Description
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>).
README.md
Scope
Multivariate elastic net regression through stacked generalisation (extending the R package glmnet).
Installation
Install the current release from CRAN:
install.packages("joinet")
or the latest development version from GitHub:
#install.packages("devtools")
devtools::install_github("rauschenberger/joinet")
Reference
Armin Rauschenberger and Enrico Glaab (2021). “Predicting correlated outcomes from molecular data”. Bioinformatics btab576. doi: 10.1093/bioinformatics/btab576.