Sparsemodels imports the core RGCCA implementation of the R Penalized Multivariate Analysis (RGCCA) package using rpy2. It is used to perform sparse generalized canonical correlation analysis (SGCCA) with optimization for parallel processing. SGCCA uses one of multiple components to maximize the covariance among multiple datasets (called data-views) while imposing an L1 penalty. The package also includes optional functions for SGCCA-regression, permutation testing, bootstrap analysis, and variable stability selection.
Tenenhaus, A., Philippe, C., Guillemot, V., Le Cao, K.-A., Grill, J., & Frouin, V. (2014). Variable selection for generalized canonical correlation analysis. Biostatistics , 15(3), 569–583.
Tenenhaus, M., Tenenhaus, A., & Groenen, P. J. F. (2017). Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods. Psychometrika. https://doi.org/10.1007/s11336-017-9573-x
git clone https://github.com/trislett/sparsemodels
cd sparsemodels
conda env create -f environment.yml
conda activate sparsemodels_env
pip install git+https://github.com/trislett/sparsemodels