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qrcmNP: Nonlinear and Penalized Quantile Regression Coefficients Modeling

Penalized and nonlinear parametric modeling of quantile regression coefficient functions. The penalized integrated loss minimization estimator (lasso and group lasso penalties) of Sottile, Frumento, Chiodi and Bottai (2020) <doi:10.1177/1471082X19825523> performs covariate selection using the information on all quantiles simultaneously. Nonlinear parametric quantile models, in which the conditional quantile function can be a nonlinear function of the parameters, of the covariates or both, are estimated by integrated loss minimization as in Bottai and Cilluffo (2020) <doi:10.1177/0962280220941159>. Both approaches extend the quantile regression coefficients modeling framework of Frumento and Bottai (2016) <doi:10.1111/biom.12410> implemented in 'qrcm'.

Version: 1.0.0
Depends: R (≥ 4.1.0)
Imports: graphics, grDevices, qrcm (≥ 3.0), stats, survival (≥ 2.41), utils
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2026-10-30
DOI: 10.32614/CRAN.package.qrcmNP
Author: Gianluca Sottile ORCID iD [aut, cre], Paolo Frumento [aut, cph] (author of parts of the code adapted from 'qrcm')
Maintainer: Gianluca Sottile <gianluca.sottile at unipa.it>
BugReports: https://github.com/gianluca-sottile/qrcmNP/issues
License: GPL-2
URL: https://github.com/gianluca-sottile/qrcmNP
NeedsCompilation: no
Language: en-US
Citation: qrcmNP citation info
Materials: README, NEWS
CRAN checks: qrcmNP results

Documentation:

Reference manual: qrcmNP.html , qrcmNP.pdf
Vignettes: Penalized and nonlinear quantile regression coefficients modeling with qrcmNP (source, R code)

Downloads:

Package source: qrcmNP_1.0.0.tar.gz
Windows binaries: r-devel: qrcmNP_0.2.2.zip, r-release: qrcmNP_0.2.2.zip, r-oldrel: qrcmNP_0.2.2.zip
macOS binaries: r-release (arm64): qrcmNP_1.0.0.tgz, r-oldrel (arm64): qrcmNP_1.0.0.tgz, r-release (x86_64): qrcmNP_1.0.0.tgz, r-oldrel (x86_64): qrcmNP_1.0.0.tgz
Old sources: qrcmNP archive

Linking:

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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.