Convolution-Type Smoothed Quantile Regression R package conquer version 1.3.3 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. Software Depends: R (>= 3.5.0) Imports: Rcpp (>= 1.0.3), Matrix, matrixStats, stats LinkingTo: Rcpp, RcppArmadillo (>= 0.9.850.1.0) Xiaoou Pan <xip024@ucsd.edu> Comprehensive R Archive Network (CRAN) Xuming He [aut], Xiaoou Pan [aut, cre], Kean Ming Tan [aut], Wen-Xin Zhou [aut] GPL-3 2023-03-06 application/tgz https://CRAN.R-project.org/package=conquer doi:10.32614/CRAN.package.conquer