Network Data Operations and Overlapping Partitions Based Methods for Large Networks

Documentation for package ‘netOP’ version 0.1.2

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-- A --

absolute_error_losses	Compute absolute-error losses
adjacency_neighbors	Find adjacent nodes
adjacency_weighted_neighbors	Find adjacent nodes
apply_average_degree_scaling	Apply the selected average-degree scaling method
ase	Compute an adjacency spectral embedding
auc	Compute AUC scores and losses
auc_as_loss	Compute AUC scores and losses
auc_losses	Compute AUC scores and losses

-- B --

binary_deviance_losses	Compute binary deviance losses
bin_dev	Compute binary deviance losses
bin_dev_mean	Compute binary deviance losses

-- C --

clip_probabilities	Clip probability values safely
clip_values	Clip numeric values to an interval
colMeans	Matrix summary generics
colSums	Matrix summary generics
connected_components	Find connected components

-- D --

diag	Matrix summary generics
dkest_tune_regularizer	Tune a degree-regularized spectral model

-- E --

ecv_stability	Edge cross-validation stability selection
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
embedding_estimating	Network embedding, clustering, estimation, and label alignment
estimate_dcbm	Estimate stochastic block models
estimate_dcbm_P_hat	Reconstruct block-model probability matrices
estimate_sbm	Estimate stochastic block models
estimate_sbm_P_hat	Reconstruct block-model probability matrices
extrema_selectors	Compute hard and soft extrema

-- G --

generate_adjacency	Sample a network from edge probabilities
generate_community_labels	Generate or validate community labels
generate_dcbm	Generate block-model networks
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 block-model networks
generate_truncated_normal	Generate truncated standard-normal values
get_generator_parameters	Retrieve parameters attached by a network generator
graph_laplacian	Construct a graph Laplacian

-- H --

hardmax	Compute hard and soft extrema
hardmin	Compute hard and soft extrema

-- I --

Check whether a network matrix is symmetric

-- L --

label_match_brute_force	Match community labels
label_match_greedy	Match community labels
largest_connected_component	Find connected components
lsm_pgd	Fit a latent-space model by projected gradient descent

-- M --

mae	Compute absolute-error losses
matrix-generics	Matrix summary generics
matrix_density	Compute matrix density
mean	Matrix summary generics
measure_peak_ram	Measure peak memory use
modal	Compute the statistical mode
mse	Compute squared-error losses
mult_reg_sonnet	Fit SONNET over multiple regularizers
mult_reg_spectral_cluster	Fit spectral clusterings over multiple regularizers

-- N --

ncv_stability	Node cross-validation stability selection
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
network_generators	Network input, generation, and probability calibration
nmi	Compute normalized mutual information
normalize_lsm_positions	Normalize latent-space positions

-- O --

oracle_plotter	Compare clustering methods with known labels
outer_add	Add vectors by outer expansion

-- P --

pair_hamming_loss	Compare pairs of clusterings
pair_nmi_loss	Compare pairs of clusterings
plot.netcrop_blockmodel	Select a block model with NETCROP
plot.netcrop_lsm	Select latent-space dimension with NETCROP
plot.netcrop_rdpg	Select RDPG dimension with NETCROP
plot.netcrop_regularizer	Tune a network regularizer with NETCROP
print.netcrop_blockmodel	Select a block model with NETCROP
print.netcrop_lsm	Select latent-space dimension with NETCROP
print.netcrop_rdpg	Select RDPG dimension with NETCROP
print.netcrop_regularizer	Tune a network regularizer with NETCROP
print.sonnet	Fit a SONNET network clustering
print.summary.netcrop_blockmodel	Select a block model with NETCROP
print.summary.netcrop_lsm	Select latent-space dimension with NETCROP
print.summary.netcrop_rdpg	Select RDPG dimension with NETCROP
print.summary.netcrop_regularizer	Tune a network regularizer with NETCROP
print.summary.sonnet	Fit a SONNET network clustering
procrustes	Align point configurations by Procrustes transformation

-- R --

read_network	Read and write network edge lists
rowMeans	Matrix summary generics
rowSums	Matrix summary generics
run_simulations	Run reproducible simulation jobs

-- S --

sae	Compute absolute-error losses
scale_lsm_to_average_degree	Scale latent-space logits to a target average degree
scale_to_average_degree	Scale probabilities to a target average degree
scale_to_average_degree_naive	Scale probabilities to a target average degree
shortest_path_distances	Compute shortest-path distances
sigmoid	Compute the sigmoid transform
singular_decomp	Compute a singular-value decomposition
softmax	Compute hard and soft extrema
softmin	Compute hard and soft extrema
softplus	Compute the softplus transform
sonnet	Fit a SONNET network clustering
sonnet_independent_overlap	Fit SONNET overlap variants
sonnet_param_select	Select SONNET partition parameters
sonnet_shared_overlap	Fit SONNET overlap variants
spectral_cluster	Cluster a network spectrally
squared_error_losses	Compute squared-error losses
sse	Compute squared-error losses
sum	Matrix summary generics
summary.netcrop_blockmodel	Select a block model with NETCROP
summary.netcrop_lsm	Select latent-space dimension with NETCROP
summary.netcrop_rdpg	Select RDPG dimension with NETCROP
summary.netcrop_regularizer	Tune a network regularizer with NETCROP
summary.sonnet	Fit a SONNET network clustering

-- T --

Reconstruct a truncated singular-value approximation

-- U --

uni_mclapply	Apply a function consistently across platforms
usvt	Estimate a matrix with universal singular-value thresholding

-- W --

which_hardmax	Compute hard and soft extrema
which_hardmin	Compute hard and soft extrema
which_softmax	Compute hard and soft extrema
which_softmin	Compute hard and soft extrema
write_network	Read and write network edge lists