swMATH ID: 44298
Software Authors: Xuming He, Xiaoou Pan, Kean Ming Tan, Wen-Xin Zhou
Description: R package conquer: Convolution-Type Smoothed Quantile Regression. Estimation and inference for conditional linear quantile regression models using a convolution smoothed approach. In the low-dimensional setting, efficient gradient-based methods are employed for fitting both a single model and a regression process over a quantile range. Normal-based and (multiplier) bootstrap confidence intervals for all slope coefficients are constructed. In high dimensions, the conquer method is complemented with flexible types of penalties (Lasso, elastic-net, group lasso, sparse group lasso, scad and mcp) to deal with complex low-dimensional structures.
Homepage: https://cran.r-project.org/web/packages/conquer/index.html
Source Code:  https://github.com/cran/conquer
Dependencies: R
Related Software: rqPen; R; AS 229; SparseM; RcppArmadillo
Cited in: 2 Documents

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