Package: xrnet 1.0.1

Garrett Weaver

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.

Authors:Garrett Weaver [aut, cre], Dixin Shen [aut], Juan Pablo Lewinger [ctb, ths]

xrnet_1.0.1.tar.gz
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xrnet_1.0.1.tgz(r-4.6-x86_64)xrnet_1.0.1.tgz(r-4.6-arm64)xrnet_1.0.1.tgz(r-4.5-x86_64)xrnet_1.0.1.tgz(r-4.5-arm64)
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xrnet_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
xrnet/json (API)

# Install 'xrnet' in R:
install.packages('xrnet', repos = c('https://uscbiostats.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/uscbiostats/xrnet/issues

Pkgdown/docs site:https://uscbiostats.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • ext_linear - Simulated external data
  • x_linear - Simulated example data for hierarchical regularized linear regression
  • y_linear - Simulated outcome data

On CRAN:

Conda:

cpp

4.65 score 10 stars 1 packages 10 scripts 285 downloads 7 exports 9 dependencies

Last updated from:e171fe7424. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK213
linux-devel-x86_64OK194
source / vignettesOK226
linux-release-arm64OK201
linux-release-x86_64OK182
macos-release-arm64OK145
macos-release-x86_64OK255
macos-oldrel-arm64OK106
macos-oldrel-x86_64OK195
windows-develOK171
windows-releaseOK170
windows-oldrelOK165
wasm-releaseOK138

Exports:define_enetdefine_lassodefine_penaltydefine_ridgetune_xrnetxrnetxrnet_control

Dependencies:BHbigmemorybigmemory.sricodetoolsforeachiteratorsRcppRcppEigenuuid