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| RGCCA-package | Regularized (or Sparse) Generalized Canonical Correlation Analysis (R/SGCCA) for multi-block data analysis |
| bootstrap | Bootstrap confidence intervals and p-values |
| ECSI | European Customer Satisfaction Index |
| get_bootstrap | Extract statistics from a fitted bootstrap object |
| load_blocks | Create a list of matrix from loading files corresponding to blocks |
| load_response | Create a matrix corresponding to the response |
| plot.bootstrap | Plot a fitted bootstrap object |
| plot.cval | Plot cross-validation |
| plot.permutation | Plot fitted rgcca permutation object |
| plot.predict | plot.predict |
| plot.rgcca | Plot for RGCCA |
| plot2D | Plot RGCCA components in a bi-dimensional space |
| plot_ave | Histogram of Average Variance Explained |
| plot_bootstrap_1D | Plot a fitted bootstrap object in 1D |
| plot_bootstrap_2D | Plot a bootstrap in 2D |
| plot_histogram | Histogram settings |
| plot_ind | Plot the two components of a RGCCA |
| plot_network | Plot the connection between blocks |
| plot_network2 | Plot the connection between blocks (dynamic plot) |
| plot_permut_2D | Plot permutation in 2D |
| plot_var_1D | Barplot of a fingerprint |
| plot_var_2D | Plot of variables space |
| print.bootstrap | Print bootstrap |
| print.cval | print.cval |
| print.permutation | Print a fitted rgcca_permutation object |
| print.rgcca | Print the call of rgcca results |
| print_comp | Print the variance of a component |
| RGCCA | Regularized (or Sparse) Generalized Canonical Correlation Analysis (R/SGCCA) for multi-block data analysis |
| rgcca | Regularized (or Sparse) Generalized Canonical Correlation Analysis (S/RGCCA) |
| rgccad | Regularized Generalized Canonical Correlation Analysis (RGCCA) |
| rgccak | Internal function for computing the RGCCA parameters (RGCCA block components, outer weight vectors, etc.). |
| rgcca_cv | Tune RGCCA parameters in 'supervised' mode with cross-validation |
| rgcca_cv_k | Cross-validation |
| rgcca_permutation | Tune the S/RGCCA hyper-parameters by permutation |
| rgcca_predict | Predict RGCCA |
| rgcca_stability | Stability selection for SGCCA |
| Russett | Russett data |
| save_plot | Save a ggplot object |
| select_analysis | Define the parameters associated with each multi-block component method of the literature. |
| set_connection | Create either a superblock design matrix (if superblock = TRUE), or a supervised design matrix (if response != NULL) or a fully connected design matrix (if response == NULL and superblock == FALSE) |
| sgcca | Variable Selection For Generalized Canonical Correlation Analysis (SGCCA) |
| sgccak | Internal function for computing the SGCCA parameters (SGCCA block components, outer weight vectors etc.) |