RaJIVE

Description

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>.

Downloads

Trend: +16.1% (30d vs prior 30d)

CRAN Check Status

Maintainer: ‘Erica Ponzi <erica.ponzi@medisin.uio.no>’

The Description field contains
  <arXiv:2101.09110>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Erica Ponzi <erica.ponzi@medisin.uio.no>’

The Description field contains
  <arXiv:2101.09110>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Check History

Maintainer: ‘Erica Ponzi <erica.ponzi@medisin.uio.no>’

The Description field contains
  <arXiv:2101.09110>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Maintainer: ‘Erica Ponzi <erica.ponzi@medisin.uio.no>’

The Description field contains
  <arXiv:2101.09110>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.

Dependency Network

Version History