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DESCRIPTION
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Package: hal9001
Title: The Scalable Highly Adaptive Lasso
Version: 0.2.6
Authors@R: c(
person("Jeremy", "Coyle", email = "[email protected]",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-9874-6649")),
person("Nima", "Hejazi", email = "[email protected]",
role = "aut",
comment = c(ORCID = "0000-0002-7127-2789")),
person("David", "Benkeser", email = "[email protected]",
role = "ctb",
comment = c(ORCID = "0000-0002-1019-8343")),
person("Oleg", "Sofrygin", email = "[email protected]",
role = "ctb"),
person("Weixin", "Cai", email = "[email protected]",
role = "ctb",
comment = c(ORCID = "0000-0003-2680-3066")),
person("Mark", "van der Laan", email = "[email protected]",
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>.
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
Suggests:
testthat,
knitr,
rmarkdown,
microbenchmark,
future,
ggplot2,
dplyr,
tidyr,
stringr,
survival,
data.table,
SuperLearner
LinkingTo:
Rcpp,
RcppEigen
VignetteBuilder: knitr
RoxygenNote: 7.1.0