QuantRegGLasso: Adaptively Weighted Group Lasso for Semiparametric Quantile Regression Models

Implements an adaptively weighted group Lasso procedure for simultaneous variable selection and structure identification in varying coefficient quantile regression models and additive quantile regression models with ultra-high dimensional covariates. The methodology, grounded in a strong sparsity condition, establishes selection consistency under certain weight conditions. To address the challenge of tuning parameter selection in practice, a BIC-type criterion named high-dimensional information criterion (HDIC) is proposed. The Lasso procedure, guided by HDIC-determined tuning parameters, maintains selection consistency. Theoretical findings are strongly supported by simulation studies. (Toshio Honda, Ching-Kang Ing, Wei-Ying Wu, 2019, <doi:10.3150/18-BEJ1091>).

Version:	1.0.1
Depends:	R (≥ 3.4.0)
Imports:	Rcpp (≥ 1.0.12), ggplot2
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	knitr, rmarkdown, testthat (≥ 2.1.0)
Published:	2025-10-06
DOI:	10.32614/CRAN.package.QuantRegGLasso
Author:	Wen-Ting Wang [aut, cre], Wei-Ying Wu [aut], Toshio Honda [aut], Ching-Kang Ing [aut]
Maintainer:	Wen-Ting Wang <egpivo at gmail.com>
BugReports:	https://github.com/egpivo/QuantRegGLasso/issues
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/egpivo/QuantRegGLasso
NeedsCompilation:	yes
SystemRequirements:	GNU make
Materials:	README, NEWS
CRAN checks:	QuantRegGLasso results

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