adjacency_neighbors                    Find adjacent nodes
adjacency_weighted_neighbors           Find weighted adjacent nodes
apply_average_degree_scaling           Apply average-degree scaling
ase                                    Compute an adjacency spectral embedding
auc                                    Compute an area under the ROC curve
auc_as_loss                            Compute an AUC loss
bin_dev                                Compute summed binary deviance
bin_dev_mean                           Compute mean binary deviance
clip_probabilities                     Clip probability values safely
clip_values                            Clip numeric values to an interval
colMeans                               Compute sparse or dense column means
colSums                                Compute sparse or dense column sums
connected_components                   Find connected components
diag                                   Extract a sparse or dense matrix diagonal
dkest_tune_regularizer                 Tune a degree-regularized spectral model
ecv_stability_blockmodel               Select block-model size with edge cross-validation
ecv_stability_rdpg                     Select RDPG dimension with edge cross-validation
eig_decomp                             Compute an eigendecomposition
estimate_dcbm                          Estimate a degree-corrected block model
estimate_dcbm_P_hat                    Reconstruct DCBM edge probabilities
estimate_sbm                           Estimate a stochastic block model
estimate_sbm_P_hat                     Reconstruct SBM edge probabilities
generate_adjacency                     Sample a network from edge probabilities
generate_community_labels              Generate or validate community labels
generate_dcbm                          Generate a degree-corrected block-model network
generate_degree_parameters             Generate or validate DCBM degree parameters
generate_er                            Generate an Erdos-Renyi network
generate_inverse_beta_degree_parameters Generate inverse-beta degree parameters
generate_latent_positions              Generate or validate latent positions
generate_lsm                           Generate a latent-space-model network
generate_lsm_alpha                     Generate latent-space-model intercepts
generate_lsm_positions                 Generate latent-space-model positions
generate_rdpg                          Generate a random dot product graph
generate_sbm                           Generate a stochastic block-model network
generate_truncated_normal              Generate truncated standard-normal values
get_generator_parameters               Retrieve attached generator parameters
graph_laplacian                        Construct a graph Laplacian
hardmax                                Compute a hard maximum selector
hardmin                                Compute a hard minimum selector
label_match_brute_force                Match labels by exact search
label_match_greedy                     Match labels greedily
largest_connected_component            Extract the largest connected component
lsm_pgd                                Fit a latent-space model by projected gradient descent
mae                                    Compute mean absolute error
matrix_density                         Compute matrix density
mean                                   Compute a sparse or dense matrix mean
measure_peak_ram                       Measure peak memory use
modal                                  Compute the statistical mode
mse                                    Compute mean squared error
mult_reg_sonnet                        Fit SONNET over multiple regularizers
mult_reg_spectral_cluster              Fit spectral clusterings over multiple regularizers
ncv_stability_blockmodel               Select block-model size with node cross-validation
netcrop_blockmodel                     Select a block model with NETCROP
netcrop_lsm                            Select latent-space dimension with NETCROP
netcrop_param_select                   Select NETCROP partition parameters
netcrop_rdpg                           Select RDPG dimension with NETCROP
netcrop_tune_regularizer               Tune a network regularizer with NETCROP
nmi                                    Compute normalized mutual information
normalize_lsm_positions                Normalize latent-space positions
oracle_plotter                         Compare clustering methods with known labels
outer_add                              Add vectors by outer expansion
pair_hamming_loss                      Compute pairwise Hamming disagreement
pair_nmi_loss                          Compute pairwise NMI disagreement
procrustes                             Align point configurations
read_network                           Read a network edge list
rowMeans                               Compute sparse or dense row means
rowSums                                Compute sparse or dense row sums
run_simulations                        Run reproducible simulation jobs
sae                                    Compute summed absolute error
scale_lsm_to_average_degree            Scale LSM logits to an average degree
scale_to_average_degree                Calibrate probabilities to an average degree
scale_to_average_degree_naive          Scale probabilities by a naive multiplier
shortest_path_distances                Compute shortest-path distances
sigmoid                                Compute the sigmoid transform
singular_decomp                        Compute a singular-value decomposition
softmax                                Compute soft maximum weights
softmin                                Compute soft minimum weights
softplus                               Compute the softplus transform
sonnet                                 Fit a SONNET network clustering
sonnet_independent_overlap             Fit independent-overlap SONNET
sonnet_param_select                    Select SONNET partition parameters
sonnet_shared_overlap                  Fit shared-overlap SONNET
spectral_cluster                       Cluster a network spectrally
sse                                    Compute summed squared error
sum                                    Compute a sparse or dense matrix sum
truncated_svd_reconstruct              Reconstruct a truncated SVD approximation
uni_mclapply                           Apply a function consistently across platforms
usvt                                   Apply universal singular-value thresholding
which_hardmax                          Select a hard maximum index
which_hardmin                          Select a hard minimum index
which_softmax                          Select a soft maximum index
which_softmin                          Select a soft minimum index
write_network                          Write a network edge list
