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 Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=hal9001 to link to this page.
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