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hrqglas  

Group Variable Selection for Quantile and Robust Mean Regression
View on CRAN: Click here


Download and install hrqglas package within the R console
Install from CRAN:
install.packages("hrqglas")

Install from Github:
library("remotes")
install_github("cran/hrqglas")

Install by package version:
library("remotes")
install_version("hrqglas", "1.1.0")



Attach the package and use:
library("hrqglas")
Maintained by
Shaobo Li
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-06-23
Latest Update: 2023-01-30
Description:
A program that conducts group variable selection for quantile and robust mean regression (Sherwood and Li, 2022). The group lasso penalty (Yuan and Lin, 2006) is used for group-wise variable selection. Both of the quantile and mean regression models are based on the Huber loss. Specifically, with the tuning parameter in the Huber loss approaching to 0, the quantile check function can be approximated by the Huber loss for the median and the tilted version of Huber loss at other quantiles. Such approximation provides computational efficiency and stability, and has also been shown to be statistical consistent.
How to cite:
Shaobo Li (2021). hrqglas: Group Variable Selection for Quantile and Robust Mean Regression. R package version 1.1.0, https://cran.r-project.org/web/packages/hrqglas. Accessed 29 Jan. 2025.
Previous versions and publish date:
1.0.1 (2021-08-17 00:00), 1.0 (2021-06-23 08:20)
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Complete documentation for hrqglas
Functions, R codes and Examples using the hrqglas R package
Some associated functions: coef.cv.hrq_glasso . coef.hrq_glasso . cv.hrq_glasso . hrq_glasso . plot.cv.hrq_glasso . predict.cv.hrq_glasso . predict.hrq_glasso . 
Some associated R codes: cores.R . hrq_glasso.R . hrq_glasso_coef.R . hrq_glasso_cv.R . hrq_glasso_cv_plot.R . hrq_glasso_predict.R . utils.R .  Full hrqglas package functions and examples
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