Package: fastkqr 1.0.0

Qian Tang

fastkqr: A Fast Algorithm for Kernel Quantile Regression

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.

Authors:Qian Tang [aut, cre], Yuwen Gu [aut], Boxiang Wang [aut]

fastkqr_1.0.0.tar.gz
fastkqr_1.0.0.tar.gz(r-4.5-noble)fastkqr_1.0.0.tar.gz(r-4.4-noble)
fastkqr_1.0.0.tgz(r-4.4-emscripten)fastkqr_1.0.0.tgz(r-4.3-emscripten)
fastkqr.pdf |fastkqr.html✨
fastkqr/json (API)

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

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • fortran– Runtime library for GNU Fortran applications

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 515 downloads 4 exports 5 dependencies

Last updated 6 months agofrom:80a6e89af8. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 10 2024
R-4.5-linux-x86_64NOTENov 10 2024

Exports:cv.kqrcv.nckqrkqrnckqr

Dependencies:dotCall64latticeMASSMatrixrlang

Getting started with fastkqr

An introductory tutorial with examples

Rendered fromfastkqr.Rmdusingknitr::rmarkdownon Nov 10 2024.

Last update: 2024-05-14
Started: 2024-05-14