Implements high-dimensional multivariate regression by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement.
| Version: | 0.0.3 |
| Depends: | R (≥ 3.0.0) |
| Imports: | glmnet, palasso, cornet |
| Suggests: | knitr, testthat, MASS |
| Enhances: | spls, SiER, MRCE |
| Published: | 2019-11-13 |
| Author: | Armin Rauschenberger [aut, cre] |
| Maintainer: | Armin Rauschenberger <armin.rauschenberger at uni.lu> |
| BugReports: | https://github.com/rauschenberger/joinet/issues |
| License: | GPL-3 |
| URL: | https://github.com/rauschenberger/joinet |
| NeedsCompilation: | no |
| Language: | en-GB |
| Materials: | README NEWS |
| CRAN checks: | joinet results |
| Reference manual: | joinet.pdf |
| Vignettes: |
article vignette |
| Package source: | joinet_0.0.3.tar.gz |
| Windows binaries: | r-devel: joinet_0.0.3.zip, r-release: joinet_0.0.3.zip, r-oldrel: joinet_0.0.3.zip |
| macOS binaries: | r-release: joinet_0.0.3.tgz, r-oldrel: joinet_0.0.3.tgz |
| Old sources: | joinet archive |
| Reverse imports: | starnet |
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