Package: boostrq 1.0.0

boostrq: Boosting Regression Quantiles

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.

Authors:Stefan Linner [aut, cre, cph]

boostrq_1.0.0.tar.gz
boostrq_1.0.0.zip(r-4.5)boostrq_1.0.0.zip(r-4.4)boostrq_1.0.0.zip(r-4.3)
boostrq_1.0.0.tgz(r-4.5-any)boostrq_1.0.0.tgz(r-4.4-any)boostrq_1.0.0.tgz(r-4.3-any)
boostrq_1.0.0.tar.gz(r-4.5-noble)boostrq_1.0.0.tar.gz(r-4.4-noble)
boostrq_1.0.0.tgz(r-4.4-emscripten)boostrq_1.0.0.tgz(r-4.3-emscripten)
boostrq.pdf |boostrq.html
boostrq/json (API)

# Install 'boostrq' in R:
install.packages('boostrq', repos = c('https://stefanlinner.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/stefanlinner/boostrq/issues

On CRAN:

Conda:

3.00 score 2 stars 194 downloads 2 exports 19 dependencies

Last updated 1 years agofrom:f2d3480f8d. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 31 2025
R-4.5-winOKMar 31 2025
R-4.5-macOKMar 31 2025
R-4.5-linuxOKMar 31 2025
R-4.4-winOKMar 31 2025
R-4.4-macOKMar 31 2025
R-4.4-linuxOKMar 31 2025
R-4.3-winOKMar 31 2025
R-4.3-macOKMar 31 2025

Exports:boostrqbrq

Dependencies:backportscheckmateFormulainumlatticelibcoinMASSMatrixMatrixModelsmboostmvtnormnnlspartykitquadprogquantregrpartSparseMstabssurvival