Package: xrnet 1.0.0
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:
xrnet_1.0.0.tar.gz
xrnet_1.0.0.zip(r-4.5)xrnet_1.0.0.zip(r-4.4)xrnet_1.0.0.zip(r-4.3)
xrnet_1.0.0.tgz(r-4.4-x86_64)xrnet_1.0.0.tgz(r-4.4-arm64)xrnet_1.0.0.tgz(r-4.3-x86_64)xrnet_1.0.0.tgz(r-4.3-arm64)
xrnet_1.0.0.tar.gz(r-4.5-noble)xrnet_1.0.0.tar.gz(r-4.4-noble)
xrnet_1.0.0.tgz(r-4.4-emscripten)xrnet_1.0.0.tgz(r-4.3-emscripten)
xrnet.pdf |xrnet.html✨
xrnet/json (API)
NEWS
| # 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
- ext_linear - Simulated external data
- x_linear - Simulated example data for hierarchical regularized linear regression
- y_linear - Simulated outcome data
Last updated 4 months agofrom:682eba69d8. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 13 2024 |
| R-4.5-win-x86_64 | OK | Nov 13 2024 |
| R-4.5-linux-x86_64 | OK | Nov 13 2024 |
| R-4.4-win-x86_64 | OK | Nov 13 2024 |
| R-4.4-mac-x86_64 | OK | Nov 13 2024 |
| R-4.4-mac-aarch64 | OK | Nov 13 2024 |
| R-4.3-win-x86_64 | OK | Nov 13 2024 |
| R-4.3-mac-x86_64 | OK | Nov 13 2024 |
| R-4.3-mac-aarch64 | OK | Nov 13 2024 |
Exports:define_enetdefine_lassodefine_penaltydefine_ridgetune_xrnetxrnetxrnet_control
Dependencies:BHbigmemorybigmemory.sricodetoolsforeachiteratorsRcppRcppEigenuuid
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Get coefficient estimates from "tune_xrnet" model object. | coef.tune_xrnet |
| Get coefficient estimates from "xrnet" model object. | coef.xrnet |
| Define elastic net regularization object for predictor and external data | define_enet |
| Define lasso regularization object for predictor and external data | define_lasso |
| Define regularization object for predictor and external data. | define_penalty |
| Define ridge regularization object for predictor and external data | define_ridge |
| Simulated external data | ext_linear |
| Plot k-fold cross-validation error grid | plot.tune_xrnet |
| Predict function for "tune_xrnet" object | predict.tune_xrnet |
| Predict function for "xrnet" object | predict.xrnet |
| k-fold cross-validation for hierarchical regularized regression | tune_xrnet |
| Simulated example data for hierarchical regularized linear regression | x_linear |
| Fit hierarchical regularized regression model | xrnet |
| Control function for xrnet fitting | xrnet_control |
| Simulated outcome data | y_linear |
