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 |
Documentation:
Downloads:
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