Package: regnet 1.0.1
regnet: Network-Based Regularization for Generalized Linear Models
Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features. This package provides procedures of network-based variable selection for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and Ren et al.(2019) <doi:10.1002/gepi.22194>). Continuous, binary, and survival response are supported. Robust network-based methods are available for continuous and survival responses.
Authors:
regnet_1.0.1.tar.gz
regnet_1.0.1.zip(r-4.5)regnet_1.0.1.zip(r-4.4)regnet_1.0.1.zip(r-4.3)
regnet_1.0.1.tgz(r-4.4-x86_64)regnet_1.0.1.tgz(r-4.4-arm64)regnet_1.0.1.tgz(r-4.3-x86_64)regnet_1.0.1.tgz(r-4.3-arm64)
regnet_1.0.1.tar.gz(r-4.5-noble)regnet_1.0.1.tar.gz(r-4.4-noble)
regnet_1.0.1.tgz(r-4.4-emscripten)regnet_1.0.1.tgz(r-4.3-emscripten)
regnet.pdf |regnet.html✨
regnet/json (API)
NEWS
| # Install 'regnet' in R: |
| install.packages('regnet', repos = c('https://jrhub.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jrhub/regnet/issues
cancer-prognosislogistic-regressionnetworkpenalized-regressionregularizationsurvival
Last updated 9 months agofrom:82b6d6aece. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 18 2024 |
| R-4.5-win-x86_64 | OK | Nov 18 2024 |
| R-4.5-linux-x86_64 | OK | Nov 18 2024 |
| R-4.4-win-x86_64 | OK | Nov 18 2024 |
| R-4.4-mac-x86_64 | OK | Nov 18 2024 |
| R-4.4-mac-aarch64 | OK | Nov 18 2024 |
| R-4.3-win-x86_64 | OK | Nov 18 2024 |
| R-4.3-mac-x86_64 | OK | Nov 18 2024 |
| R-4.3-mac-aarch64 | OK | Nov 18 2024 |
Dependencies:clicodetoolscpp11foreachglmnetglueigraphiteratorslatticelifecyclemagrittrMatrixpkgconfigRcppRcppArmadilloRcppEigenrlangshapesurvivalvctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| regnet: Network-Based Regularization for Generalized Linear Models | regnet-package |
| k-folds cross-validation for regnet | cv.regnet |
| plot a regnet object | plot.regnet |
| print a cv.regnet object | print.cv.regnet |
| print a regnet object | print.regnet |
| fit a regression for given lambda with network-based regularization | regnet |
| Example datasets for demonstrating the features of regnet | rgn rgn.htr rgn.logi rgn.surv rgn.tcga |
