RegularizedSCA: Regularized simultaneous component analysis of multiblock data in R

Zhengguo Gu*, Katrijn Van Deun

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
48 Downloads (Pure)


This article introduces a package developed for R (R Core Team, 2017) for performing an integrated analysis of multiple data blocks (i.e., linked data) coming from different sources. The methods in this package combine simultaneous component analysis (SCA) with structured selection of variables. The key feature of this package is that it allows to (1) identify joint variation that is shared across all the data sources and specific variation that is associated with one or a few of the data sources and (2) flexibly estimate component matrices with predefined structures. Linked data occur in many disciplines (e.g., biomedical research, bioinformatics, chemometrics, finance, genomics, psychology, and sociology) and especially in multidisciplinary research. Hence, we expect our package to be useful in various fields.
Original languageEnglish
Pages (from-to)2268-2289
JournalBehavior Research Methods
Issue number5
Publication statusPublished - 2019


  • Common
  • Group Lasso
  • Lasso
  • Linked data analysis
  • Multiblock analysis
  • SCA
  • Simultaneous component analysis
  • distinctive components

Fingerprint Dive into the research topics of 'RegularizedSCA: Regularized simultaneous component analysis of multiblock data in R'. Together they form a unique fingerprint.

Cite this