Package index • rMVPA

REMAP Diagnostics

Summaries that quantify how far memory is from perception and how large the learned correction is.

summarize_remap_roi(): Summarize REMAP diagnostics at the ROI level
summarize_remap_items(): Summarize REMAP item-level residuals for an ROI

All Functions

All functions exported by rMVPA

MVPAModels: Pre-defined MVPA Classification Models
add_interaction_contrasts(): Add Interaction Contrasts to an msreve_design
as_roi(): Convert object to ROI
average_labels(): Average NeuroVec Data by Labels
balance_partitions(): Balance Cross-Validation Partitions
banded_ridge_da() grouped_ridge_da(): Convenience wrapper: build a grouped-ridge domain-adaptation model from matrices
banded_ridge_da_model() grouped_ridge_da_model(): Grouped (banded) ridge domain-adaptation model (continuous predictors -> brain)
binary_classification_result(): Classification results for binary outcome
blocks(): Define consecutive column blocks as feature sets
build_analysis(): Build an Analysis Object
by_set(): Define feature sets by a per-column set label
category_rdm(): Create Hypothesis RDM from Category Structure
classification_result(): Create a classification_result instance
combine_prediction_tables(): Combine prediction tables
compute_local_redundancy(): Compute Local Redundancy for Each Searchlight Center
contrast_rsa_model(): Constructor for contrast_rsa_model
contrasts(): Generate Contrast Matrices
create_dist(): Create a Distance Function Object
create_model_spec(): Create a Generic Model Specification
bootstrap_blocked_cross_validation() blocked_cross_validation() sequential_blocked_cross_validation() custom_cross_validation(): bootstrap_blocked_cross_validation
crossv_block(): Block Cross-Validation Data Preparation
crossv_k(): K-fold Cross-Validation Data Preparation
crossval_samples(): Cross-validation samples
custom_performance(): Apply Custom Performance Metric to Prediction Result
cv_evaluate_roi(): Evaluate One ROI with rMVPA Cross-Validation Helpers
data_sample(): Extract Sample from Dataset
cordist() mahadist() eucdist() euclidean() robustmahadist() pcadist(): Distance Function Constructors
era_partition_model(): ERA Variance-Partition Model
era_rsa_design(): Build item-level ERA-RSA confounds and summaries from mvpa_design
era_rsa_model(): ERA-RSA: Encoding-Retrieval Similarity and ER Geometry
evaluate_model.feature_rsa_model(): Evaluate model performance for feature RSA
evaluate_model.vector_rsa_model(): Evaluate model performance for vector RSA
expected_features(): Build expected-domain features from a soft alignment matrix
explain_searchlight_engine(): Explain Searchlight Engine Selection
extract_weights(): Extract Raw Model Weights
feature_rsa_connectivity(): Compute ROI-by-ROI Representational Connectivity from Feature RSA Predictions
feature_rsa_cross_connectivity(): Compute Cross-Connectivity: Predicted-Observed ROI x ROI Matrix
feature_rsa_da_model(): Feature-RSA Domain Adaptation Model
feature_rsa_design(): Create a Feature-Based RSA Design
feature_rsa_model(): Create a Feature-Based RSA Model
feature_rsa_rdm_vectors(): Extract Per-ROI Predicted and Observed RDM Vectors from Feature RSA Results
feature_selection: Feature Selection Methods
feature_selector(): Create a feature selection specification
feature_sets(): Feature sets: grouped predictor matrices
feature_sets_design(): Feature-sets design (mvpa_design extension for continuous regression)
fit_model(): Fit Model
fit_roi(): Fit a Model on a Single ROI
fit_roi(<contrast_rsa_model>): Fit ROI for contrast_rsa_model
format_result(): Format Result Object
gen_clustered_sample_dataset(): Generate a Synthetic Clustered MVPA Dataset
gen_sample_dataset(): Generate Sample Dataset for MVPA Analysis
generate_folds(): Generate Cross-Validation Folds
get_center_ids(): Get Center IDs for Searchlight Iteration
get_feature_matrix(): Extract Full Feature Matrix from a Dataset
