QuantRegGLasso: Adaptively Weighted Group Lasso for Semiparametric Quantile Regression Models

QuantRegGLasso is an R package designed for adaptively weighted group Lasso procedures in quantile regression. It excels in simultaneous variable selection and structure identification for varying coefficient quantile regression models and additive quantile regression models with ultra-high dimensional covariates.

Installation

• Install the current development version from GitHub:

  remotes::install_github("egpivo/QuantRegGLasso")

Please Note:

• Windows Users: Ensure that you have Rtools installed before proceeding with the installation.

• Mac Users: You need Xcode Command Line Tools and should install the library gfortran. Follow these steps in the terminal:

  brew update
  brew install gcc

  For a detailed solution, refer to this link, or download and install the library gfortran to resolve the "ld: library not found for -lgfortran" error.

Authors

• Wen-Ting Wang (GitHub)

• Wei-Ying Wu

• Toshio Honda

• Ching-Kang Ing

Maintainer

Wen-Ting Wang (GitHub)

Reference

Toshio Honda, Ching-Kang Ing, Wei-Ying Wu (2019). Adaptively weighted group Lasso for semiparametric quantile regression models.

This paper introduces the adaptively weighted group Lasso procedure and its application to semiparametric quantile regression models. The methodology is grounded in a strong sparsity condition, establishing selection consistency under certain weight conditions.

License

GPL (>= 2)