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 functions. For ease of use and increased flexibility, the Lasso
fitting routines invoke code from the 'glmnet' package by default. The highly
adaptive lasso was first formulated and described by MJ van der Laan (2017)
<doi:10.1515/ijb-2015-0097>, with practical demonstrations of its performance
given by Benkeser and van der Laan (2016) <doi:10.1109/DSAA.2016.93>. This
implementation of the highly adaptive lasso algorithm was described by Hejazi,
Coyle, and van der Laan (2020) <doi:10.21105/joss.02526>.
| Version: |
0.4.6 |
| Depends: |
R (≥ 3.1.0), Rcpp |
| Imports: |
Matrix, stats, utils, methods, assertthat, origami (≥ 1.0.3), glmnet, data.table, stringr |
| LinkingTo: |
Rcpp, RcppEigen |
| Suggests: |
testthat, knitr, rmarkdown, microbenchmark, future, ggplot2, dplyr, tidyr, survival, SuperLearner |
| Published: |
2023-11-14 |
| DOI: |
10.32614/CRAN.package.hal9001 |
| Author: |
Jeremy Coyle 
[aut, cre],
Nima Hejazi [aut],
Rachael Phillips
[aut],
Lars van der Laan [aut],
David Benkeser
[ctb],
Oleg Sofrygin [ctb],
Weixin Cai [ctb],
Mark van der Laan
[aut, cph, ths] |
| Maintainer: |
Jeremy Coyle <jeremyrcoyle at gmail.com> |
| BugReports: |
https://github.com/tlverse/hal9001/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/tlverse/hal9001 |
| NeedsCompilation: |
yes |
| Citation: |
hal9001 citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
hal9001 results |