Package: regnet 1.0.1

Jie Ren

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:Jie Ren, Luann C. Jung, Yinhao Du, Cen Wu, Yu Jiang, Junhao Liu

regnet_1.0.1.tar.gz
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://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jrhub/regnet/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • rgn.htr - Example datasets for demonstrating the features of regnet
  • rgn.logi - Example datasets for demonstrating the features of regnet
  • rgn.surv - Example datasets for demonstrating the features of regnet
  • rgn.tcga - Example datasets for demonstrating the features of regnet

openblascppopenmp

1.70 score 6 scripts 186 downloads 1 mentions 2 exports 20 dependencies

Last updated 10 months agofrom:1d38ca9863. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 03 2024
R-4.5-linux-x86_64OKDec 03 2024

Exports:cv.regnetregnet

Dependencies:clicodetoolscpp11foreachglmnetglueigraphiteratorslatticelifecyclemagrittrMatrixpkgconfigRcppRcppArmadilloRcppEigenrlangshapesurvivalvctrs