R package RGCCA: RGCCA and Sparse GCCA for multi-block data analysis. Multi-block data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: (i) to study the relationships between blocks and (ii) to identify subsets of variables of each block which are active in their relationships with the other blocks.
Keywords for this software
References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Wang, Wenjia; Zhou, Yi-Hui: Eigenvector-based sparse canonical correlation analysis: fast computation for estimation of multiple canonical vectors (2021)
- Derek Beaton: Generalized eigen, singular value, and partial least squares decompositions: The GSVD package (2020) arXiv
- Tenenhaus, Michel; Tenenhaus, Arthur; Groenen, Patrick J. F.: Regularized generalized canonical correlation analysis: a framework for sequential multiblock component methods (2017)
- Tenenhaus, Arthur; Philippe, Cathy; Frouin, Vincent: Kernel generalized canonical correlation analysis (2015)
- Tenenhaus, Arthur; Tenenhaus, Michel: Regularized generalized canonical correlation analysis for multiblock or multigroup data analysis (2014)