get_nfolds(): Get the Number of Folds
get_samples(): Get Multiple Data Samples
get_searchlight(): Generate Searchlight Iterator
global_mvpa_result(): Construct a Global MVPA Result
group_means(): Compute Group Means of a Matrix
has_crossval(): Cross-Validation Availability
has_test_set(): Test Set Availability
haufe_importance(): Haufe Feature Importance (Activation Patterns)
install_cli(): Install rMVPA Command-Line Wrappers
item_design(): Construct an ITEM design for trial-wise decoding
item_model(): ITEM decoding model for ROI/searchlight analysis
kfold_cross_validation(): kfold_cross_validation
load_model(): Load a Pre-defined MVPA Model
make_feature_contrasts(): Generate Contrasts from a Feature Matrix (Optional PCA)
manova_design(): Create a MANOVA Design
manova_model(): Create a MANOVA Model
merge_classif_results(): Merge Multiple Classification/Regression Results
merge_predictions(): Merge Predictions
merge_results(<manova_model>) merge_results(<mvpa_model>) merge_results(<binary_classification_result>) merge_results(<regression_result>) merge_results(<multiway_classification_result>) merge_results(<regional_mvpa_result>) merge_results(<rsa_model>) merge_results(<vector_rsa_model>) merge_results(<feature_rsa_model>): Merge Results for MANOVA Model
merge_results(): Merge Multiple Results
mock_context(): Build a Mock fit_roi Context
mock_roi_data(): Build Mock ROI Data for Plugin Tests
model_importance(): Per-Feature Model Importance
msreve_design(): Constructor for msreve_design
msreve_temporal_confounds(): MS-ReVE: build temporal nuisance RDMs from a spec
multiway_classification_result(): Create a Multiway Classification Result Object
mvpa_clustered_dataset(): Create an MVPA Dataset for Clustered/Parcellated Data
mvpa_config(): Build a Public Analysis Configuration
mvpa_dataset(): Create an MVPA Dataset Object
mvpa_design(): Create an MVPA Design Object
mvpa_iterate(): Iterate MVPA Analysis Over Multiple ROIs
mvpa_model(): Create an MVPA Model
mvpa_multibasis_dataset(): Create a Multibasis MVPA Image Dataset
mvpa_multibasis_image_dataset(): Alias for mvpa_multibasis_dataset
mvpa_surface_dataset(): Create a Surface-Based MVPA Dataset Object
mvpa_sysinfo(): Report System and Package Information for rMVPA
naive_xdec_model(): Naive Cross-Decoding (correlation to source prototypes)
new-analysis-overview: Extending rMVPA: Plugin Analysis Models
nmf_preprocess_maps(): Preprocess Maps for Spatial NMF
nobs(): Get Number of Observations
nresponses(): Number of Response Categories
orthogonalize_contrasts(): Orthogonalize a Contrast Matrix
output_schema(<contrast_rsa_model>): Output schema for contrast_rsa_model
performance(): Compute Performance Metrics
performance(<regression_result>): Calculate Performance Metrics for Regression Result
permutation_control(): Create a Permutation Control Object
permute_labels(): Permute Training Labels in an MVPA Design
predict_model(): Predict Model Output
predicted_class(): Calculate the Predicted Class from Probability Matrix
prep_regional(): Prepare regional data for MVPA analysis
print(<feature_rsa_design>): Print Method for Feature RSA Design
print(<feature_rsa_model>): Print Method for Feature RSA Model
print(<feature_sets>): Print method for feature_sets
print(<feature_sets_design>): Print method for feature_sets_design
print(<vector_rsa_design>): Print Method for vector_rsa_design
print(<vector_rsa_model>): Print Method for vector_rsa_model
prob_observed(): Probability of Observed Class
process_roi(): Process ROI
region_importance(): Region Importance via Random Subset Comparison
region_importance_result(): Construct a Region Importance Result
regional_mvpa_result(): Create a regional_mvpa_result instance
register_mvpa_model(): Register a Custom MVPA Model
regression_result(): Create a Regression Result Object
remap_rrr_model(): REMAP-RRR: Residual low-rank, domain-adaptive cross-decoding
repmap_design(): Representational mapping design helper (ReNA-Map)
