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Boosting Regression Quantiles is a component-wise boosting algorithm, that embeds all boosting steps in the well-established framework of quantile regression. It is initialized with the corresponding quantile, uses a quantile-specific learning rate, and uses quantile regression as its base learner. The package implements this algorithm and allows cross-validation and stability selection.
Version: | 1.0.0 |
Depends: | mboost, stabs, stats, parallel |
Imports: | quantreg, checkmate |
Suggests: | testthat (≥ 3.0.0) |
Published: | 2024-03-05 |
DOI: | 10.32614/CRAN.package.boostrq |
Author: | Stefan Linner [aut, cre, cph] |
Maintainer: | Stefan Linner <stefan.linner97 at gmail.com> |
BugReports: | https://github.com/stefanlinner/boostrq/issues |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
URL: | https://github.com/stefanlinner/boostrq |
NeedsCompilation: | no |
Materials: | README |
CRAN checks: | boostrq results |
Reference manual: | boostrq.pdf |
Package source: | boostrq_1.0.0.tar.gz |
Windows binaries: | r-devel: boostrq_1.0.0.zip, r-release: boostrq_1.0.0.zip, r-oldrel: boostrq_1.0.0.zip |
macOS binaries: | r-release (arm64): boostrq_1.0.0.tgz, r-oldrel (arm64): boostrq_1.0.0.tgz, r-release (x86_64): boostrq_1.0.0.tgz, r-oldrel (x86_64): boostrq_1.0.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.