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conquer  

Convolution-Type Smoothed Quantile Regression
View on CRAN: Click here


Download and install conquer package within the R console
Install from CRAN:
install.packages("conquer")

Install from Github:
library("remotes")
install_github("cran/conquer")

Install by package version:
library("remotes")
install_version("conquer", "1.3.3")



Attach the package and use:
library("conquer")
Maintained by
Xiaoou Pan
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-04-15
Latest Update: 2023-03-06
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.
How to cite:
Xiaoou Pan (2020). conquer: Convolution-Type Smoothed Quantile Regression. R package version 1.3.3, https://cran.r-project.org/web/packages/conquer. Accessed 13 Oct. 2026.
Previous versions and publish date:
(2026-07-09 07:28), 1.0.0 (2020-04-15 10:50), 1.0.1 (2020-05-06 14:40), 1.0.2 (2020-08-27 07:10), 1.2.0 (2021-10-30 00:00), 1.2.1 (2021-11-01 16:50), 1.2.2 (2022-02-13 00:10), 1.3.0 (2022-03-21 09:20), 1.3.1 (2022-09-13 09:30), 1.3.2 (2023-02-06 02:12)
Other packages that cited conquer R package
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Other R packages that conquer depends, imports, suggests or enhances
Complete documentation for conquer
Functions, R codes and Examples using the conquer R package
Some associated functions: conquer-package . conquer.cv.reg . conquer . conquer.process . conquer.reg . 
Some associated R codes: RcppExports.R . conquer-package.R . generate_ns.R . smqr.R .  Full conquer package functions and examples
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