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hal9001: Scalable highly adaptive lasso regression in R

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


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  <identifier identifierType="DOI">10.5281/zenodo.4050561</identifier>
  <creators>
    <creator>
      <creatorName>Hejazi, Nima S</creatorName>
      <givenName>Nima S</givenName>
      <familyName>Hejazi</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-7127-2789</nameIdentifier>
      <affiliation>University of California, Berkeley</affiliation>
    </creator>
    <creator>
      <creatorName>Coyle, Jeremy R</creatorName>
      <givenName>Jeremy R</givenName>
      <familyName>Coyle</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9874-6649</nameIdentifier>
      <affiliation>University of California, Berkeley</affiliation>
    </creator>
    <creator>
      <creatorName>van der Laan, Mark J</creatorName>
      <givenName>Mark J</givenName>
      <familyName>van der Laan</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1432-5511</nameIdentifier>
      <affiliation>University of California, Berkeley</affiliation>
    </creator>
  </creators>
  <titles>
    <title>hal9001: Scalable highly adaptive lasso regression in R</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>machine learning</subject>
    <subject>semiparametric theory</subject>
    <subject>nonparametric estimation</subject>
    <subject>causal inference</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-09-25</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4050561</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3558313</relatedIdentifier>
  </relatedIdentifiers>
  <version>v0.2.7-joss</version>
  <rightsList>
    <rights rightsURI="https://opensource.org/licenses/GPL-3.0">GNU General Public License v3.0 only</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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. This version of the R package corresponds to the software paper in the &lt;em&gt;Journal of Open Source Software&lt;/em&gt;.&lt;/p&gt;</description>
  </descriptions>
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