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
Author:	Xuming He [aut], Xiaoou Pan [aut, cre], Kean Ming Tan [aut], Wen-Xin Zhou [aut]
Maintainer:	Xiaoou Pan <xip024 at ucsd.edu>
License:	GPL-3
URL:	https://github.com/XiaoouPan/conquer
NeedsCompilation:	yes
SystemRequirements:	C++17
Materials:	README
CRAN checks:	conquer results

Documentation:

Reference manual:

conquer.pdf

Downloads:

Package source:	conquer_1.3.3.tar.gz
Windows binaries:	r-devel: conquer_1.3.3.zip, r-release: conquer_1.3.3.zip, r-oldrel: conquer_1.3.3.zip
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

Linking:

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