conquer
1.3.3Convolution-Type Smoothed Quantile Regression
Overview
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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- OK2026-04-2512 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-04-2212 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1811 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 98%
- Return-value docs
- 100%
- References docs
- 100%
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Code & Tests
People & History
10 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 1.3.3Latest
- 1.3.22023-02-06 · diff ↗
- 1.3.12022-09-13 · diff ↗
- RR 4.2.0 released · 2022-04-22
- 1.3.02022-03-21 · diff ↗
- 1.2.22022-02-13 · diff ↗
- 1.2.12021-11-01 · diff ↗
- 1.2.02021-10-30 · diff ↗
- RR 4.1.0 released · 2021-05-18
- 1.0.22020-08-27 · diff ↗
- 1.0.12020-05-06 · diff ↗
- RR 4.0.0 released · 2020-04-24
Show 2 earlier events
- 1.0.02020-04-15
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2020-04-15
- Total releases
- 10 / 6 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.5.0
- Download size
- 55 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet