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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:
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=xrnet
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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