Package: grpnet 0.6

Nathaniel E. Helwig

grpnet: Group Elastic Net Regularized GLMs and GAMs

Efficient algorithms for fitting generalized linear and additive models with group elastic net penalties as described in Helwig (2024) <doi:10.1080/10618600.2024.2362232>. Implements group LASSO, group MCP, and group SCAD with an optional group ridge penalty. Computes the regularization path for linear regression (gaussian), logistic regression (binomial), multinomial logistic regression (multinomial), log-linear count regression (poisson and negative.binomial), and log-linear continuous regression (gamma and inverse gaussian). Supports default and formula methods for model specification, k-fold cross-validation for tuning the regularization parameters, and nonparametric regression via tensor product reproducing kernel (smoothing spline) basis function expansion.

Authors:Nathaniel E. Helwig [aut, cre]

grpnet_0.6.tar.gz
grpnet_0.6.tar.gz(r-4.5-noble)grpnet_0.6.tar.gz(r-4.4-noble)
grpnet_0.6.tgz(r-4.4-emscripten)grpnet_0.6.tgz(r-4.3-emscripten)
grpnet.pdf |grpnet.html
grpnet/json (API)

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

Peer review:

Uses libs:
  • fortran– Runtime library for GNU Fortran applications
Datasets:
  • auto - Auto MPG Data Set

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

fortran

1.60 score 339 downloads 30 exports 0 dependencies

Last updated 2 months agofrom:65dd82783f. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 11 2024
R-4.5-linux-x86_64OKDec 11 2024

Exports:coef.cv.grpnetcoef.grpnetcv.comparecv.grpnetcv.grpnet.defaultcv.grpnet.formulafamily.grpnetgrpnetgrpnet.defaultgrpnet.formulaplot.cv.grpnetplot.grpnetpredict.cv.grpnetpredict.grpnetprint.coef.grpnetprint.cv.grpnetprint.grpnetR_grpnet_binomialR_grpnet_gammaR_grpnet_gaussianR_grpnet_invgausR_grpnet_maxeigvalR_grpnet_multinomR_grpnet_negbinR_grpnet_penaltyrkrk.model.matrixrow.kroneckervisualize.penaltyvisualize.shrink

Dependencies: