Man pages for bbuchsbaum/rMVPA
Multivoxel Pattern Analysis in R
| as_roi | Convert an object to an ROIVolume or ROISurface object |
| binary_classification_result | Classification results for binary outcome |
| classification_result | Create a 'classification_result' instance |
| coalesce_join2 | Coalesce Join Two Data Frames |
| combine_prediction_tables | Combine prediction tables |
| combine_randomized | Combine randomized classifier results |
| combine_regional_results | Combine Regional Results |
| combine_rsa_standard | Combine RSA standard classifier results |
| combine_standard | Combine standard classifier results |
| combine_vector_rsa_standard | Combine Vector RSA standard classifier results |
| comp_perf | Compile performance results and generate volumetric results |
| compute_performance | Compute Performance Metrics for Classification/Regression... |
| create_dist | Create Distance Function Object |
| cross_validation | bootstrap_blocked_cross_validation |
| crossval_samples | crossval_samples |
| crossv_block | Block Cross-Validation Data Preparation |
| crossv_bootstrap_block | Block Bootstrap Cross-Validation Data Preparation |
| crossv_k | K-fold Cross-Validation Data Preparation |
| custom_performance | Apply Custom Performance Metric to Prediction Result |
| data_sample | Extract Sample from Dataset |
| distance-constructors | Distance Function Constructors |
| do_merge_results | Merge searchlight results |
| do_randomized | Perform randomized searchlight analysis |
| do_standard | Perform standard searchlight analysis |
| external_crossval | External Cross-Validation |
| feature_rsa_iterate | feature_rsa_iterate |
| feature_selector | Create a feature selection specification |
| filter_roi | Filter Region of Interest (ROI) |
| fit_model | Fit Model |
| fit_model.mvpa_model | Fit an MVPA model |
| gen_sample_dataset | gen_sample_dataset |
| get_samples | Get Multiple Data Samples |
| get_searchlight | Generate Searchlight Iterator |
| group_means | Compute Group Means of a Matrix |
| has_test_set | Test Set Availability |
| kfold_cross_validation | kfold_cross_validation |
| list_model | a list of model fits |
| load_model | load_model |
| manova_design | Create a MANOVA Design |
| manova_iterate | MANOVA Iteration for Voxel Sets |
| manova_model | Create a MANOVA Model |
| merge_predictions | Merge predictions from multiple models |
| merge_results | Merge Multiple Classification/Regression Results |
| merge_results.regional_mvpa_result | Merge regional MVPA results |
| model_fit | Create a Model Fit Object |
| multiway_classification_result | Create a Multiway Classification Result Object |
| mvpa_dataset | mvpa_dataset |
| mvpa_design | Create an MVPA Design Object |
| mvpa_iterate | MVPA Iteration for Voxel Sets with Parallelization |
| mvpa_model | Create an MVPA Model |
| mvpa_surface_dataset | mvpa_surface_dataset |
| nobs | Get Number of Observations |
| nresponses | Number of response categories |
| pairwise_dist | Generic Pairwise Distance Computation |
| pairwise_dist.cordist | Compute Pairwise Correlation Distances |
| pairwise_dist.euclidean | Compute Pairwise Euclidean Distances |
| pairwise_dist.mahalanobis | Compute Pairwise Mahalanobis Distances |
| pairwise_dist.robustmahadist | Compute Pairwise Robust Mahalanobis Distances |
| performance | Compute Performance Metrics for Classification/Regression... |
| performance.regression_result | Calculate Performance Metrics for Regression Result |
| pool_randomized | Combine randomized searchlight results by pooling |
| predict.class_model_fit | This function predicts class labels and probabilities for new... |
| predicted_class | Calculate the Predicted Class from Probability Matrix |
| predict.regression_model_fit | Predict continuous values for a new dataset using a... |
| prep_regional | Prepare regional data for MVPA analysis |
| prob_observed | Probability of observed class |
| regional_mvpa_result | Create a 'regional_mvpa_result' instance |
| regression_result | Create a Regression Result Object |
| rMVPA | rMVPA: A package for multi-voxel pattern analysis (MVPA) |
| rsa_design | Construct a design for an RSA (Representational Similarity... |
| rsa_iterate | rsa_iterate |
| rsa_model | Construct an RSA (Representational Similarity Analysis) model |
| rsa_model_mat | Construct a model matrix for an RSA (Representational... |
| run_regional | Region of interest based MVPA analysis |
| run_regional.mvpa_model | Run regional MVPA analysis on a specified MVPA model |
| run_regional.rsa_model | Run regional RSA analysis on a specified RSA model |
| run_searchlight | run_searchlight |
| second_order_similarity | Compute Second-Order Similarity Scores |
| select_features | Select Features |
| select_features.catscore | Perform feature selection using the CATSCORE method |
| select_features.FTest | Perform feature selection using the F-test method |
| sub_result | Extract Row-wise Subset of a Result Object |
| sub_result.binary_classification_result | Subset Binary Classification Result |
| sub_result.multiway_classification_result | Subset Multiway Classification Result |
| test_design | Test Design Extraction |
| train_model | Train Model |
| train_model.feature_rsa_model | Train an RSA Model |
| train_model.manova_model | Train a MANOVA Model |
| train_model.mvpa_model | Train an MVPA Model |
| train_model.rsa_model | Train an RSA Model |
| train_model.vector_rsa_model | Train a vector RSA model |
| tune_grid | Tune Grid Extraction |
| tune_model | This function finds the best hyperparameters for a given... |
| twofold_blocked_cross_validation | twofold_blocked_cross_validation |
| vector_rsa_design | Construct a design for a vectorized RSA model |
| vector_rsa_model | Create a vectorized RSA model |
| weighted_model | Create a Weighted Consensus Model |
| wrap_out | Wrap output results |
| wrap_output | Wrap output values into a desired format |
| y_test | Test Labels/Response Extraction |
| y_train | Training Labels/Response Extraction |
