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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.
| Version: | 1.3.3 |
| Depends: | R (≥ 3.5.0) |
| Imports: | Rcpp (≥ 1.0.3), Matrix, matrixStats, stats |
| LinkingTo: | Rcpp, RcppArmadillo (≥ 0.9.850.1.0) |
| Published: | 2023-03-06 |
| DOI: | 10.32614/CRAN.package.conquer |
| Author: | Xuming He [aut], Xiaoou Pan [aut, cre], Kean Ming Tan [aut], Wen-Xin Zhou [aut] |
| Maintainer: | Xiaoou Pan <xip024 at ucsd.edu> |
| License: | GPL-3 |
| URL: | https://github.com/XiaoouPan/conquer |
| NeedsCompilation: | yes |
| SystemRequirements: | C++17 |
| Materials: | README |
| CRAN checks: | conquer results |
| Reference manual: | conquer.pdf |
| Package source: | conquer_1.3.3.tar.gz |
| Windows binaries: | r-devel: conquer_1.3.3.zip, r-release: conquer_1.3.3.zip, r-oldrel: conquer_1.3.3.zip |
| macOS binaries: | r-release (arm64): conquer_1.3.3.tgz, r-oldrel (arm64): conquer_1.3.3.tgz, r-release (x86_64): conquer_1.3.3.tgz, r-oldrel (x86_64): conquer_1.3.3.tgz |
| Old sources: | conquer archive |
| Reverse imports: | diagL1, HIMA, Qtools |
| Reverse suggests: | quantreg, SGDinference |
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