Package: hal9001
Title: The Scalable Highly Adaptive Lasso
Version: 0.4.3
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: 7.1.2
NeedsCompilation: yes
Packaged: 2022-02-09 21:49:31 UTC; nsh
Author: Jeremy Coyle [aut, cre] (<https://orcid.org/0000-0002-9874-6649>),
  Nima Hejazi [aut] (<https://orcid.org/0000-0002-7127-2789>),
  Rachael Phillips [aut] (<https://orcid.org/0000-0002-8474-591X>),
  Lars van der Laan [aut],
  David Benkeser [ctb] (<https://orcid.org/0000-0002-1019-8343>),
  Oleg Sofrygin [ctb],
  Weixin Cai [ctb] (<https://orcid.org/0000-0003-2680-3066>),
  Mark van der Laan [aut, cph, ths]
    (<https://orcid.org/0000-0003-1432-5511>)
Maintainer: Jeremy Coyle <jeremyrcoyle@gmail.com>
Repository: CRAN
Date/Publication: 2022-02-09 22:50:02 UTC
Built: R 4.1.2; x86_64-apple-darwin17.0; 2022-02-10 12:36:21 UTC; unix
Archs: hal9001.so.dSYM
