grpreg v3.3.0
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Regularization Paths for Regression Models with Grouped Covariates
Efficient algorithms for fitting the regularization path of linear
regression, GLM, and Cox regression models with grouped penalties. This
includes group selection methods such as group lasso, group MCP, and
group SCAD as well as bi-level selection methods such as the group
exponential lasso, the composite MCP, and the group bridge.
Readme
Regularization Paths for Regression Models with Grouped Covariates
grpreg is an R package for fitting the regularization path of linear regression, GLM, and Cox regression models with grouped penalties. This includes group selection methods such as group lasso, group MCP, and group SCAD as well as bi-level selection methods such as the group exponential lasso, the composite MCP, and the group bridge. Utilities for carrying out cross-validation as well as post-fitting visualization, summarization, and prediction are also provided.
Install
- To install the latest release version from CRAN:
install.packages("grpreg") - To install the latest development version from GitHub:
remotes::install_github("pbreheny/grpreg")
Get started
See the "getting started" vignette
Learn more
Follow the links under "Learn more" at the grpreg website
Details of the algorithms used
- Breheny, P. and Huang, J. (2009) Penalized methods for bi-level variable selection. Statistics and its interface, 2: 369-380.
- Breheny, P. and Huang, J. (2015) Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors. Statistics and Computing, 25: 173-187.
Functions in grpreg
| Name | Description | |
| gBridge | Fit a group bridge regression path | |
| Lung | VA lung cancer data set | |
| logLik.grpreg | logLik method for grpreg | |
| Birthwt | Risk Factors Associated with Low Infant Birth Weight | |
| birthwt.grpreg | Risk Factors Associated with Low Infant Birth Weight | |
| AUC.cv.grpsurv | Calculates AUC for cv.grpsurv objects | |
| grpsurv | Fit an group penalized survival model | |
| grpreg | Fit a group penalized regression path | |
| cv.grpreg | Cross-validation for grpreg/grpsurv | |
| grpreg-package | Regularization paths for regression models with grouped covariates | |
| plot.grpsurv.func | Plot survival curve for grpsurv model | |
| summary.cv.grpreg | Summarizing inferences based on cross-validation | |
| predict.grpreg | Model predictions based on a fitted grpreg object | |
| select.grpreg | Select an value of lambda along a grpreg path | |
| predict.grpsurv | Model predictions based on a fitted "grpsurv" object. | |
| plot.grpreg | Plot coefficients from a "grpreg" object | |
| plot.cv.grpreg | Plots the cross-validation curve from a cv.grpreg object | |
| No Results! | ||
Vignettes of grpreg
| Name | ||
| web/models.rmd | ||
| web/penalties.rmd | ||
| getting-started.rmd | ||
| vignette.css | ||
| No Results! | ||
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Details
| Date | 2020-06-10 |
| VignetteBuilder | knitr |
| BugReports | http://github.com/pbreheny/grpreg/issues |
| License | GPL-3 |
| URL | http://pbreheny.github.io/grpreg, https://github.com/pbreheny/grpreg |
| LazyData | TRUE |
| RoxygenNote | 7.0.2 |
| Encoding | UTF-8 |
| NeedsCompilation | yes |
| Packaged | 2020-06-10 17:45:26 UTC; pbreheny |
| Repository | CRAN |
| Date/Publication | 2020-06-10 18:20:02 UTC |
| suggests | knitr , survival , tinytest |
| imports | Matrix |
| depends | R (>= 3.1.0) |
| Contributors | Yaohui Zeng |
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