hdqr
Fast Algorithm for Penalized Quantile Regression
Implements an efficient algorithm for fitting the entire regularization path of quantile regression models with elastic-net penalties using a generalized coordinate descent scheme. The framework also supports SCAD and MCP penalties. It is designed for high-dimensional datasets and emphasizes numerical accuracy and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) <https://openreview.net/pdf?id=RvwMTDYTOb>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.2 |
rolling source/ R- | hdqr_1.0.2.tar.gz |
38.6 KiB |
1.0.2 |
rolling linux/jammy R-4.5 | hdqr_1.0.2.tar.gz |
111.6 KiB |
1.0.2 |
rolling linux/noble R-4.5 | hdqr_1.0.2.tar.gz |
112.1 KiB |
1.0.2 |
latest source/ R- | hdqr_1.0.2.tar.gz |
38.6 KiB |
1.0.2 |
latest linux/jammy R-4.5 | hdqr_1.0.2.tar.gz |
111.6 KiB |
1.0.2 |
latest linux/noble R-4.5 | hdqr_1.0.2.tar.gz |
112.1 KiB |
1.0.2 |
2026-04-23 source/ R- | hdqr_1.0.2.tar.gz |
38.6 KiB |
1.0.2 |
2026-04-09 windows/windows R-4.5 | hdqr_1.0.2.zip |
121.2 KiB |
1.0.1 |
2025-04-20 source/ R- | hdqr_1.0.1.tar.gz |
37.9 KiB |