Package: joinet
Version: 0.0.6
Title: Multivariate Elastic Net Regression
Description: 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 from GitHub (<https://github.com/cran/MRCE>).
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
LazyData: true
Language: en-GB
RoxygenNote: 7.1.1
URL: https://github.com/rauschenberger/joinet
BugReports: https://github.com/rauschenberger/joinet/issues
NeedsCompilation: no
Packaged: 2020-11-18 09:19:56 UTC; armin.rauschenberger
Author: Armin Rauschenberger [aut, cre]
Maintainer: Armin Rauschenberger <armin.rauschenberger@uni.lu>
Repository: CRAN
Date/Publication: 2020-11-23 10:50:16 UTC
