Adaptively Weighted Group Lasso for Semiparametric Quantile Regression Models R package QuantRegGLasso version 1.0.1 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>). Software Depends: R (>= 3.4.0) Imports: Rcpp (>= 1.0.12), ggplot2 LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat (>= 2.1.0) Wen-Ting Wang <egpivo@gmail.com> Comprehensive R Archive Network (CRAN) Wen-Ting Wang [aut, cre] (ORCID: <https://orcid.org/0000-0003-3051-7302>), Wei-Ying Wu [aut], Toshio Honda [aut], Ching-Kang Ing [aut] (ORCID: <https://orcid.org/0000-0003-1362-8246>) GPL (>= 2) 2025-10-06 application/tgz https://CRAN.R-project.org/package=QuantRegGLasso doi:10.32614/CRAN.package.QuantRegGLasso