repmap_model(): Representational mapping model (ReNA-Map)
repmed_design(): Representational mediation design helper (ReNA-RM)
repmed_model(): Representational mediation model (ReNA-RM)
repnet_design(): Representational connectivity design helper (ReNA-RC)
repnet_model(): Representational connectivity model (ReNA-RC)
rmvpa_api_lifecycle(): List the rMVPA API Lifecycle Registry
rmvpa_stable_api(): Return the Stable rMVPA API Surface
roi_result(): Construct a Standardized ROI Result
rsa_design(): Construct a design for an RSA (Representational Similarity Analysis) model
rsa_model(): Construct an RSA (Representational Similarity Analysis) model
run_analysis(): Run a Built Analysis
run_custom_regional(): Run a Custom Analysis Function Regionally
run_custom_searchlight(): Run a Custom Analysis Function in a Searchlight
run_global(): Run Global (Whole-Brain) MVPA Analysis
run_permutation_searchlight(): Run Permutation Searchlight Inference
run_regional() run_regional_base(): Region of Interest Based MVPA Analysis
run_searchlight(): Run Searchlight Analysis
run_searchlight(<default>): Default method for run_searchlight
save_results(): Save MVPA Results to Disk
save_results(<rmvpa_analysis_run>): Save a High-Level Analysis Run
schema_scalar() schema_vector(): Metric Schema Constructors
searchlight_engines(): Summarize Available Searchlight Engines
searchlight_mode(): Get Or Set Searchlight Mode
second_order_similarity(): Compute Second-Order Similarity Scores
select_features(): Select Features
set_log_level(): Set rMVPA Logging Level
shard_backend: Experimental Shared-Memory Backend for Parallel MVPA
shard_cleanup(): Clean Up Shared-Memory Segments
spatial_nmf(): Spatial Non-negative Matrix Factorization
spatial_nmf_component_test(): Component-level Inference for Spatial NMF
spatial_nmf_global_test(): Global Cross-validated Group Test for Spatial NMF
spatial_nmf_maps(): Spatial NMF on Map Lists
spatial_nmf_stability(): Bootstrap Stability for Spatial NMF Components
spatial_nmf_voxelwise_stats(): Voxelwise Statistics from Spatial NMF Stability
strip_dataset(): Strip Dataset from Model Specification
sub_result(<multiway_classification_result>) sub_result(<binary_classification_result>): Subset Multiway Classification Result
sub_result(): Extract Row-wise Subset of a Result
subsample_centers(): Subsample Searchlight Centers
subspace_alignment_model(): Subspace Alignment cross-decoder
summarize_remap_items(): Summarize REMAP item-level residuals for an ROI
summarize_remap_roi(): Summarize REMAP diagnostics at the ROI level
summary(<feature_rsa_model>): Summary Method for Feature RSA Model
table_to_rdm(): Convert Similarity Table to RDM
temporal(): Temporal RDM wrapper for formula usage
temporal_confounds(): Build multiple temporal confounds from a spec
temporal_from_onsets(): Temporal RDM from onsets (sugar)
temporal_hrf_overlap(): HRF-overlap temporal confound RDM
temporal_nuisance_for_msreve(): Temporal nuisance RDM at condition level (for MS-ReVE)
temporal_rdm(): Temporal/ordinal nuisance RDM (trial-level)
test_design(): Test Design Extraction
train_indices(): Get Training Indices for a Fold
transform_contrasts(): Apply Transformations to an Existing Contrast Matrix
tune_grid(): Extract Tuning Grid
twofold_blocked_cross_validation(): twofold_blocked_cross_validation
use_shard(): Enable Shared-Memory Backend for an MVPA Model Spec
validate_analysis(): Validate an MVPA Analysis for Common Methodological Issues
validate_cutoff(): Validate Feature-Selection Cutoff
validate_model_spec() print(<model_spec_validation_result>): Validate a Model Specification for Plugin Readiness
validate_plugin_model() print(<plugin_validation_result>): Validate a Plugin Model Contract
vector_rsa_design(): Construct a design for a vectorized RSA model
vector_rsa_model(): Create a vectorized RSA model
y_test(): Test Labels/Response Extraction
y_train(): Training Labels/Response Extraction