There is a newer version of this record available.

Software Open Access

hal9001: The Scalable Highly Adaptive Lasso

Coyle, Jeremy R; Hejazi, Nima S; van der Laan, Mark J


JSON Export

{
  "files": [
    {
      "links": {
        "self": "https://zenodo.org/api/files/55fb0bc1-cc9a-4a1e-b11a-dcc23fa4ce55/hal9001_0.2.6.tar.gz"
      }, 
      "checksum": "md5:7d7a2eace70e8250fe9b1c1f477eb5e9", 
      "bucket": "55fb0bc1-cc9a-4a1e-b11a-dcc23fa4ce55", 
      "key": "hal9001_0.2.6.tar.gz", 
      "type": "gz", 
      "size": 135421
    }
  ], 
  "owners": [
    27050
  ], 
  "doi": "10.5281/zenodo.3905440", 
  "stats": {
    "version_unique_downloads": 19.0, 
    "unique_views": 25.0, 
    "views": 31.0, 
    "version_views": 240.0, 
    "unique_downloads": 1.0, 
    "version_unique_views": 192.0, 
    "volume": 135421.0, 
    "version_downloads": 21.0, 
    "downloads": 1.0, 
    "version_volume": 5403713.0
  }, 
  "links": {
    "doi": "https://doi.org/10.5281/zenodo.3905440", 
    "conceptdoi": "https://doi.org/10.5281/zenodo.3558313", 
    "bucket": "https://zenodo.org/api/files/55fb0bc1-cc9a-4a1e-b11a-dcc23fa4ce55", 
    "conceptbadge": "https://zenodo.org/badge/doi/10.5281/zenodo.3558313.svg", 
    "html": "https://zenodo.org/record/3905440", 
    "latest_html": "https://zenodo.org/record/4050561", 
    "badge": "https://zenodo.org/badge/doi/10.5281/zenodo.3905440.svg", 
    "latest": "https://zenodo.org/api/records/4050561"
  }, 
  "conceptdoi": "10.5281/zenodo.3558313", 
  "created": "2020-06-24T01:50:55.919783+00:00", 
  "updated": "2020-09-25T21:51:21.591912+00:00", 
  "conceptrecid": "3558313", 
  "revision": 6, 
  "id": 3905440, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.5281/zenodo.3905440", 
    "description": "<p>A scalable implementation of the highly adaptive lasso algorithm,including routines for constructing sparse matrices of basis functions of the observed data, as well as a custom implementation of Lasso regression tailored to enhance efficiency when the matrix of predictors is composed exclusively of indicator basis functions. For ease of use and increased flexibility, the Lasso fitting routines may invoke code from the glmnet package optionally.</p>", 
    "license": {
      "id": "GPL-3.0-only"
    }, 
    "title": "hal9001: The Scalable Highly Adaptive Lasso", 
    "relations": {
      "version": [
        {
          "count": 4, 
          "index": 1, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "3558313"
          }, 
          "is_last": false, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "4050561"
          }
        }
      ]
    }, 
    "version": "v0.2.6", 
    "keywords": [
      "machine learning", 
      "semiparametric theory", 
      "nonparametric estimation"
    ], 
    "publication_date": "2020-06-23", 
    "creators": [
      {
        "orcid": "0000-0002-9874-6649", 
        "affiliation": "University of California, Berkeley", 
        "name": "Coyle, Jeremy R"
      }, 
      {
        "orcid": "0000-0002-7127-2789", 
        "affiliation": "University of California, Berkeley", 
        "name": "Hejazi, Nima S"
      }, 
      {
        "orcid": "0000-0003-1432-5511", 
        "affiliation": "University of California, Berkeley", 
        "name": "van der Laan, Mark J"
      }
    ], 
    "access_right": "open", 
    "resource_type": {
      "type": "software", 
      "title": "Software"
    }, 
    "related_identifiers": [
      {
        "scheme": "doi", 
        "identifier": "10.5281/zenodo.3558313", 
        "relation": "isVersionOf"
      }
    ]
  }
}
240
21
views
downloads
All versions This version
Views 24031
Downloads 211
Data volume 5.4 MB135.4 kB
Unique views 19225
Unique downloads 191

Share

Cite as