Software Open Access
hal9001: The Scalable Highly Adaptive Lasso
Coyle, Jeremy R;
Hejazi, Nima S;
van der Laan, Mark J
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.
Files
(446.6 kB)
| Name | Size | |
|---|---|---|
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hal9001_0.2.5.tar.gz
md5:d0d872e23fdffd5662909651e84caa7f |
446.6 kB | Download |
133
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downloads
| All versions | This version | |
|---|---|---|
| Views | 133 | 55 |
| Downloads | 12 | 7 |
| Data volume | 4.1 MB | 3.1 MB |
| Unique views | 100 | 43 |
| Unique downloads | 10 | 6 |
Indexed in
- Publication date:
- November 29, 2019
- DOI:
-
- Keyword(s):
- machine learning semiparametric theory nonparametric estimation
- License (for files):
- GNU General Public License v3.0 only