        "GENE168",
        "GENE169",
        "GENE170",
        "GENE171",
        "GENE172",
        "GENE173",
        "GENE174",
        "GENE175",
        "GENE176",
        "GENE177",
        "GENE178",
        "GENE179",
        "GENE180",
        "GENE181",
        "GENE182",
        "GENE183",
        "GENE184",
        "GENE185",
        "GENE186",
        "GENE187",
        "GENE188",
        "GENE189",
        "GENE190",
        "GENE191",
        "GENE192",
        "GENE193",
        "GENE194",
        "GENE195",
        "GENE196",
        "GENE197",
        "GENE198",
        "GENE199",
        "GENE200"
      ],
      "rows": 100,
      "table": true,
      "tojson": true
    },
    {
      "name": "longitudinal_example",
      "title": "Example Longitudinal Data for Clinical Research",
      "object": "longitudinal_example",
      "class": [
        "data.frame"
      ],
      "fields": [
        "obs_id",
        "subject",
        "site",
        "time",
        "outcome",
        "x1",
        "x2",
        "x3",
        "x4",
        "x5",
        "x6",
        "x7",
        "x8",
        "x9",
        "x10",
        "x11",
        "x12",
        "x13",
        "x14",
        "x15",
        "x16",
        "x17",
        "x18",
        "x19",
        "x20"
      ],
      "rows": 500,
      "table": true,
      "tojson": true
    },
    {
      "name": "survey_example",
      "title": "Example Survey Data for Social Science Research",
      "object": "survey_example",
      "class": [
        "data.frame"
      ],
      "fields": [
        "respondent_id",
        "age",
        "gender",
        "education",
        "overall_satisfaction",
        "satisfaction_1",
        "satisfaction_2",
        "satisfaction_3",
        "satisfaction_4",
        "satisfaction_5",
        "satisfaction_6",
        "satisfaction_7",
        "satisfaction_8",
        "satisfaction_9",
        "satisfaction_10",
        "engagement_1",
        "engagement_2",
        "engagement_3",
        "engagement_4",
        "engagement_5",
        "engagement_6",
        "engagement_7",
        "engagement_8",
        "engagement_9",
        "engagement_10",
        "loyalty_1",
        "loyalty_2",
        "loyalty_3",
        "loyalty_4",
        "loyalty_5",
        "loyalty_6",
        "loyalty_7",
        "loyalty_8",
        "loyalty_9",
        "loyalty_10"
      ],
      "rows": 200,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "corrselect-package",
      "title": "corrselect: Correlation-Based and Model-Based Predictor Pruning",
      "topics": [
        "corrselect-package",
        "corrselect"
      ]
    },
    {
      "page": "as.data.frame.CorrCombo",
      "title": "Coerce CorrCombo to a Data Frame",
      "topics": [
        "as.data.frame.CorrCombo"
      ]
    },
    {
      "page": "assocSelect",
      "title": "Select Variable Subsets with Low Association (Mixed-Type Data Frame Interface)",
      "topics": [
        "assocSelect"
      ]
    },
    {
      "page": "bioclim_example",
      "title": "Example Bioclimatic Data for Ecological Modeling",
      "topics": [
        "bioclim_example"
      ]
    },
    {
      "page": "cor_example",
      "title": "Example Correlation Matrix with Block Structure",
      "topics": [
        "cor_example"
      ]
    },
    {
      "page": "CorrCombo",
      "title": "CorrCombo Class",
      "topics": [
        "CorrCombo",
        "print.CorrCombo"
      ]
    },
    {
      "page": "corrPrune",
      "title": "Association-Based Predictor Pruning",
      "topics": [
        "corrPrune"
      ]
    },
    {
      "page": "corrSelect",
      "title": "Select Variable Subsets with Low Correlation (Data Frame Interface)",
