Package: xrnet
Type: Package
Title: Hierarchical Regularized Regression
Version: 0.1.7
Authors@R: c(
  person("Garrett", "Weaver", email = "gmweaver.usc@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9918-8386")),
  person("Juan Pablo", "Lewinger", email = "lewinger@usc.edu", role = c("ctb", "ths"))
  )
URL: https://github.com/USCbiostats/xrnet
Description: Fits hierarchical regularized regression models
    to incorporate potentially informative external data, Weaver and Lewinger (2019) <doi:10.21105/joss.01761>. 
    Utilizes coordinate descent to efficiently fit regularized regression models both with and without
    external information with the most common penalties used in practice (i.e. ridge, lasso, elastic net). 
    Support for standard R matrices, sparse matrices and big.matrix objects.
License: GPL-2
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Suggests: knitr, rmarkdown, testthat, Matrix, doParallel
LinkingTo: Rcpp, RcppEigen, BH, bigmemory
Imports: Rcpp (>= 0.12.19), foreach, bigmemory, methods
Depends: R (>= 3.5)
SystemRequirements: C++11
NeedsCompilation: yes
Packaged: 2020-02-29 23:54:54 UTC; gmweaver
Author: Garrett Weaver [aut, cre] (<https://orcid.org/0000-0002-9918-8386>),
  Juan Pablo Lewinger [ctb, ths]
Maintainer: Garrett Weaver <gmweaver.usc@gmail.com>
Repository: CRAN
Date/Publication: 2020-03-01 06:50:02 UTC
Built: R 3.5.2; x86_64-apple-darwin15.6.0; 2020-03-02 14:43:08 UTC; unix
Archs: xrnet.so.dSYM
