Package: grpnet 0.7
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), multivariate regression (multigaussian), 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:
grpnet_0.7.tar.gz
grpnet_0.7.tar.gz(r-4.5-noble)grpnet_0.7.tar.gz(r-4.4-noble)
grpnet_0.7.tgz(r-4.4-emscripten)grpnet_0.7.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')) |
- 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.
Last updated 8 days agofrom:0b9f065584. Checks:2 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 24 2025 |
| R-4.5-linux-x86_64 | OK | Jan 24 2025 |
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_multigausR_grpnet_multinomR_grpnet_negbinR_grpnet_penaltyrkrk.model.matrixrow.kroneckervisualize.penaltyvisualize.shrink
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Auto MPG Data Set | auto |
| Extract Coefficients for cv.grpnet and grpnet Fits | coef.cv.grpnet coef.grpnet |
| Compare Multiple cv.grpnet Solutions | cv.compare |
| Cross-Validation for grpnet | cv.grpnet cv.grpnet.default cv.grpnet.formula |
| Prepare 'family' Argument for grpnet | family.grpnet |
| Fit a Group Elastic Net Regularized GLM/GAM | grpnet grpnet.default grpnet.formula |
| Plot Cross-Validation Curve for cv.grpnet Fits | plot.cv.grpnet |
| Plot Regularization Path for grpnet Fits | plot.grpnet |
| Predict Method for cv.grpnet Fits | predict.cv.grpnet |
| Predict Method for grpnet Fits | predict.grpnet |
| S3 'print' Methods for grpnet | print.coef.grpnet print.cv.grpnet print.grpnet |
| Reproducing Kernel Basis | rk |
| Construct Design Matrices via Reproducing Kernels | rk.model.matrix |
| Row-Wise Kronecker Product | row.kronecker |
| Startup Message for grpnet | grpnetStartupMessage StartupMessage |
| Plots grpnet Penalty Function or its Derivative | visualize.penalty |
| Plots grpnet Shrinkage Operator or its Estimator | visualize.shrink |