      "topics": [
        "corrSelect"
      ]
    },
    {
      "page": "corrSubset",
      "title": "Extract Variable Subsets from a CorrCombo Object",
      "topics": [
        "corrSubset"
      ]
    },
    {
      "page": "genes_example",
      "title": "Example Gene Expression Data for Bioinformatics",
      "topics": [
        "genes_example"
      ]
    },
    {
      "page": "longitudinal_example",
      "title": "Example Longitudinal Data for Clinical Research",
      "topics": [
        "longitudinal_example"
      ]
    },
    {
      "page": "MatSelect",
      "title": "Select Variable Subsets with Low Correlation or Association (Matrix Interface)",
      "topics": [
        "MatSelect"
      ]
    },
    {
      "page": "modelPrune",
      "title": "Model-Based Predictor Pruning",
      "topics": [
        "modelPrune"
      ]
    },
    {
      "page": "survey_example",
      "title": "Example Survey Data for Social Science Research",
      "topics": [
        "survey_example"
      ]
    }
  ],
  "_readme": "https://github.com/gcol33/corrselect/raw/HEAD/README.md",
  "_rundeps": [
    "Rcpp",
    "S7"
  ],
  "_sysdeps": [
    {
      "shlib": "libstdc++",
      "package": "libstdc++6",
      "source": "gcc",
      "version": "14.2.0-4ubuntu2~24.04.1",
      "name": "c++",
      "homepage": "http://gcc.gnu.org/",
      "description": "GNU Standard C++ Library v3"
    }
  ],
  "_vignettes": [
    {
      "source": "advanced.Rmd",
      "filename": "advanced.html",
      "title": "Advanced Topics",
      "author": "Gilles Colling",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "1. Understanding the Algorithms",
        "1.1 Exact vs Greedy: When to Use Each",
        "Exact Mode (Graph-Theoretic)",
        "Greedy Mode (Heuristic)",
        "Auto Mode (Recommended)",
        "1.2 Complexity Analysis with Benchmarks",
        "1.3 Deterministic Tie-Breaking",
        "1.4 Grouped Pruning",
        "Basic Usage",
        "Parameters",
        "When to Use",
        "2. Custom Engines for modelPrune()",
        "2.1 Understanding Custom Engines",
        "What is a Custom Engine?",
        "Built-in Criteria",
        "How Custom Engines Work",
        "Engine Structure Requirements",
        "2.2 Example: INLA Engine (Bayesian Spatial Models)",
        "Background",
        "Implementation",
        "How It Works",
        "2.3 Example: mgcv Engine (GAMs)",
        "2.4 Example: Custom Criterion (AIC-Based)",
        "2.5 Validation and Error Handling",
        "Automatic Validation",
        "Validation Checklist",
        "Debugging Tips",
        "3. Exact Subset Enumeration",
        "3.1 When You Need ALL Maximal Subsets",
        "3.2 Exploring Multiple Subsets",
        "3.3 Extracting Specific Subsets",
        "3.4 Advanced: Domain-Specific Subset Selection",
        "4. Performance Optimization",
        "4.1 Precomputed Correlation Matrices",
        "4.2 Memory Considerations for Large Data",
        "Memory-Efficient Correlation Computation",
        "Sparse Correlation Matrices",
        "4.3 Parallel Processing Strategies",
        "4.4 Choosing the Right Mode",
        "5. Troubleshooting",
        "5.1 Common Errors and Solutions",
        "Error: \"No valid subsets found\"",
        "Error: force_in variables conflict with threshold",
        "Error: VIF computation fails in modelPrune()",
        "5.2 Threshold Selection Guidance",
        "For corrPrune() (Correlation Threshold)",
        "For modelPrune() (VIF Limit)",
        "Empirical Approach: Visualize First",
        "5.3 Handling Edge Cases",
        "Single Predictor After Pruning",
        "All Variables Removed",
        "Mixed-Type Data",
        "6. Best Practices",
