Robust Angle Based Joint and Individual Variation Explained

A robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: ) 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) .


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install.packages("RaJIVE")

1.0 by Erica Ponzi, a month ago


Browse source code at https://github.com/cran/RaJIVE


Authors: Erica Ponzi [aut, cre] , Abhik Ghosh [aut]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports ggplot2, doParallel, foreach

Suggests knitr, rmarkdown, testthat, cowplot, reshape2, dplyr


See at CRAN