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hqreg  

Regularization Paths for Lasso or Elastic-Net Penalized Huber Loss Regression and Quantile Regression
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("hqreg", "1.4-1")



Attach the package and use:
library("hqreg")
Maintained by
Congrui Yi
[Scholar Profile | Author Map]
First Published: 2015-06-20
Latest Update: 2017-02-16
Description:
Efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss.
How to cite:
Congrui Yi (2015). hqreg: Regularization Paths for Lasso or Elastic-Net Penalized Huber Loss Regression and Quantile Regression. R package version 1.4-1, https://cran.r-project.org/web/packages/hqreg. Accessed 29 Mar. 2025.
Previous versions and publish date:
0.9 (2015-06-20 23:56), 1.0 (2015-09-11 17:10), 1.1 (2016-02-02 11:53), 1.2 (2016-02-17 00:18), 1.3 (2016-05-23 06:01), 1.4 (2017-02-16 07:13)
Other packages that cited hqreg R package
View hqreg citation profile
Other R packages that hqreg depends, imports, suggests or enhances
Complete documentation for hqreg
Functions, R codes and Examples using the hqreg R package
Some associated functions: cv.hqreg . hqreg-package . hqreg . hqreg_raw . plot.cv.hqreg . plot.hqreg . predict.cv.hqreg . predict.hqreg . 
Some associated R codes: cv.hqreg.R . hqreg.R . hqreg_raw.R . measure.hqreg.R . plot.cv.hqreg.R . plot.hqreg.R . predict.cv.hqreg.R . predict.hqreg.R .  Full hqreg package functions and examples
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