{
  "id": "2006.01635",
  "version": "v1",
  "published": "2020-05-30T23:42:36.000Z",
  "updated": "2020-05-30T23:42:36.000Z",
  "title": "direpack: A Python 3 package for state-of-the-art statistical dimension reduction methods",
  "authors": [
    "Emmanuel Jordy Menvouta",
    "Sven Serneels",
    "Tim Verdonck"
  ],
  "categories": [
    "stat.CO"
  ],
  "abstract": "The direpack package aims to establish a set of modern statistical dimension reduction techniques into the Python universe as a single, consistent package. The dimension reduction methods included resort into three categories: projection pursuit based dimension reduction, sufficient dimension reduction, and robust M estimators for dimension reduction. As a corollary, regularized regression estimators based on these reduced dimension spaces are provided as well, ranging from classical principal component regression up to sparse partial robust M regression. The package also contains a set of classical and robust pre-processing utilities, including generalized spatial signs, as well as dedicated plotting functionality and cross-validation utilities. Finally, direpack has been written consistent with the scikit-learn API, such that the estimators can flawlessly be included into (statistical and/or machine) learning pipelines in that framework.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-05-30T23:42:36.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "62H20",
      "62H12",
      "62H25",
      "62P99"
    ],
    "keywords": [
      "state-of-the-art statistical dimension reduction methods",
      "statistical dimension reduction techniques",
      "estimators",
      "sufficient dimension reduction",
      "direpack package aims"
    ],
    "tags": [
      "research tool"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}