        "6.1 Workflow Recommendations",
        "For Exploratory Analysis",
        "For Publication-Quality Analysis",
        "6.2 Combining with Other Methods",
        "7. Summary",
        "Key Takeaways",
        "Algorithms",
        "Custom Engines",
        "Optimization",
        "Troubleshooting",
        "8. References",
        "See Also",
        "Session Info"
      ],
      "created": "2025-11-20 18:36:10",
      "modified": "2026-02-24 13:51:36",
      "commits": 16
    },
    {
      "source": "comparison.Rmd",
      "filename": "comparison.html",
      "title": "Comparison with Alternatives",
      "author": "Gilles Colling",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "Evaluation Dataset",
        "Comparison 1: caret::findCorrelation()",
        "Method",
        "Execution",
        "Distribution Comparison",
        "Comparison",
        "Applications",
        "Comparison 2: Boruta",
        "Sequential Application",
        "Comparison 3: glmnet (LASSO/Ridge)",
        "Coefficient Comparison",
        "Comparison 4: modelPrune() vs Manual VIF Removal",
        "Manual Implementation",
        "modelPrune() Comparison",
        "Visual: VIF Comparison",
        "Summary",
        "Method Selection",
        "corrselect Distinguishing Features",
        "Integrated Workflow",
        "References",
        "See Also",
        "Session Info"
      ],
      "created": "2025-11-20 18:36:10",
      "modified": "2026-05-31 05:33:46",
      "commits": 15
    },
    {
      "source": "workflows.Rmd",
      "filename": "workflows.html",
      "title": "Complete Workflows: Real-World Examples",
      "author": "Gilles Colling",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "Workflow 1: Ecological Modeling",
        "Data",
        "Correlation-based pruning",
        "Correlation distribution",
        "Fit models",
        "Model comparison",
        "Visualization",
        "Coefficient stability",
        "Workflow 2: Survey Data Analysis",
        "Prune with protected variables",
        "Construct coverage",
        "Model satisfaction",
        "Workflow 3: High-Dimensional Data",
        "Greedy pruning",
        "Dimensionality reduction",
        "Exact vs greedy comparison",
        "Classification",
        "Workflow 4: Mixed Models",
        "Prune fixed effects",
        "Final model",
        "VIF verification",
        "VIF reduction visualization",
        "Summary",
        "See Also",
        "References",
        "Session Info"
      ],
      "created": "2025-11-20 18:36:10",
      "modified": "2026-02-24 13:51:36",
      "commits": 14
    },
    {
      "source": "quickstart.Rmd",
      "filename": "quickstart.html",
      "title": "Quick Start",
      "author": "Gilles Colling",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Installation",
        "What corrselect Does",
        "Interface Hierarchy",
        "Level 1: Simple Pruning",
        "Level 2: Structured Subset Selection",
        "Level 3: Low-Level Matrix Interface",
        "Quick Examples",
        "corrPrune(): Association-Based Pruning",
        "modelPrune(): VIF-Based Pruning",
        "corrSelect(): Enumerate All Maximal Subsets",
        "assocSelect(): Mixed-Type Data",
        "Protecting Variables",
        "Threshold Selection",
        "Interface Selection Guide",
        "Quick Reference",
        "corrPrune()",
        "modelPrune()",
        "corrSelect()",
        "assocSelect()",
        "MatSelect()",
        "corrSubset()",
        "Troubleshooting",
        "See Also",
        "Session Info"
      ],
      "created": "2025-11-20 18:36:10",
      "modified": "2026-03-23 09:16:47",
      "commits": 17
    },
    {
      "source": "theory.Rmd",
