SPQR: Semi-Parametric Quantile Regression

Methods for flexible estimation of conditional density and quantile function, as well as model agnostic tools for analyzing quantile covariate effect and variable importance. The estimation method implements the semi-parametric quantile regression model described in Xu and Reich (2021) <doi:10.1111/biom.13576>, and the model agnostic tools extend accumulative local effects (ALE) to quantile regression setting.

Version: 0.1.0
Depends: R (≥ 3.6)
Imports: Rcpp (≥ 1.0.8), stats, torch, splines2, ggplot2, loo, progress, progressr, interp, RColorBrewer, yaImpute, coro (≥ 1.0.2)
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (≥ 3.0.0)
Published: 2022-05-02
Author: Steven Xu [aut, cre], Reetam Majumder [aut], Brian Reich [ctb]
Maintainer: Steven Xu <sgxu at ncsu.edu>
BugReports: https://github.com/stevengxu/SPQR/issues
License: GPL (≥ 3)
URL: https://github.com/stevengxu/SPQR
NeedsCompilation: yes
SystemRequirements: C++11
Materials: README NEWS
CRAN checks: SPQR results


Reference manual: SPQR.pdf


Package source: SPQR_0.1.0.tar.gz
Windows binaries: r-devel: SPQR_0.1.0.zip, r-release: SPQR_0.1.0.zip, r-oldrel: SPQR_0.1.0.zip
macOS binaries: r-release (arm64): SPQR_0.1.0.tgz, r-oldrel (arm64): SPQR_0.1.0.tgz, r-release (x86_64): SPQR_0.1.0.tgz, r-oldrel (x86_64): SPQR_0.1.0.tgz


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