4037393
doi
10.5281/zenodo.4037393
oai:zenodo.org:4037393
Coyle, Jeremy R
University of California, Berkeley
van der Laan, Mark J
University of California, Berkeley
hal9001: Scalable highly adaptive lasso regression in R
Hejazi, Nima S
University of California, Berkeley
info:eu-repo/semantics/openAccess
GNU General Public License v3.0 only
https://www.gnu.org/licenses/gpl-3.0-standalone.html
machine learning
semiparametric theory
nonparametric estimation
causal inference
<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>
Zenodo
2020-09-18
info:eu-repo/semantics/other
3558313
v0.2.7
1601070681.891903
640292
md5:47123ce012bd34f1f52f8313f997a99b
https://zenodo.org/records/4037393/files/hal9001-0.2.7.zip
public
10.5281/zenodo.3558313
isVersionOf
doi