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conquer

Convolution-Type Smoothed Quantile Regression

v1.3.3 · Mar 6, 2023 · GPL-3

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.

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r-oldrel-macos-arm64 NOTE
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NOTE r-oldrel-macos-arm64

installed package size

installed size is 19.7Mb
  sub-directories of 1Mb or more:
    libs  19.4Mb
NOTE r-oldrel-macos-x86_64

installed package size

installed size is 20.6Mb
  sub-directories of 1Mb or more:
    libs  20.4Mb

Check History

NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
NOTE r-oldrel-macos-arm64

installed package size

installed size is 19.7Mb
  sub-directories of 1Mb or more:
    libs  19.4Mb
NOTE r-oldrel-macos-x86_64

installed package size

installed size is 20.6Mb
  sub-directories of 1Mb or more:
    libs  20.4Mb

Reverse Dependencies (5)

imports

Dependency Network

Dependencies Reverse dependencies Rcpp Matrix matrixStats HIMA Qtools diagL1 SGDinference quantreg conquer

Version History

new 1.3.3 Mar 10, 2026
updated 1.3.3 ← 1.3.2 diff Mar 5, 2023
updated 1.3.2 ← 1.3.1 diff Feb 5, 2023
updated 1.3.1 ← 1.3.0 diff Sep 12, 2022
updated 1.3.0 ← 1.2.2 diff Mar 20, 2022
updated 1.2.2 ← 1.2.1 diff Feb 12, 2022
updated 1.2.1 ← 1.2.0 diff Oct 31, 2021
updated 1.2.0 ← 1.0.2 diff Oct 29, 2021
updated 1.0.2 ← 1.0.1 diff Aug 26, 2020
updated 1.0.1 ← 1.0.0 diff May 5, 2020
new 1.0.0 Apr 14, 2020