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  "Date": "2026-01-15",
  "Title": "Exact Variable-Subset Selection in Linear Regression",
  "Authors@R": "c(person(given = \"Marc\", family = \"Hofmann\",\nrole  = c(\"aut\", \"cre\"),\nemail = \"marc.hofmann@gmail.com\"),\nperson(given = \"Cristian\", family = \"Gatu\",\nrole  = \"aut\",\nemail = \"cgatu@info.uaic.ro\"),\nperson(given = \"Erricos J.\", family = \"Kontoghiorghes\",\nrole = \"aut\",\nemail = \"erricos@dcs.bbk.ac.uk\"),\nperson(given = \"Ana\", family = \"Colubi\",\nrole = \"aut\",\nemail = \"ana.colubi@gmail.com\"),\nperson(given = \"Achim\", family = \"Zeileis\",\nrole = \"aut\",\nemail = \"Achim.Zeileis@R-project.org\",\ncomment = c(ORCID = \"0000-0003-0918-3766\")),\nperson(given = \"Martin\", family = \"Moene\",\nrole = \"cph\",\ncomment = \"for the GSL Lite library\"),\nperson(given = \"Microsoft Corporation\",\nrole = \"cph\",\ncomment = \"for the GSL Lite library\"))",
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  "Description": "Exact and approximation algorithms for variable-subset\nselection in ordinary linear regression models.  Either compute\nall submodels with the lowest residual sum of squares, or\ndetermine the single-best submodel according to a\npre-determined statistical criterion.  Hofmann et al. (2020)\n<doi:10.18637/jss.v093.i03>.",
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    {
      "page": "lmSubsets-package",
      "title": "Package 'lmSubsets'",
      "topics": [
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      ]
    },
    {
      "page": "AIC.lmSubsets",
      "title": "Extract AIC values from a subset regression",
      "topics": [
        "AIC.lmSelect",
        "AIC.lmSubsets"
      ]
    },
    {
      "page": "AirPollution",
      "title": "Air pollution and mortality",
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    },
    {
      "page": "BIC.lmSubsets",
      "title": "Extract BIC values from a subset regression",
      "topics": [
        "BIC.lmSelect",
        "BIC.lmSubsets"
      ]
    },
    {
      "page": "coef.lmSubsets",
      "title": "Extract the ceofficients from a subset regression",
      "topics": [
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        "coef.lmSubsets"
      ]
    },
    {
      "page": "deviance.lmSubsets",
      "title": "Extract the deviance from a subset regression",
      "topics": [
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        "deviance.lmSubsets"
      ]
    },
    {
      "page": "fitted.lmSubsets",
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      "topics": [
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        "fitted.lmSubsets"
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    {
      "page": "formula.lmSubsets",
      "title": "Extract a formula from a subset regression",
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        "formula.lmSubsets"
      ]
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    },
    {
      "page": "image.lmSubsets",
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      "topics": [
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        "image.lmSubsets"
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      "topics": [
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    {
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      "topics": [
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        "print.lmSubsets"
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    {
      "page": "lmSubsets_fit",
      "title": "All-subsets regression",
      "topics": [
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    {
      "page": "lmSubsets.matrix",
      "title": "All-subsets regression",
      "topics": [
        "lmSubsets.matrix"
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    },
    {
      "page": "logLik.lmSubsets",
      "title": "Extract the log-likelihood from a subset regression",
      "topics": [
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        "logLik.lmSubsets"
      ]
    },
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      "title": "Model response",
      "topics": [
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        "model_response.default"
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    },
    {
      "page": "model_response.lmSubsets",
      "title": "Extract the model response from a subset regression",
      "topics": [
        "model_response.lmSelect",
        "model_response.lmSubsets"
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    },
    {
      "page": "model.frame.lmSubsets",
      "title": "Extract the model frame from a subset regression",
      "topics": [
        "model.frame.lmSelect",
        "model.frame.lmSubsets"
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    },
    {
      "page": "model.matrix.lmSubsets",
      "title": "Extract a model matrix from a subset regression",
      "topics": [
        "model.matrix.lmSelect",
        "model.matrix.lmSubsets"
      ]
    },
    {
      "page": "plot.lmSubsets",
      "title": "Plot a subset regression",
      "topics": [
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        "plot.lmSubsets"
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        "summary.lmSelect",
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        "variable.names.lmSubsets"
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