Package index • manifoldalign

All functions

Zt_times(): Apply Z^T to arbitrary multi-vectors without forming Z.
align_many(): Align multiple datasets by synchronizing pairwise transforms
aligner_capabilities(): Capabilities for an aligner
alignment_benchmark: Benchmark Data for Alignment Methods
alignment_quality(): Alignment Quality Metrics
apply_transform(): Apply a transform to data
as.data.frame(<pseudolabels>): Convert pseudolabels to data.frame
as_hyperdesign(): Construct a lightweight hyperdesign from matrices and labels
assess_ssma(): Assess SSMA Alignment Quality on Synthetic Paired Domains
assign_pseudolabels(): Assign pseudolabels based on sparse similarity matrix clustering
benchmark_graph_alignment_methods(): Benchmark graph alignment methods on synthetic permuted graphs
benchmark_parrot_scalability(): Benchmark PARROT Scalability
benchmark_token_ot_graph_align_scalability(): Benchmark token_ot_graph_align scalability on synthetic permuted graphs
block_indices(): Block indices for data list
choose_sigma(): Choose optimal sigma for RBF kernel
coef(<fpgw>): Extract coefficients from FPGW
compare_parrot_baselines(): Compare PARROT with Baseline Methods
compose_transform(): Compose two transforms (a->b and b->c)
compute_anchor_gradient_cpp(): Compute anchor prior gradient
compute_diffusion_coordinates(): Compute diffusion coordinates from a spectral basis
compute_diffusion_coordinates_from_adjacency(): Convenience: compute diffusion coordinates from an adjacency matrix
compute_edge_distances_cpp(): Compute pairwise squared distances for connected nodes
compute_edge_gradient_cpp(): Compute edge consistency gradient
compute_graph_laplacian(): Compute a (normalized) graph Laplacian from an adjacency matrix
compute_hks_descriptors(): Compute Heat Kernel Signatures (HKS) from a spectral basis
compute_hks_from_adjacency(): Convenience: compute HKS descriptors from an adjacency matrix
compute_laplacian_basis(): Compute a Laplacian spectral basis (eigenpairs) from an adjacency matrix
compute_neighborhood_gradient_cpp(): Compute neighborhood consistency gradient
compute_parrot_cost_cpp(): Compute PARROT cost matrix
compute_rwr_vectorized_cpp(): Vectorized RWR computation
compute_squared_distances_cpp(): Compute squared Euclidean distance matrix
cone_align(): CONE-Align: Consensus Optimization for Node Embedding Alignment
cone_align_multiple(): Multiple Graph CONE-Align: Consensus Optimization for Multi-Domain Node Embedding Alignment
cone_multi_aligner(): CONE-Align multiple aligner (native multi-view)
coskern(): Cosine kernel function
coupled_diagonalization(): Coupled Diagonalization for Multi-Modal Alignment
createSimFun(): Create a similarity function from a label similarity matrix
create_synthetic_similarity_matrix(): Create synthetic similarity matrix for testing pseudolabeling
cv_alignment_rows(): Cross-Validated Row-Wise Alignment Scoring
evaluate_parrot_accuracy(): Evaluate PARROT Alignment Accuracy
evaluate_pseudolabeling(): Evaluate pseudolabeling performance against ground truth
fit_many(): Native multi-domain entry point (optional)
fit_pair(): Fit a pairwise alignment model
fit_pair(<grasp_aligner>): Fit GRASP on a pair of domains
fit_pair(<ot_procrustes_aligner>): Fit OT-Procrustes on a pair of domains
fit_pair(<parrot_aligner>): Fit PARROT on a pair of domains
fit_pair(<token_ot_graph_aligner>): Fit Token-OT Graph alignment on a pair of domains
fitted(<fpgw>): Extract fitted values
fpgw-methods: Additional methods for FPGW objects
fpgw(): Fused-Partial Gromov-Wasserstein Distance
generalized_procrustes(): Generalized Orthogonal Procrustes Alignment
generate_parrot_validation_data(): Generate Synthetic Network Alignment Data
generate_spiral_validation_data(): Generate Synthetic Two-Domain Spiral Data
get_parrot_use_cpp(): Get whether to use Rcpp implementations in PARROT
global_geo_align(): Global-Geometry Multi-Dataset Alignment
global_geo_align_control(): Control settings for `global_geo_align.hyperdesign()`
global_geo_aligner(): Global geometry aligner (native multi-view)
gpa_aligner(): Generalized Procrustes aligner (native multi-view)
gpca_align(): Generalized PCA Alignment
gpca_align(<hyperdesign>): Generalized PCA Alignment for Hyperdesign Data
gpca_align_control(): Control settings for `gpca_align.hyperdesign()`
gram_Ld(): Build Gram for between-class Laplacian using label factors.
