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.
Efficient design matrix free procedure for solving a soft maximin problem for large scale array-tensor structured models, see Lund, Mogensen and Hansen (2019) <doi:10.48550/arXiv.1805.02407>. Currently Lasso and SCAD penalized estimation is implemented.
| Version: | 1.0.3 |
| Imports: | Rcpp (≥ 0.12.12) |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2020-09-17 |
| Author: | Adam Lund |
| Maintainer: | Adam Lund <adam.lund at math.ku.dk> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | SMMA results |
| Reference manual: | SMMA.pdf |
| Package source: | SMMA_1.0.3.tar.gz |
| Windows binaries: | r-devel: SMMA_1.0.3.zip, r-release: SMMA_1.0.3.zip, r-oldrel: SMMA_1.0.3.zip |
| macOS binaries: | r-release (arm64): SMMA_1.0.3.tgz, r-oldrel (arm64): SMMA_1.0.3.tgz, r-release (x86_64): SMMA_1.0.3.tgz, r-oldrel (x86_64): SMMA_1.0.3.tgz |
| Old sources: | SMMA archive |
Please use the canonical form https://CRAN.R-project.org/package=SMMA 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.