joinet: Multivariate Elastic Net Regression

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. If required, install MRCE or remMap from GitHub (<https://github.com/cran/MRCE>, <https://github.com/cran/remMap>).

Version: 0.0.9
Depends: R (≥ 3.0.0)
Imports: glmnet, palasso, cornet
Suggests: knitr, rmarkdown, testthat, MASS
Enhances: mice, earth, spls, MRCE, remMap, MultivariateRandomForest, SiER, mcen, GPM, RMTL, MTPS
Published: 2021-07-16
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

Downloads:

Reference manual: joinet.pdf
Vignettes: article
vignette
analysis
Package source: joinet_0.0.9.tar.gz
Windows binaries: r-devel: joinet_0.0.9.zip, r-release: joinet_0.0.9.zip, r-oldrel: joinet_0.0.9.zip
macOS binaries: r-release (arm64): joinet_0.0.9.tgz, r-release (x86_64): joinet_0.0.9.tgz, r-oldrel: joinet_0.0.9.tgz
Old sources: joinet archive

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