The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Performs the MRFA approach proposed by Sung et al. (2020) <doi:10.1080/01621459.2019.1595630> to fit and predict nonlinear regression problems, particularly for large-scale and high-dimensional problems. The application includes deterministic or stochastic computer experiments, spatial datasets, and so on.
| Version: | 0.6 |
| Depends: | R (≥ 2.14.1) |
| Imports: | fields, glmnet, grplasso, methods, plyr, randtoolbox, foreach, stats, graphics, utils |
| Published: | 2023-11-10 |
| Author: | Chih-Li Sung |
| Maintainer: | Chih-Li Sung <sungchih at msu.edu> |
| License: | GPL-2 | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | MRFA results |
| Reference manual: | MRFA.pdf |
| Package source: | MRFA_0.6.tar.gz |
| Windows binaries: | r-devel: MRFA_0.6.zip, r-release: MRFA_0.6.zip, r-oldrel: MRFA_0.6.zip |
| macOS binaries: | r-release (arm64): MRFA_0.6.tgz, r-oldrel (arm64): MRFA_0.6.tgz, r-release (x86_64): MRFA_0.6.tgz, r-oldrel (x86_64): MRFA_0.6.tgz |
| Old sources: | MRFA archive |
Please use the canonical form https://CRAN.R-project.org/package=MRFA to link to this page.
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.
Health stats visible at Monitor.