gram_Ls(): Build Gram for same-class Laplacian using label factors.
gram_ZZ(): Compute Z^T Z without forming Z explicitly.
gram_Z_A_Z(): Generic helper for Z^T A Z using an operator.
gram_Z_diag_Z(): Fast path for diagonal A in Z^T A Z.
grasp(): Graph Alignment by Spectral Corresponding Functions (GRASP)
grasp_aligner(): GRASP adapter for align_many()
grasp_multiset(): Multi-Graph GRASP: Graph Alignment by Spectral Corresponding Functions
gromov_wasserstein(): Gromov-Wasserstein Distance
high_sim_pseudolabels(): High-similarity pseudolabels from multi-domain data
hks_time_grid(): Construct a diffusion-time grid for HKS/diffusion descriptors
invert_transform(): Invert a transform
kema(): Kernel Manifold Alignment (KEMA)
kema_aligner(): Multi-view aligner adapters (native delegates)
kema_orig(): Original Kernel Manifold Alignment (KEMA)
latent_dim(): Latent dimension selected by the method (if applicable)
linear_sim_embed(): Linear Similarity Embedding using Optimal Transport
lowrank_align(): Low-rank Alignment
make_Zop_from_Ks(): Build matrix-free operator for block-diagonal kernel matrix constructed from pre-computed kernel blocks.
mma_align_multiple(): Multiset Manifold Alignment (MMA)
mma_align_multiple(<hyperdesign>): Multiset Manifold Alignment (MMA)
multiscale_manifold_align(): Multiscale manifold alignment (Wang-Mahadevan style)
multiscale_manifold_align_control(): Control settings for multiscale manifold alignment backends
multiset_uot_align(): Multi-subject UOT alignment to a shared template
multiset_uot_map(): Map multiple subjects into template space from a multi-set fit
new_align_transform(): Construct an alignment transform object
new_aligner(): Generic alignment adapter interface and transform helpers
oos_predict(): Out-of-sample projection/prediction
ot_procrustes_aligner(): OT + Procrustes aligner adapter for align_many()
pair_loss(): Pairwise loss (for weighting/diagnostics)
parrot(): Position-Aware Random Transport (PARROT) Network Alignment
parrot_aligner(): PARROT adapter for align_many()
pivoted_chol_kernel_block(): Simple pivoted Cholesky kernel rank selection.
