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.

Version: 1.3.3
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 1.0.3), Matrix, matrixStats, stats
LinkingTo: Rcpp, RcppArmadillo (≥ 0.9.850.1.0)
Published: 2023-03-06
DOI: 10.32614/CRAN.package.conquer
Author: Xuming He [aut], Xiaoou Pan [aut, cre], Kean Ming Tan [aut], Wen-Xin Zhou [aut]
Maintainer: Xiaoou Pan <xip024 at>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: C++17
Materials: README
CRAN checks: conquer results


Reference manual: conquer.pdf


Package source: conquer_1.3.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): conquer_1.3.3.tgz, r-oldrel (arm64): conquer_1.3.3.tgz, r-release (x86_64): conquer_1.3.3.tgz, r-oldrel (x86_64): conquer_1.3.3.tgz
Old sources: conquer archive

Reverse dependencies:

Reverse imports: diagL1, HIMA, Qtools
Reverse suggests: quantreg, SGDinference


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