Package: QuantRegGLasso 1.0.0

Wen-Ting Wang

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>).

Authors:Wen-Ting Wang [aut, cre], Wei-Ying Wu [aut], Toshio Honda [aut], Ching-Kang Ing [aut]

QuantRegGLasso_1.0.0.tar.gz
QuantRegGLasso_1.0.0.tar.gz(r-4.5-noble)QuantRegGLasso_1.0.0.tar.gz(r-4.4-noble)
QuantRegGLasso_1.0.0.tgz(r-4.4-emscripten)QuantRegGLasso_1.0.0.tgz(r-4.3-emscripten)
QuantRegGLasso.pdf |QuantRegGLasso.html✨
QuantRegGLasso/json (API)
NEWS

# Install 'QuantRegGLasso' in R:
install.packages('QuantRegGLasso', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/egpivo/quantregglasso/issues

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

On CRAN:

Conda-Forge:

openblascpp

1.70 score 2 scripts 175 downloads 11 exports 30 dependencies

Last updated 1 years agofrom:09a3e439dd. Checks:2 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKFeb 10 2025
R-4.5-linux-x86_64OKFeb 10 2025

Exports:awglawgl_omegacheck_predict_parametersorthogonize_bsplineplot_bic_resultplot_coefficient_functionplot_sequentiallyplot.qrglassoplot.qrglasso.predictpredictqrglasso

Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangscalestibbleutf8vctrsviridisLitewithr

Citation

To cite package ‘QuantRegGLasso’ in publications use:

Wang W, Wu W, Honda T, Ing C (2024). QuantRegGLasso: Adaptively Weighted Group Lasso for Semiparametric Quantile Regression Models. R package version 1.0.0, https://CRAN.R-project.org/package=QuantRegGLasso.

Corresponding BibTeX entry: 

  @Manual{,
    title = {QuantRegGLasso: Adaptively Weighted Group Lasso for
      Semiparametric Quantile Regression Models},
    author = {Wen-Ting Wang and Wei-Ying Wu and Toshio Honda and
      Ching-Kang Ing},
    year = {2024},
    note = {R package version 1.0.0},
    url = {https://CRAN.R-project.org/package=QuantRegGLasso},
  }

Readme and manuals

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
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)