Robust Angle Based Joint and Individual Variation Explained R package RaJIVE version 1.0 A robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>. Software Depends: R (>= 3.1.0) Imports: ggplot2, doParallel, foreach Suggests: knitr, rmarkdown, testthat (>= 2.1.0), cowplot, reshape2, dplyr Erica Ponzi <erica.ponzi@medisin.uio.no> Comprehensive R Archive Network (CRAN) Erica Ponzi [aut, cre], Abhik Ghosh [aut] MIT + file LICENSE (https://CRAN.R-project.org/package=RaJIVE/LICENSE) 2021-02-04 application/tgz https://CRAN.R-project.org/package=RaJIVE doi:10.32614/CRAN.package.RaJIVE