xrnet: Hierarchical Regularized Regression

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.

Version:	1.0.0
Depends:	R (≥ 4.0)
Imports:	Rcpp (≥ 0.12.19), foreach, bigmemory, methods
LinkingTo:	Rcpp, RcppEigen, BH, bigmemory
Suggests:	knitr, rmarkdown, testthat, Matrix, doParallel
Published:	2024-07-16
DOI:	10.32614/CRAN.package.xrnet
Author:	Garrett Weaver [aut, cre], Dixin Shen [aut], Juan Pablo Lewinger [ctb, ths]
Maintainer:	Garrett Weaver <gmweaver.usc at gmail.com>
BugReports:	https://github.com/USCbiostats/xrnet/issues
License:	GPL-2
URL:	https://github.com/USCbiostats/xrnet, https://uscbiostats.github.io/xrnet/
NeedsCompilation:	yes
Materials:	README NEWS
CRAN checks:	xrnet results

Documentation:

Reference manual:

xrnet.pdf

Downloads:

Package source:	xrnet_1.0.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available
Old sources:	xrnet archive

Reverse dependencies:

Reverse enhances:

transreg

Linking:

Please use the canonical form https://CRAN.R-project.org/package=xrnet to link to this page.