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-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 281 downloads 7 exports 9 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-arm64OK167
linux-devel-x86_64OK178
source / vignettesOK203
linux-release-arm64OK166
linux-release-x86_64OK180
macos-release-arm64OK103
macos-release-x86_64OK459
macos-oldrel-arm64OK124
macos-oldrel-x86_64OK203
windows-develOK174
windows-releaseOK166
windows-oldrelOK185
wasm-releaseOK134

Exports:define_enetdefine_lassodefine_penaltydefine_ridgetune_xrnetxrnetxrnet_control

Dependencies:BHbigmemorybigmemory.sricodetoolsforeachiteratorsRcppRcppEigenuuid