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 methods complemented with l_1-penalization and iteratively reweighted l_1-penalization are used to fit sparse models.

Version: 1.2.1
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 1.0.3), Matrix, matrixStats, stats, caret
LinkingTo: Rcpp, RcppArmadillo (≥ 0.9.850.1.0)
Published: 2021-11-01
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++11
CRAN checks: conquer results

Documentation:

Reference manual: conquer.pdf

Downloads:

Package source: conquer_1.2.1.tar.gz
Windows binaries: r-devel: conquer_1.2.1.zip, r-devel-UCRT: conquer_1.2.1.zip, r-release: conquer_1.2.1.zip, r-oldrel: conquer_1.2.1.zip
macOS binaries: r-release (arm64): conquer_1.2.1.tgz, r-release (x86_64): conquer_1.2.1.tgz, r-oldrel: conquer_1.2.1.tgz
Old sources: conquer archive

Reverse dependencies:

Reverse imports: quantreg

Linking:

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