      "filename": "theory.html",
      "title": "Theory and Formulation",
      "author": "Gilles Colling",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "Contents",
        "Terminology",
        "Association measure",
        "Association matrix",
        "Threshold ((\\tau))",
        "Valid subset",
        "Maximal valid subset",
        "Threshold graph",
        "Clique",
        "Maximal clique",
        "Forced-in variables (force_in)",
        "ELS (Eppstein–Löffler–Strash)",
        "Bron–Kerbosch",
        "Greedy mode",
        "Exact mode",
        "Auto mode",
        "Intuitive Overview",
        "The Core Idea",
        "How corrselect Works",
        "Why \"Maximal\" Not \"Maximum\"?",
        "Toy Example (4 Variables)",
        "Graph Representation",
        "Visual Graph Representation",
        "Network Visualization with cor_example",
        "Finding Maximal Cliques",
        "Key Insight",
        "Problem Formulation",
        "Intuitive Problem Statement",
        "Formal Problem Statement",
        "Association Matrix",
        "Threshold Constraint",
        "Maximal Valid Subsets",
        "Graph-Theoretic Interpretation",
        "Why Graphs?",
        "Threshold Graph",
        "Maximal Cliques",
        "Example: 6-Variable Threshold Graph",
        "From Theory to Implementation",
        "Threshold ((\\tau)) → threshold argument",
        "Maximal cliques → Returned subsets",
        "Forced-in set ((F)) → force_in argument",
        "Search type → mode and method arguments",
        "Association matrix ((A)) → Data input and matrix-based functions",
        "Graph density → Performance considerations",
        "Example mapping",
        "Search Algorithms",
        "Exact Enumeration",
        "Eppstein–Löffler–Strash (ELS)",
        "Pseudocode for Practitioners",
        "Greedy Heuristic",
        "Algorithm Pseudocode",
        "Bron–Kerbosch with Pivoting",
        "Forced Variables",
        "Graph Modification",
        "Correlation vs Association",
        "Complexity Analysis",
        "Output Structure",
        "Design Philosophy",
        "Why Hard Threshold Not Soft Constraint?",
        "Why Graph Algorithms Not Optimization?",
        "Why Pairwise Associations Only?",
        "Why Enumerate All Solutions Not Just Return One?",
        "References",
        "Graph-Theoretic Algorithms",
        "Multicollinearity and Variable Selection",
        "Association Measures",
        "Threshold Graph Theory",
        "Computational Complexity",
        "Applications",
        "See Also",
        "Session Info"
      ],
      "created": "2025-11-25 15:32:13",
      "modified": "2026-03-23 09:09:08",
      "commits": 12
    }
  ],
  "_score": 6.105510184769974,
  "_indexed": true,
  "_nocasepkg": "corrselect",
  "_universes": [
    "gcol33"
  ],
  "_binaries": [
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "3.2.2",
      "date": "2026-05-31T07:05:36.000Z",
      "distro": "noble",
      "arch": "aarch64",
      "commit": "859c8e225212cb11b5ba996c01e83310d71f602b",
      "fileid": "6eae7a869214b45e3553775a6c9365b0c17e3b80cf27687dc1e364f243bd6b86",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/gcol33/actions/runs/26705924331"
    },
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "3.2.2",
      "date": "2026-05-31T07:05:33.000Z",
      "distro": "noble",
      "arch": "x86_64",
      "commit": "859c8e225212cb11b5ba996c01e83310d71f602b",
      "fileid": "50216b11048c36a669ff02ac6fb14ad9ac1b660e3284155b1ec3436bf37f39c7",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/gcol33/actions/runs/26705924331"
    },
    {
      "r": "4.6.0",
      "os": "linux",
      "version": "3.2.2",
