{
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  "Title": "SODA: Main and Interaction Effects Selection for Logistic\nRegression, Quadratic Discriminant and General Index Models",
  "Version": "1.2",
  "Date": "2018-05-12",
  "Author": "Yang Li, Jun S. Liu",
  "Maintainer": "Yang Li <yangli.stat@gmail.com>",
  "Description": "Variable and interaction selection are essential to\nclassification in high-dimensional setting. In this package, we\nprovide the implementation of SODA procedure, which is a\nforward-backward algorithm that selects both main and\ninteraction effects under logistic regression and quadratic\ndiscriminant analysis. We also provide an extension, S-SODA,\nfor dealing with the variable selection problem for\nsemi-parametric models with continuous responses.",
  "License": "GPL-2",
  "NeedsCompilation": "no",
  "Packaged": {
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    "User": "root"
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  "Repository": "https://cranhaven.r-universe.dev",
  "Date/Publication": "2026-10-12 12:02:00 UTC",
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    "name": "Yang Li",
    "email": "yangli.stat@gmail.com",
    "login": "yangli88",
    "description": "PhD graduate in Statistics",
    "uuid": 6255639
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    "packages",
    "r-universe"
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  "_contributors": [
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    "followers": 3,
    "description": "CRANhaven - Repository for Recently Archived CRAN Packages"
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/sodavis"
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  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/manual.pdf",
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  "_realowner": "yangli88",
  "_cranurl": false,
  "_releases": [
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      "date": "2015-11-16"
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    "s_soda_model",
    "s_soda_pred",
    "s_soda_pred_grid",
    "soda",
    "soda_trace_CV"
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        "array"
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      "tojson": true
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      "title": "Gene expression data for Michigan lung cancer study in Beer et al. (2002)",
      "object": "mich_lung",
      "file": "mich_lung.RData",
      "class": [
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      ],
      "fields": [],
      "table": false,
      "tojson": true
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      "title": "Pumadyn dataset",
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      "file": "pumadyn.RData",
      "class": [
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      ],
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        "theta2",
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        "theta4",
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        "theta6",
        "thetad1",
        "thetad2",
        "thetad3",
        "thetad4",
        "thetad5",
        "thetad6",
        "tau1",
        "tau2",
        "tau3",
        "tau4",
        "tau5",
        "dm1",
        "dm2",
        "dm3",
        "dm4",
        "dm5",
        "da1",
        "da2",
        "da3",
        "da4",
        "da5",
        "db1",
        "db2",
        "db3",
        "db4",
        "db5"
      ],
      "rows": 4499,
      "table": true,
      "tojson": true
    },
    {
      "name": "pumadyn_isample_y",
      "title": "Pumadyn dataset",
      "object": "pumadyn",
      "file": "pumadyn.RData",
      "class": [
        "numeric"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
    {
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      "title": "Pumadyn dataset",
      "object": "pumadyn",
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        "array"
      ],
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        "theta2",
        "theta3",
        "theta4",
        "theta5",
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        "thetad2",
        "thetad3",
        "thetad4",
        "thetad5",
        "thetad6",
        "tau1",
        "tau2",
        "tau3",
        "tau4",
        "tau5",
        "dm1",
        "dm2",
        "dm3",
        "dm4",
        "dm5",
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        "da2",
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      ],
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      "table": false,
      "tojson": true
    }
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  "_help": [
    {
      "page": "umich_lung",
      "title": "Gene expression data for Michigan lung cancer study in Beer et al. (2002)",
      "topics": [
        "mich_lung_xx",
        "mich_lung_yy"
      ]
    },
    {
      "page": "pumadyn",
      "title": "Pumadyn dataset",
      "topics": [
        "pumadyn_isample_x",
        "pumadyn_isample_y",
        "pumadyn_osample_x",
        "pumadyn_osample_y"
      ]
    },
    {
      "page": "s_soda",
      "title": "S-SODA algorithm for general index model variable selection",
      "topics": [
        "s_soda"
      ]
    },
    {
      "page": "s_soda_model",
      "title": "S-SODA model estimation.",
      "topics": [
        "s_soda_model"
      ]
    },
    {
      "page": "s_soda_pred",
      "title": "Predict the response y using S-SODA model.",
      "topics": [
        "s_soda_pred"
      ]
    },
    {
      "page": "s_soda_pred_grid",
      "title": "Predict the response y using S-SODA model in a 2-dimensional grid.",
      "topics": [
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      ]
    },
    {
      "page": "soda",
      "title": "SODA algorithm for variable and interaction selection",
      "topics": [
        "soda"
      ]
    },
    {
      "page": "soda_trace_CV",
      "title": "Calculate a trace of cross-validation error rate for SODA forward-backward procedure",
      "topics": [
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      ]
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