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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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  "Date/Publication": "2018-05-13 20:24:03 UTC",
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        "pumadyn_osample_y"
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