plot(<fpgw>): Plot method for FPGW
plot_cycle_consistency(): Plot cycle-consistency residuals as a heatmap
plot_edge_residuals_heatmap(): Plot edge residuals as a heatmap
predict(<fpgw>): Predict method for FPGW
predict(<gromov_wasserstein>): Predict method for Gromov-Wasserstein
predict(<simembed>): Predict Method for Similarity Embedding
print(<fpgw>): Print Method for FPGW Objects
print(<pseudolabels>): Print method for pseudolabels objects
print(<simembed>): Print Method for Similarity Embedding
profile_parrot_memory(): Profile PARROT Memory Usage
pseudolabeling: Pseudolabeling for Unsupervised Domain Adaptation
relative_transform(): Extract a relative transform from a pairwise fit
residuals(<fpgw>): Extract residuals (not applicable for FPGW)
rotation_sync(): Rotation synchronization for orthogonal group O(k)
run_kema_validation_suite(): Comprehensive KEMA Numerical Validation
run_parrot_validation_suite(): Run PARROT Validation Suite
sinkhorn_unified(): Unified Sinkhorn algorithm with optional log-domain stabilization
sinkhorn_unified_potentials(): Unified Sinkhorn algorithm returning dual potentials for warm-starting
solve_sylvester_rwr_cpp(): Solve Sylvester equation for RWR cost
spectral_mnn_align(): Spectral MNN Alignment
spectral_mnn_align_control(): Control settings for `spectral_mnn_align()`
spectral_mnn_aligner(): Spectral MNN aligner (native multi-view)
ssma_align(): Semi-Supervised Manifold Alignment (SSMA)
ssma_align_control(): Control settings for `ssma_align()`
ssma_aligner(): SSMA aligner (native multi-view)
ssma_serial_control(): Control settings for serial-correlation handling in SSMA graph construction
structure_descriptors: Structural graph descriptors (HKS, diffusion coordinates)
summarize_bad_edges(): Summarize the worst edges by residual
summary(<fpgw>): Summary method for FPGW
summary(<pseudolabels>): Summary method for pseudolabels objects
token_ot_graph_align(): Token-level Optimal Transport Graph Alignment
token_ot_graph_align_control(): Control settings for `token_ot_graph_align()`
token_ot_graph_aligner(): Token-OT graph aligner adapter for align_many()
transform(<fpgw>): Transform new data using FPGW alignment
uot_apply_map(): Apply a UOT coupling to map signals into template space
uot_apply_map_dense_mat_cpp(): Apply UOT coupling as a barycentric map (dense, matrix signal)
uot_apply_map_dense_vec_cpp(): Apply UOT coupling as a barycentric map (dense, vector signal)
uot_apply_map_sparse_mat_cpp(): Apply UOT coupling as a barycentric map (sparse, matrix signal via CSC)
uot_apply_map_sparse_vec_cpp(): Apply UOT coupling as a barycentric map (sparse, vector signal)
uot_build_cost(): Build dense or sparse costs for multi-set UOT alignment
uot_build_sparse_cost_knn_cpp(): Build a sparse cost graph from neighbour indices/distances (CSR+CSC)
uot_extract_coupling(): Extract a sparse coupling or mapping operator (dgCMatrix)
uot_extract_coupling_sparse_csc_cpp(): Extract a sparse coupling / map operator from UOT dual potentials (CSC)
uot_fit_pair(): Fit a pairwise UOT alignment to a fixed template support
uot_kl_logpi2_meanF_dense_cpp(): Compute log second marginal and transported feature means (dense)
uot_kl_logpi2_meanF_sparse_cpp(): Compute log second marginal and transported feature means (sparse CSR)
uot_map(): Map signals into template space using a `uot_pair_fit`
uot_operator(): Extract a reusable map/coupling operator from a `uot_pair_fit`
uot_ti_sinkhorn_kl(): Translation-invariant unbalanced OT (KL) via TI-Sinkhorn
uot_ti_sinkhorn_kl_dense_cpp(): KL translation-invariant Sinkhorn (dense cost)
uot_ti_sinkhorn_kl_sparse_cpp(): KL translation-invariant Sinkhorn (sparse kNN cost)
uot_ti_sinkhorn_kl_sparse_csr_csc_cpp(): KL translation-invariant Sinkhorn (sparse CSR+CSC cost)
validate_kema_eigenvalues(): Validate KEMA Eigenvalues Against Paper Specifications
validate_out_of_sample_reconstruction(): Test Out-of-Sample Reconstruction Accuracy