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.
The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing supervised classification of high-dimensional and correlated profiles. The procedure combines a decorrelation step based on a factor modeling of the dependence among covariates and a classification method. The available methods are Lasso regularized logistic model (see Friedman et al. (2010)), sparse linear discriminant analysis (see Clemmensen et al. (2011)), shrinkage linear and diagonal discriminant analysis (see M. Ahdesmaki et al. (2010)). More methods of classification can be used on the decorrelated data provided by the package FADA.
| Version: | 1.3.5 |
| Depends: | MASS, elasticnet |
| Imports: | sparseLDA, sda, glmnet, mnormt, crossval, corpcor, matrixStats, methods |
| Published: | 2019-12-10 |
| Author: | Emeline Perthame (Institut Pasteur, Paris, France), Chloe Friguet (Universite de Bretagne Sud, Vannes, France) and David Causeur (Agrocampus Ouest, Rennes, France) |
| Maintainer: | David Causeur <david.causeur at agrocampus-ouest.fr> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | FADA results |
| Reference manual: | FADA.pdf |
| Package source: | FADA_1.3.5.tar.gz |
| Windows binaries: | r-devel: FADA_1.3.5.zip, r-release: FADA_1.3.5.zip, r-oldrel: FADA_1.3.5.zip |
| macOS binaries: | r-release (arm64): FADA_1.3.5.tgz, r-oldrel (arm64): FADA_1.3.5.tgz, r-release (x86_64): FADA_1.3.5.tgz, r-oldrel (x86_64): FADA_1.3.5.tgz |
| Old sources: | FADA archive |
Please use the canonical form https://CRAN.R-project.org/package=FADA 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.