Package: rqPen 4.1.3

Ben Sherwood

rqPen: Penalized Quantile Regression

Performs penalized quantile regression with LASSO, elastic net, SCAD and MCP penalty functions including group penalties. In addition, offers a group penalty that provides consistent variable selection across quantiles. Provides a function that automatically generates lambdas and evaluates different models with cross validation or BIC, including a large p version of BIC. Below URL provides a link to a work in progress vignette.

Authors:Ben Sherwood [aut, cre], Shaobo Li [aut], Adam Maidman [aut]

rqPen_4.1.3.tar.gz
rqPen_4.1.3.tar.gz(r-4.5-noble)rqPen_4.1.3.tar.gz(r-4.4-noble)
rqPen_4.1.3.tgz(r-4.4-emscripten)rqPen_4.1.3.tgz(r-4.3-emscripten)
rqPen.pdf |rqPen.html✨
rqPen/json (API)
NEWS

# Install 'rqPen' in R:
install.packages('rqPen', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/bssherwood/rqpen/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

3.53 score 3 packages 1.3k downloads 1 mentions 8 exports 19 dependencies

Last updated 2 months agofrom:1e809b9e07. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 15 2025
R-4.5-linux-x86_64OKMar 15 2025
R-4.4-linux-x86_64OKMar 15 2025

Exports:bytau.plotqic.selectrq.gq.penrq.gq.pen.cvrq.group.penrq.group.pen.cvrq.penrq.pen.cv

Dependencies:clidata.tablegluehqreghrqglaslatticelifecycleMASSMatrixMatrixModelsplyrquantregrbibutilsRcppRcppArmadilloRdpackrlangSparseMsurvival

Citation

To cite package ‘rqPen’ in publications use:

Sherwood B, Li S, Maidman A (2025). rqPen: Penalized Quantile Regression. R package version 4.1.3, https://CRAN.R-project.org/package=rqPen.

Corresponding BibTeX entry: 

  @Manual{,
    title = {rqPen: Penalized Quantile Regression},
    author = {Ben Sherwood and Shaobo Li and Adam Maidman},
    year = {2025},
    note = {R package version 4.1.3},
    url = {https://CRAN.R-project.org/package=rqPen},
  }

Readme and manuals

rqPen: Penalized quantile regression

Overview

This R package provides tools for estimating a quantile regression model with a penalized objective function. Implements a variety of penalties, including group penalties.

Installation

For most up to date versions use the following code. However, be warned the github package is often in a state of testing and debugging.

devtools::install_github("bssherwood/rqpen")

The following code will install the more stable CRAN version.

install.packages("rqPen")

Example

library(rqPen)
n<- 200
p<- 30
x0<- matrix(rnorm(n*p),n,p)
x<- cbind(x0, x0^2, x0^3)[,order(rep(1:p,3))]
y<- -2+x[,1]+0.5*x[,2]-x[,3]-0.5*x[,7]+x[,8]-0.2*x[,9]+rt(n,2)
group<- rep(1:p, each=3)

# lasso estimation
# one tau
fit1 <- rq.pen(x,y)
# several values of tau
fit2 <- rq.pen(x,y,tau=c(.2,.5,.8))

# Group SCAD estimation
fit3 <- rq.group.pen(x,y,groups=group,penalty="gSCAD")

# cross validation
cv1 <- rq.pen.cv(x,y)
plot(cv1)

cv2 <- rq.pen.cv(x,y,tau=c(.2,.5,.8))
plot(cv2)

cv3 <- rq.group.pen(x,y,groups=group,penalty="gSCAD")
plot(cv3)

# BIC selection of tuning parameters
qs1 <- qic.select(fit1)
qs2 <- qic.select(fit2)
qs3 <- qic.select(fit3)

See, https://github.com/bssherwood/rqpen/blob/master/ignore/rqPenArticle.pdf, for a vignette. The Huber approach for rq.pen relies on the R package hqreg and work presented in "Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression". The Huber approach in rq.group.pen relies on R package hrqglas and work presented in An Efficient Approach to Feature Selection and Estimation for Quantile Regression with Grouped Variables

References

Sherwood, B. and Li, S. (2022) An Efficient Approach to Feature Selection and Estimation for Quantile Regression with Grouped Variables, Statistics and computing, 75.

Yi, C. and Huang, J. (2015) Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression, Journal of Computational and Graphical Statistics, 26:3, 547-557.

Help Manual

Help pageTopics
Plot of how coefficients change with taubytau.plot
Plot of how coefficients change with tau.bytau.plot.rq.pen.seq
Plot of coefficients varying by quantiles for rq.pen.seq.cv objectbytau.plot.rq.pen.seq.cv
Returns coefficients of a rq.pen.seq objectcoef.rq.pen.seq
Returns coefficients from a rq.pen.seq.cv object.coef.rq.pen.seq.cv
Plot of coefficients of rq.pen.seq object as a function of lambdaplot.rq.pen.seq
Plots cross validation results from a rq.pen.seq.cv objectplot.rq.pen.seq.cv
Predictions from a qic.select objectpredict.qic.select
Predictions from rq.pen.seq objectpredict.rq.pen.seq
Predictions from rq.pen.seq.cv objectpredict.rq.pen.seq.cv
Print a qic.select objectprint.qic.select
Print a rq.pen.seq objectprint.rq.pen.seq
Prints a rq.pen.seq.cv objectprint.rq.pen.seq.cv
Select tuning parameters using ICqic.select
Select tuning parameters using ICqic.select.rq.pen.seq
Select tuning parameters using ICqic.select.rq.pen.seq.cv
Title Quantile regression estimation and consistent variable selection across multiple quantilesrq.gq.pen
Title Cross validation for consistent variable selection across multiple quantiles.rq.gq.pen.cv
Fits quantile regression models using a group penalized objective function.rq.group.pen
Performs cross validation for a group penalty.rq.group.pen.cv
Fit a quantile regression model using a penalized quantile loss function.rq.pen
Does k-folds cross validation for rq.pen. If multiple values of a are specified then does a grid based search for best value of lambda and a.rq.pen.cv
rqPen: A package for estimating quantile regression models using penalized objective functions.rqPen-package rqPen