Package index • neurocluster

All functions

acsc(): Adaptive Correlation Superclustering (ACSC)
check_cluster_ordering(): Check for Spatial Ordering in Cluster IDs
cl_class_ids(<cluster_result>): Extract Class IDs from Cluster Result
cluster4d(): Unified 4D Clustering for Neuroimaging Data
cluster4d_acsc(): Cluster4d using ACSC method
cluster4d_brsslic(): Cluster4d using boundary-refined sketch SLIC
cluster4d_commute(): Cluster4d using commute-time spectral method
cluster4d_corrslic(): Cluster4d using correlation-embedded SLIC method
cluster4d_flash3d(): Cluster4d using FLASH-3D method
cluster4d_g3s(): Gradient-guided geodesic clustering on a masked grid
cluster4d_mcl(): Cluster4d using sparse Markov Clustering (MCL)
cluster4d_rena(): Cluster4d using ReNA method
cluster4d_rena_plus(): Cluster4d wrapper for ReNA++
cluster4d_slic(): Cluster4d using SLIC method
cluster4d_slice_msf(): Cluster4d using slice_msf method
cluster4d_snic(): Cluster4d using SNIC method
cluster4d_supervoxels(): Cluster4d using supervoxels method
cluster_metrics(): Explicit cluster-quality estimands
clustering_accuracy(): Compute Clustering Accuracy Metrics
commute_cluster(): Commute-time clustering on a physical k-nearest-neighbor graph
compare_cluster4d(): Compare multiple cluster4d results
compress_features_svd(): Compress High-Dimensional Features via Randomized SVD
compute_centroids(): Compute Centroids of Clusters
compute_functional_gradient(): Compute Functional Gradient from Feature Matrix
detrend_basis_cpp(): Flexible Detrending with Basis Functions (Fast C++ Implementation)
detrend_dct_cpp(): DCT Detrending (Convenience Function)
detrend_poly_cpp(): Polynomial Detrending (Convenience Function)
detrend_time(): Detrend Voxel Timeseries
detrend_time_cpp(): Detrend Voxel Timeseries (Fast C++ Implementation)
er_ml_rena(): Backwards-compatible alias for rena_plus
estimate_optimal_components(): Estimate Optimal Number of SVD Components
find_gradient_seeds_g3s(): Find G3S Seeds via Functional Gradient Minima
find_initial_points(): Find Initial Cluster Centers for Supervoxel Algorithm (Deprecated)
generate_synthetic_volume(): Generate a synthetic 4D neuroimaging volume with labeled clusters
is.cl_partition(<cluster_result>): Test if Object is a Partition
knn_shrink(): K-nearest-neighbor shrink
make_block_synthetic(): Make a tiny block-structured synthetic volume
make_dct_basis(): Generate DCT-II Basis Matrix
make_poly_basis(): Generate Polynomial Basis Matrix
make_synthetic_clusters(): Create Synthetic Ground Truth Clustering Data
merge_clus(): Merge Clustering Results Using a Consensus Clustering Algorithm
merge_clus(<cluster_result>): Merge Clustering Results for ClusteredNeuroVol Objects
meta_clust(): Meta Clustering for Cluster Results (S4 Generic)
meta_clust(<cluster_result>): Meta Clustering for Cluster Results
neurocluster_simd_info(): SIMD / CPU Feature Report (Development Helper)
normalize_detrend(): Normalize and Detrend Neuroimaging Data
normalize_detrend_cpp(): Normalize Volumes and Detrend Timeseries (Fast C++ Implementation)
normalize_volumes(): Normalize Volumes by Removing Mean Offset
normalize_volumes_cpp(): Normalize Volumes by Removing Mean Offset (Fast C++ Implementation)
plot(<cluster4d_result>): Plot cluster4d result
print(<cluster4d_result>): S3 Methods for cluster4d_result Objects
print(<g3s_result>): Print Method for G3S Results
randomize_cluster_ids(): Randomize Cluster IDs
rena(): Recursive Nearest Agglomeration (ReNA) Clustering
rena_plus(): ReNA++: Edge-aware Reciprocal Multi-Level ReNA with Ward refinement
slic4d_grad_summary(): Visualize gradient used for seed relocation
slic4d_supervoxels(): Fast 4D SLIC supervoxels (mask-aware, gradient relocation, preserve-K)
slice_msf(): Volumetric DCT minimum-spanning-forest clustering
slice_msf_consensus(): Fuse Slice-MSF runs on a masked graph
slice_msf_single(): Run one Slice-MSF segmentation
snic(): SNIC: Simple Non-Iterative Clustering
spatial_gradient(): Spatial Gradient Calculation
suggest_cluster4d_params(): Suggest cluster4d parameters based on data characteristics
summary(<cluster4d_result>): Summarize cluster4d result
supervoxel_cluster_surface(): Supervoxel Clustering on a Surface
supervoxel_cluster_time(): Supervoxel Clustering in Time
supervoxels(): Supervoxel Clustering (3D volumes)
supervoxels_flash3d(): FLASH-3D: Fast 3D Superclustering for fMRI
synthetic_time_patterns(): Generate synthetic temporal patterns for cluster signals
tesselate(): Tesselate a Mask Volume into K Clusters using K-means
test_all_clustering_methods(): Test All Clustering Methods on Synthetic Data
test_clustering_method(): Test Clustering Method on Synthetic Data
transform_new_data_svd(): Transform New Data Using Existing SVD Compression
validate_cluster4d(): Validate cluster4d result