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