Package: rqPen 4.1.2

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], Adam Maidman [aut], Shaobo Li [aut]

rqPen_4.1.2.tar.gz
rqPen_4.1.2.zip(r-4.5)rqPen_4.1.2.zip(r-4.4)rqPen_4.1.2.zip(r-4.3)
rqPen_4.1.2.tgz(r-4.4-x86_64)rqPen_4.1.2.tgz(r-4.4-arm64)rqPen_4.1.2.tgz(r-4.3-x86_64)rqPen_4.1.2.tgz(r-4.3-arm64)
rqPen_4.1.2.tar.gz(r-4.5-noble)rqPen_4.1.2.tar.gz(r-4.4-noble)
rqPen_4.1.2.tgz(r-4.4-emscripten)rqPen_4.1.2.tgz(r-4.3-emscripten)
rqPen.pdf |rqPen.html✨
rqPen/json (API)
NEWS

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

Peer review:

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

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

On CRAN:

7.07 score 15 stars 3 packages 102 scripts 858 downloads 8 exports 19 dependencies

Last updated 24 days agofrom:c021a40c72. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 28 2024
R-4.5-win-x86_64OKOct 28 2024
R-4.5-linux-x86_64OKOct 28 2024
R-4.4-win-x86_64OKOct 28 2024
R-4.4-mac-x86_64OKOct 28 2024
R-4.4-mac-aarch64OKOct 28 2024
R-4.3-win-x86_64OKOct 28 2024
R-4.3-mac-x86_64OKOct 28 2024
R-4.3-mac-aarch64OKOct 28 2024

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

Dependencies:clidata.tablegluehqreghrqglaslatticelifecycleMASSMatrixMatrixModelsplyrquantregrbibutilsRcppRcppArmadilloRdpackrlangSparseMsurvival

Readme and manuals

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