{
  "id": "1804.07134",
  "version": "v1",
  "published": "2018-04-19T13:12:03.000Z",
  "updated": "2018-04-19T13:12:03.000Z",
  "title": "varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets",
  "authors": [
    "Gilles Kratzer",
    "Reinhard Furrer"
  ],
  "comment": "18 pages, 4 figures",
  "categories": [
    "stat.ML",
    "cs.LG"
  ],
  "abstract": "This article describes the R package varrank. It has a flexible implementation of heuristic approaches which perform variable ranking based on mutual information. The package is particularly suitable for exploring multivariate datasets requiring a holistic analysis. The core functionality is a general implementation of the minimum redundancy maximum relevance (mRMRe) model. This approach is based on information theory metrics. It is compatible with discrete and continuous data which are discretised using a large choice of possible rules. The two main problems that can be addressed by this package are the selection of the most representative variables for modeling a collection of variables of interest, i.e., dimension reduction, and variable ranking with respect to a set of variables of interest.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-04-19T13:12:03.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "mutual information",
      "variable ranking",
      "systemic datasets",
      "applications",
      "minimum redundancy maximum relevance"
    ],
    "tags": [
      "research tool"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 18,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}