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| bootstrap | Compute bootstrap |
| bootstrap_k | Compute bootstrap (internal) |
| char_to_list | Convert a character in a vector |
| check_connection | Check the format of the connection matrix |
| check_quantitative | Check if a dataframe contains no qualitative variables |
| color_group | Groups of color |
| common_rows | Keep only the rows with the same names among a list of dataframe |
| comparison | comparison of two RGCCA results |
| cov2 | Variance and Covariance (Matrices) |
| cov3 | cov3 |
| createNA | Create a list with missing data (for simulation) |
| defl.select | deflation function |
| get_bloc_var | Get the blocs of each variables |
| get_bootstrap | Extract a bootstrap |
| get_comp | Get the components of the analysis |
| get_cor_all | Correlation between the blocks |
| get_ctr | Variable contribution |
| get_ctr2 | Get the indexes of the analysis |
| get_edges | Creates the edges for a design matrix |
| get_filename | File name from a path |
| get_nodes | Creates the nodes for a design matrix |
| get_patternNA | get_patternNA |
| imputeColmeans | Product for Matrices with missing data (pm) |
| imputeEM | imputeEM: impute with superblock method |
| imputeNN | imputeNN: Impute with k-Nearest Neighbors |
| imputeSB | imputeSB: impute with superblock method |
| initsvd | Initialisation by SVD decomposition of X |
| intersection | Intersection |
| is.character2 | Test for character vector |
| keep_common_rows | Keep only the rows with the same names among a list of dataframe |
| load_blocks | Create a list of matrix from loading files corresponding to blocks |
| load_file_excel | Creates a data frame from an Excel file loading |
| load_file_text | Creates a matrix from loading a file |
| load_response | Create a matrix corresponding to the response |
| MIRGCCA | Multiple imputation for RGCCA |
| miscrossprod | Cross product function for inputs with missing data. |
| naEvolution | Evolution of quality of rgcca with increasing missing values |
| order_df | Rank values of a dataframe in decreasing order |
| parallelize | Set a list of sockets for parralel package |
| plot.bootstrap | Plots a bootstrap object |
| plot.cv | plot.cv |
| plot.cval | plot.cval |
| plot.list_rgcca | plots a list_rgcca object |
| plot.naEvolution | plot.naEvolution |
| plot.patternNA | plot.patternNA |
| plot.permutation | plot.permutation Plots a permutation object. The parameters tuned for maximizing RGCCA criteria is displayed in the title. In x, the index of combination (number corresponding to the rownames of tuning parameters object). In ordinate, a score depending of the type parameter (RGCCA criterion for crit, and zstat for the pseudo z-scores) |
| plot.predict | plot.cv |
| plot.rgcca | Plots for RGCCA |
| plot.whichNAmethod | Which missing method to choose ? |
| plot2D | Plots RGCCA components in a bi-dimensional space |
| plot3D | Plot in 3 dimensions |
| plot_ave | Histogram of Average Variance Explained |
| plot_bootstrap_1D | Plot a bootstrap 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 permuation in 2D |
| plot_permut_3D | Plot permuation in 3D |
| plot_var_1D | Barplot of a fingerprint |
| plot_var_2D | Plot of variables space |
| pm | Product for Matrices with missing data (pm) |
| print.bootstrap | Print bootstrap |
| print.cval | print.cval |
| print.permutation | Prints the results of permutation rgcca |
| print.rgcca | Printing rgcca results Prints the call of rgcca results |
| print_comp | Print the variance of a component |
| remove_null_sd | Remove column having a standard deviation equals to 0 |
| rgcca | Regularized (or Sparse) Generalized Canonical Correlation Analysis (R/SGCCA) |
| rgccad | Regularized Generalized Canonical Correlation Analysis (RGCCA) |
| rgccak | Internal function for computing the RGCCA parameters (RGCCA block components, outer weight vectors, etc.). |
| rgccaNa | Regularized Generalized Canonical Correlation Analysis (RGCCA) |
| rgcca_crossvalidation | Cross-validation |
| rgcca_cv | rgcca_cv |
| rgcca_permutation | Tuning RGCCA parameters |
| rgcca_predict | Predict RGCCA |
| save_plot | Save a ggplot object |
| scale2 | Scaling and Centering of Matrix-like Objects |
| scale3 | scale3 |
| select_analysis | Define the analysis parameters |
| set_connection | Create a matrix corresponding to a connection between the blocks |
| sgcca | Variable Selection For Generalized Canonical Correlation Analysis (SGCCA) |
| sgccak | Internal function for computing the SGCCA parameters (SGCCA block components, outer weight vectors etc.) |
| sgccaNa | Regularized Generalized Canonical Correlation Analysis (RGCCA) |
| soft.threshold | The function soft.threshold() soft-thresholds a vector such that the L1-norm constraint is satisfied. |
| tau.estimate | Optimal shrinkage intensity parameters. |
| whichNAmethod | whichNAmethod |