conquer 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 Cited by 4 Authors 2 He, Xuming 2 Pan, Xiaoou 2 Tan, Kean Ming 2 Zhou, Wen-Xin Cited in 2 Serials 1 The Annals of Statistics 1 Journal of Econometrics Cited in 2 Fields 2 Statistics (62-XX) 1 Game theory, economics, finance, and other social and behavioral sciences (91-XX) Citations by Year