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An efficient algorithm to fit and tune kernel quantile regression models based on the majorization-minimization (MM) method. It can also fit multiple quantile curves simultaneously without crossing.
| Version: | 1.0.0 |
| Depends: | R (≥ 3.5.0), methods |
| Imports: | graphics, grDevices, stats, utils, dotCall64, rlang, MASS, Matrix |
| Suggests: | knitr, rmarkdown |
| Published: | 2024-05-13 |
| Author: | Qian Tang [aut, cre], Yuwen Gu [aut], Boxiang Wang [aut] |
| Maintainer: | Qian Tang <qian-tang at uiowa.edu> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| CRAN checks: | fastkqr results |
| Reference manual: | fastkqr.pdf |
| Vignettes: |
Getting started with fastkqr |
| Package source: | fastkqr_1.0.0.tar.gz |
| Windows binaries: | r-devel: fastkqr_1.0.0.zip, r-release: fastkqr_1.0.0.zip, r-oldrel: fastkqr_1.0.0.zip |
| macOS binaries: | r-release (arm64): fastkqr_1.0.0.tgz, r-oldrel (arm64): fastkqr_1.0.0.tgz, r-release (x86_64): fastkqr_1.0.0.tgz, r-oldrel (x86_64): fastkqr_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.
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