Improves the predictive performance of ridge and lasso regression exploiting one or more sources of prior information on the importance and direction of effects.
Install the current release from CRAN:
or the latest development version from GitHub or GitLab:
#install.packages("remotes")
remotes::install_github("lcsb-bds/transreg") # upstream
remotes::install_github("rauschenberger/transreg") # fork
remotes::install_gitlab("bds/transreg",host="gitlab.lcsb.uni.lu") # mirror
The code for reproducing the simulations and applications shown in the manuscript is available in a vignette (https://lcsb-bds.github.io/transreg/articles/analysis.html). After installing the package with remotes::intall_github("lcsb-bds/transreg",build_vignettes=TRUE)
and restarting R, the vignette can also be loaded with vignette(topic="analysis",package="transreg")
.
Armin Rauschenberger , Zied Landoulsi , Mark A. van de Wiel , and Enrico Glaab (2022). ‘Penalised regression with multiple sets of prior effects’. Manuscript in preparation. (arXiv: 2212.08581)
The R package transreg
implements penalised regression with multiple sources of prior effects (Rauschenberger et al., 2022).
Copyright © 2022 Armin Rauschenberger, University of Luxembourg, Luxembourg Centre for Systems Biomedicine (LCSB), Biomedical Data Science (BDS)
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.