Dimension-reduction methods aim at defining a score that maximizes signal diversity. Two approaches, namely maximum entropy weights and maximum variance weights, are provided.
| Version: | 2017.12-03 |
| Depends: | R (≥ 3.3.0) |
| Suggests: | MASS, RUnit |
| Published: | 2017-12-04 |
| Author: | Zonglin He [aut, cre], Youyi Fong [aut] |
| Maintainer: | Zonglin He <nkhezl at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | ChangeLog |
| CRAN checks: | mdw results |
| Reference manual: | mdw.pdf |
| Package source: | mdw_2017.12-03.tar.gz |
| Windows binaries: | r-devel: mdw_2017.12-03.zip, r-release: mdw_2017.12-03.zip, r-oldrel: mdw_2017.12-03.zip |
| OS X binaries: | r-release: mdw_2017.12-03.tgz, r-oldrel: mdw_2017.12-03.tgz |
| Old sources: | mdw archive |
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