Package: conquer
Type: Package
Title: Convolution-Type Smoothed Quantile Regression
Version: 1.3.3
Date: 2023-03-05
Authors@R: c(person("Xuming", "He", email = "xmhe@umich.edu", role = "aut"),
             person("Xiaoou", "Pan", email = "xip024@ucsd.edu", role = c("aut", "cre")), 
             person("Kean Ming", "Tan", email = "keanming@umich.edu", role = "aut"),
             person("Wen-Xin", "Zhou", email = "wez243@ucsd.edu", role = "aut"))
Description: 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.
Depends: R (>= 3.5.0)
License: GPL-3
Encoding: UTF-8
URL: https://github.com/XiaoouPan/conquer
SystemRequirements: C++17
Imports: Rcpp (>= 1.0.3), Matrix, matrixStats, stats
LinkingTo: Rcpp, RcppArmadillo (>= 0.9.850.1.0)
RoxygenNote: 7.2.1
Repository: https://xiaooupan.r-universe.dev
Date/Publication: 2023-03-06 06:48:00 UTC
RemoteUrl: https://github.com/xiaooupan/conquer
RemoteRef: HEAD
RemoteSha: 3adb73cfa717001af1816726fb9f9978d483499c
NeedsCompilation: yes
Packaged: 2026-07-04 05:03:47 UTC; root
Author: Xuming He [aut],
  Xiaoou Pan [aut, cre],
  Kean Ming Tan [aut],
  Wen-Xin Zhou [aut]
Maintainer: Xiaoou Pan <xip024@ucsd.edu>