Package: joinet
Version: 0.0.10
Title: Multivariate Elastic Net Regression
Description: 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 (<https://github.com/cran/MRCE>, <https://github.com/cran/remMap>).
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
Authors@R: person("Armin","Rauschenberger",email="armin.rauschenberger@uni.lu",role=c("aut","cre"))
VignetteBuilder: knitr
License: GPL-3
Language: en-GB
RoxygenNote: 7.1.1
URL: https://github.com/rauschenberger/joinet
BugReports: https://github.com/rauschenberger/joinet/issues
NeedsCompilation: no
Packaged: 2021-08-09 06:09:20 UTC; armin.rauschenberger
Author: Armin Rauschenberger [aut, cre]
Maintainer: Armin Rauschenberger <armin.rauschenberger@uni.lu>
Repository: CRAN
Date/Publication: 2021-08-09 07:40:02 UTC
Built: R 4.2.0; ; 2022-04-27 18:10:12 UTC; unix