      "date": "2026-05-31T07:05:34.000Z",
      "distro": "noble",
      "arch": "aarch64",
      "commit": "859c8e225212cb11b5ba996c01e83310d71f602b",
      "fileid": "a84a7932cd23b4ff02fbc5ea665ae13e75007d509ea97748f73648edd41f158e",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/gcol33/actions/runs/26705924331"
    },
    {
      "r": "4.6.0",
      "os": "linux",
      "version": "3.2.2",
      "date": "2026-05-31T07:05:59.000Z",
      "distro": "noble",
      "arch": "x86_64",
      "commit": "859c8e225212cb11b5ba996c01e83310d71f602b",
      "fileid": "049a881e5c8d07500e55602db2ccc8e569663e463c31441fddedc8d894538264",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/gcol33/actions/runs/26705924331"
    },
    {
      "r": "4.5.3",
      "os": "mac",
      "version": "3.2.2",
      "date": "2026-05-31T07:45:35.000Z",
      "arch": "aarch64",
      "commit": "859c8e225212cb11b5ba996c01e83310d71f602b",
      "fileid": "b634b0239ee50080331682b630216144eb70f3c4793af7909f0904f0a3a3ebc3",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/gcol33/actions/runs/26705924331"
    },
    {
      "r": "4.5.3",
      "os": "mac",
      "version": "3.2.2",
      "date": "2026-05-31T07:07:01.000Z",
      "arch": "x86_64",
      "commit": "859c8e225212cb11b5ba996c01e83310d71f602b",
      "fileid": "c8d7c4fd2e41b1753d2d0f5f57c938fa0a97d4034d9c5c20bdbaba52d0f0f376",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/gcol33/actions/runs/26705924331"
    },
    {
      "r": "4.6.0",
      "os": "mac",
      "version": "3.2.2",
      "date": "2026-05-31T07:45:08.000Z",
      "arch": "aarch64",
      "commit": "859c8e225212cb11b5ba996c01e83310d71f602b",
      "fileid": "d65aad77bed9bf176c8260ede35f0c8a877f807cd8c206486753664e35abc48b",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/gcol33/actions/runs/26705924331"
    },
    {
      "r": "4.6.0",
      "os": "mac",
      "version": "3.2.2",
      "date": "2026-05-31T07:08:33.000Z",
      "arch": "x86_64",
      "commit": "859c8e225212cb11b5ba996c01e83310d71f602b",
      "fileid": "4213341281c94338aa08e948c7a656d62eb8f8ebfeb62cefb445e8fb8180f684",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/gcol33/actions/runs/26705924331"
    },
    {
      "r": "4.6.0",
      "os": "wasm",
      "version": "3.2.2",
      "date": "2026-05-31T07:05:47.000Z",
      "arch": "emscripten",
      "commit": "859c8e225212cb11b5ba996c01e83310d71f602b",
      "fileid": "13ca698700b8ba7a9c2da0946d59d14daa1dcd95065a9dec0d4fe2b80ea50810",
      "status": "success",
      "buildurl": "https://github.com/r-universe/gcol33/actions/runs/26705924331"
    },
    {
      "r": "4.7.0",
      "os": "win",
      "version": "3.2.2",
      "date": "2026-05-31T07:15:16.000Z",
      "arch": "x86_64",
      "commit": "859c8e225212cb11b5ba996c01e83310d71f602b",
      "fileid": "eb6d2173466e62483fd51ede13c9b2cb3f9b01b6b14dfe43811ffaeba139c25b",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/gcol33/actions/runs/26705924331"
    },
    {
      "r": "4.5.3",
      "os": "win",
      "version": "3.2.2",
      "date": "2026-05-31T07:13:57.000Z",
      "arch": "x86_64",
      "commit": "859c8e225212cb11b5ba996c01e83310d71f602b",
      "fileid": "f9ca74c0d8c02ef4ab2f6c6014b072392fca300f5c1038eb2995dd06fc74da83",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/gcol33/actions/runs/26705924331"
    },
    {
      "r": "4.6.0",
      "os": "win",
      "version": "3.2.2",
      "date": "2026-05-31T07:09:09.000Z",
      "arch": "x86_64",
      "commit": "859c8e225212cb11b5ba996c01e83310d71f602b",
      "fileid": "4f4dfcb9e029121213add8b71973dd71173112374c8bf221532eac56464e4bfe",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/gcol33/actions/runs/26705924331"
    }
  ]
}