Man pages for cgplab/RadAR
Radiomic Analysis with R
| calc_concordance_index | Compute concordance index for radiomic features |
| calc_cox_regression | Fit a Cox regression model using radiomic features |
| calc_differential_radiomics | Perform differential radiomic analysis |
| do_feature_selection | Perform feature selection based on different methods |
| do_hierarchical_clustering | Perform hierarchical clustering of the radiomic dataset |
| features_to_dictionary | Retrieve the description of a list of input features |
| filter_by_feature_type | Filter a RadAR object by feature type(s) |
| filter_by_image_type | Filter a RadAR object by image type(s) |
| find_clusters | Flag samples and/or features by cluster membership |
| find_feature_outliers | Find and replace radiomic feature outliers based on IQR. |
| find_outliers | Filter out oulier patients |
| import_3dslicer | Import 3DSlicer data |
| import_lifex | Import LifeX data |
| import_lifex_session | Import LifeX session data |
| import_pyradiomics | Import pyradiomics data |
| import_radiomic_table | Import features |
| normalize_feature_values | Apply normalization to feature values |
| plot_correlation_matrix | Draw correlation matrix of radiomic features |
| plot_features | Draw boxplot + stripchart of selected feature(s) |
| plot_heatmap_hcl | Draw a clustered heatmap of the radiomic dataset |
| print_distance_methods | Print available methods for computing distance |
| print_feature_type | Print available feature types |
| print_hcl_methods | Print available methods for hierarchical clustering |
| print_image_type | Print available image types |
| scale_feature_values | This function implements different scaling strategies to... |
| select_top_features | Select top radiomic features according to a given statistics |
| test_radiomic_signature | Test radiomic signature by Cox regression model |
