joinet: Multivariate Elastic Net Regression

Implements high-dimensional multivariate regression by stacked generalisation (Rauschenberger 2021 <doi:10.1093/bioinformatics/btab576>). 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 (<>, <>).

Version: 0.0.10
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: 2021-08-09
DOI: 10.32614/CRAN.package.joinet
Author: Armin Rauschenberger [aut, cre]
Maintainer: Armin Rauschenberger <armin.rauschenberger at>
License: GPL-3
NeedsCompilation: no
Language: en-GB
Citation: joinet citation info
Materials: README NEWS
In views: MachineLearning
CRAN checks: joinet results


Reference manual: joinet.pdf
Vignettes: article


Package source: joinet_0.0.10.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): joinet_0.0.10.tgz, r-oldrel (arm64): joinet_0.0.10.tgz, r-release (x86_64): joinet_0.0.10.tgz, r-oldrel (x86_64): joinet_0.0.10.tgz
Old sources: joinet archive

Reverse dependencies:

Reverse imports: transreg


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