Package: hal9001
Title: The Scalable Highly Adaptive Lasso
Version: 0.4.6
Authors@R: c(
  person("Jeremy", "Coyle", email = "jeremyrcoyle@gmail.com",
         role = c("aut", "cre"),
         comment = c(ORCID = "0000-0002-9874-6649")),
  person("Nima", "Hejazi", email = "nh@nimahejazi.org",
         role = "aut",
         comment = c(ORCID = "0000-0002-7127-2789")),
  person("Rachael", "Phillips", email = "rachaelvphillips@berkeley.edu",
         role = "aut", comment = c(ORCID = "0000-0002-8474-591X")),
  person("Lars", "van der Laan", email = "vanderlaanlars@yahoo.com",
         role = "aut"),
  person("David", "Benkeser", email = "benkeser@emory.edu",
         role = "ctb",
         comment = c(ORCID = "0000-0002-1019-8343")),
  person("Oleg", "Sofrygin", email = "oleg.sofrygin@gmail.com",
         role = "ctb"),
  person("Weixin", "Cai", email = "wcai@berkeley.edu",
         role = "ctb",
         comment = c(ORCID = "0000-0003-2680-3066")),
  person("Mark", "van der Laan", email = "laan@berkeley.edu",
         role = c("aut", "cph", "ths"),
         comment = c(ORCID = "0000-0003-1432-5511"))
  )
Description: 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>.
Depends: R (>= 3.1.0), Rcpp
License: GPL-3
URL: https://github.com/tlverse/hal9001
BugReports: https://github.com/tlverse/hal9001/issues
Encoding: UTF-8
LazyData: true
Imports: Matrix, stats, utils, methods, assertthat, origami (>= 1.0.3),
        glmnet, data.table, stringr
Suggests: testthat, knitr, rmarkdown, microbenchmark, future, ggplot2,
        dplyr, tidyr, survival, SuperLearner
LinkingTo: Rcpp, RcppEigen
VignetteBuilder: knitr
RoxygenNote: 7.3.1
Roxygen: list(markdown = TRUE)
Config/pak/sysreqs: libicu-dev
Repository: https://tlverse.r-universe.dev
Date/Publication: 2024-11-19 20:00:18 UTC
RemoteUrl: https://github.com/tlverse/hal9001
RemoteRef: HEAD
RemoteSha: 00fe70f32bcf32e006ad415fe5b1bd8947be8b6f
NeedsCompilation: yes
Packaged: 2026-06-24 05:24:46 UTC; root
Author: Jeremy Coyle [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-9874-6649>),
  Nima Hejazi [aut] (ORCID: <https://orcid.org/0000-0002-7127-2789>),
  Rachael Phillips [aut] (ORCID: <https://orcid.org/0000-0002-8474-591X>),
  Lars van der Laan [aut],
  David Benkeser [ctb] (ORCID: <https://orcid.org/0000-0002-1019-8343>),
  Oleg Sofrygin [ctb],
  Weixin Cai [ctb] (ORCID: <https://orcid.org/0000-0003-2680-3066>),
  Mark van der Laan [aut, cph, ths] (ORCID:
    <https://orcid.org/0000-0003-1432-5511>)
Maintainer: Jeremy Coyle <jeremyrcoyle@gmail.com>