QuantRegGLasso: Adaptively Weighted Group Lasso for Semiparametric Quantile Regression Models

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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.


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Wen-Ting Wang (GitHub)


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.


GPL (>= 2)