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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.

Versions across snapshots

VersionRepositoryFileSize
1.3.3 rolling linux/jammy R-4.5 conquer_1.3.3.tar.gz 637.7 KiB
1.3.3 rolling linux/noble R-4.5 conquer_1.3.3.tar.gz 665.0 KiB
1.3.3 rolling source/ R- conquer_1.3.3.tar.gz 55.0 KiB
1.3.3 latest linux/jammy R-4.5 conquer_1.3.3.tar.gz 637.7 KiB
1.3.3 latest linux/noble R-4.5 conquer_1.3.3.tar.gz 665.0 KiB
1.3.3 latest source/ R- conquer_1.3.3.tar.gz 55.0 KiB
1.3.3 2026-04-26 source/ R- conquer_1.3.3.tar.gz 55.0 KiB
1.3.3 2026-04-23 source/ R- conquer_1.3.3.tar.gz 55.0 KiB
1.3.3 2026-04-09 windows/windows R-4.5 conquer_1.3.3.zip 1.0 MiB
1.3.3 2025-04-20 source/ R- conquer_1.3.3.tar.gz 55.0 KiB

Dependencies (latest)

Imports

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