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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)

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Version

Install

install.packages('rqPen')

Monthly Downloads

751

Version

4.1.4

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/bssherwood/rqpen/blob/master/ignore/rqPenArticle.pdf

Maintainer

Ben Sherwood

Last Published

December 18th, 2025

Functions in rqPen (4.1.4)

Title Quantile regression estimation and consistent variable selection across multiple quantiles

qic.select.rq.pen.seq.cv

Select tuning parameters using IC

Calculate information criterion for penalized quantile regression models. Currently not exported.

Select tuning parameters using IC

Fits quantile regression models using a group penalized objective function.

rq.group.pen.cv

Performs cross validation for a group penalty.

qic.select.rq.pen.seq

Select tuning parameters using IC

Estimates a quantile regression model with a group penalized objective function.

Estimates a quantile regression model with a lasso penalized quanitle loss function.

rqPen: A package for estimating quantile regression models using penalized objective functions.

Non-convex penalized quantile regression

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.

Fit a quantile regression model using a penalized quantile loss function.

Plot of how coefficients change with tau

bytau.plot.rq.pen.seq

Plot of how coefficients change with tau.

coef.rq.pen.seq

Returns coefficients of a rq.pen.seq object

bytau.plot.rq.pen.seq.cv

Plot of coefficients varying by quantiles for rq.pen.seq.cv object

coef.rq.pen.seq.cv

Returns coefficients from a rq.pen.seq.cv object.

coef.cv.rq.group.pen

Coefficients from a cv.rq.group.pen object

Plots of coefficients by lambda for cv.rq.group.pen and cv.rq.pen

Plots of cross validation results as a function of lambda.

Returns Coefficients of a cv.rq.pen object

cv.rq.group.pen

Old cross validation function for group penalty

print.rq.pen.seq

Print a rq.pen.seq object

plot.rq.pen.seq

Plot of coefficients of rq.pen.seq object as a function of lambda

predict.rq.pen.seq

Predictions from rq.pen.seq object

plot.rq.pen.seq.cv

Plots cross validation results from a rq.pen.seq.cv object

print.qic.select

Print a qic.select object

predict.qic.select

Predictions from a qic.select object

predict.rq.pen.seq.cv

Predictions from rq.pen.seq.cv object

Title Cross validation for consistent variable selection across multiple quantiles.

print.rq.pen.seq.cv

Prints a rq.pen.seq.cv object

predict.cv.rq.pen

Prediction for a cv.rq.pen object

Prediction for a rq.pen object

plot.cv.rq.group.pen

Cross validation plot for cv.rq.group.pen object