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:	0.1.7
Depends:	R (≥ 3.5)
Imports:	Rcpp (≥ 0.12.19), foreach, bigmemory, methods
LinkingTo:	Rcpp, RcppEigen, BH, bigmemory
Suggests:	knitr, rmarkdown, testthat, Matrix, doParallel
Published:	2020-03-01
Author:	Garrett Weaver [aut, cre], Juan Pablo Lewinger [ctb, ths]
Maintainer:	Garrett Weaver <gmweaver.usc at gmail.com>
License:	GPL-2
URL:	https://github.com/USCbiostats/xrnet
NeedsCompilation:	yes
SystemRequirements:	C++11
Materials:	README NEWS
CRAN checks:	xrnet results

Documentation:

Reference manual:

xrnet.pdf

Downloads:

Package source:	xrnet_0.1.7.tar.gz
Windows binaries:	r-devel: xrnet_0.1.7.zip, r-release: xrnet_0.1.7.zip, r-oldrel: xrnet_0.1.7.zip
macOS binaries:	r-release (arm64): xrnet_0.1.7.tgz, r-oldrel (arm64): xrnet_0.1.7.tgz, r-release (x86_64): xrnet_0.1.7.tgz, r-oldrel (x86_64): xrnet_0.1.7.tgz
Old sources:	xrnet archive

Linking:

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