Implements penalised multivariate regression (i.e., for multiple outcomes and many features) by stacked generalisation (<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. For optional comparisons, install 'remMap' from GitHub (<https://github.com/cran/remMap>).
Version: 1.0.0 Depends: R (≥ 3.0.0) Imports: glmnet, palasso, cornet Suggests: knitr, rmarkdown, testthat, MASS, mice, earth, spls, MRCE, remMap, MultivariateRandomForest, SiER, mcen, GPM, RMTL, MTPS Published: 2024-09-27 DOI: 10.32614/CRAN.package.joinet 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, https://rauschenberger.github.io/joinet/ NeedsCompilation: no Citation: joinet citation info Materials: README NEWS In views: MachineLearning CRAN checks: joinet results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=joinet to link to this page.
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