Package 'netOP'

Description

sae() returns the sum of absolute errors; mae() returns their mean.

Usage

sae(x, y, na_rm = FALSE, validate_inputs = TRUE)

mae(x, y, na_rm = FALSE, validate_inputs = TRUE)

Arguments

Value

A nonnegative numeric scalar.

See Also

sse(), mse()

Examples

sae(c(1, 2), c(1, 3))
mae(c(1, 2), c(1, 3))

Description

Builds adjacency lists without densifying sparse inputs. Nonzero finite entries represent edges. adjacency_neighbors() returns node indices; adjacency_weighted_neighbors() also returns edge weights. For undirected reciprocal weighted edges, the smaller nonzero weight is used.

Usage

adjacency_neighbors(A, directed = FALSE, self_loops = c("ignore", "include"))

adjacency_weighted_neighbors(
  A,
  directed = FALSE,
  self_loops = c("ignore", "include")
)

Arguments

Value

adjacency_neighbors() returns one integer neighbor vector per node. adjacency_weighted_neighbors() returns parallel nodes and weights lists.

See Also

connected_components(), shortest_path_distances()

Examples

A <- matrix(c(0, 1, 0, 1, 0, 1, 0, 1, 0), 3, 3)
adjacency_neighbors(A)
adjacency_weighted_neighbors(A)

Description

Dispatches to the calibrated or historical probability-scaling rule.

Usage

apply_average_degree_scaling(
  P,
  average_degree,
  average_degree_method = "naive",
  self_loops,
  lower_clip,
  upper_clip
)

Arguments

Value

A list containing the scaled probability matrix P and multiplier.

See Also

scale_to_average_degree(), scale_lsm_to_average_degree()

Examples

P <- matrix(0.1, 200, 200)
diag(P) <- 0
apply_average_degree_scaling(
  P, average_degree = 5, average_degree_method = "naive",
  self_loops = FALSE, lower_clip = 0, upper_clip = 1
)$multiplier

Description

Embeds a dense or sparse adjacency matrix in d dimensions.

Usage

ase(
  A,
  d,
  directed = FALSE,
  reconstruct = FALSE,
  align_with = NULL,
  ram_check = FALSE
)

Arguments

Details

For an undirected A, the embedding uses the d eigenvalues of largest magnitude and forms $\hat Z = U |\Lambda|^{1/2}$. Signed eigenvalues are retained so reconstruction uses $U \Lambda U^T$. Directed input uses a rank-d SVD to produce left and right embeddings. A supplied align_with rotates both embeddings together, preserving their reconstructed matrix.

Value

An embedding object containing coordinates and decomposition details.

See Also

spectral_cluster(), generate_rdpg(), netcrop_rdpg()

Examples

A <- generate_rdpg(n = 200, d = 3, seed = 8, ncores = 1)
fit <- ase(A, d = 3)
dim(fit$Z_hat)

Description

auc() computes binary ROC AUC using average ranks for tied scores and a compiled implementation when available. auc_as_loss() returns one minus AUC so smaller values are better.

Usage

auc(A, P, use_cpp = TRUE, validate_inputs = TRUE)

auc_as_loss(A, P, use_cpp = TRUE, validate_inputs = TRUE)

Arguments

Value

A numeric scalar containing AUC or one minus AUC.

See Also

bin_dev(), bin_dev_mean()

Examples

auc(c(0, 1, 1), c(0.1, 0.7, 0.8), use_cpp = FALSE)
auc_as_loss(c(0, 1, 1), c(0.1, 0.7, 0.8), use_cpp = FALSE)

Description

bin_dev() returns summed binary cross-entropy after clipping predictions; despite its historical name it does not multiply by two. bin_dev_mean() averages over retained response-probability pairs.

Usage

bin_dev(x, y, epsilon = 1e-05, na_rm = TRUE, validate_inputs = TRUE)

bin_dev_mean(x, y, epsilon = 1e-05, na_rm = TRUE, validate_inputs = TRUE)

Arguments

Value

A nonnegative sum from bin_dev() or mean from bin_dev_mean().

See Also

auc(), clip_probabilities()

Examples

bin_dev(c(0, 1), c(0.1, 0.8))
bin_dev_mean(c(0, 1), c(0.1, 0.8))

Description

Restricts probabilities to ⁠[eps, 1 - eps]⁠ to protect logarithms and inverse-link calculations.

Usage

clip_probabilities(P, eps = 1e-06)

Arguments

Value

An object shaped like P with clipped probabilities.

See Also

clip_values(), bin_dev()

Examples

clip_probabilities(c(0, 0.5, 1), eps = 0.01)

Description

Restricts each value to inclusive bounds while preserving missing values, names, dimensions, and dimension names.

Usage

clip_values(x, lower_clip = -Inf, upper_clip = Inf)

Arguments

Value

An object shaped like x with clipped values.

See Also

clip_probabilities()

Examples

clip_values(c(-1, 0.5, 2), 0, 1)

Description

Finds weak/undirected components: an edge in either direction connects two nodes, and weights do not affect membership. Components and their nodes are returned in breadth-first discovery order. largest_connected_component() selects the first largest component and extracts its induced matrix.

Usage

connected_components(A, self_loops = c("ignore", "include"), use_cpp = TRUE)

largest_connected_component(
  A,
  self_loops = c("ignore", "include"),
  use_cpp = TRUE,
  sort_nodes = TRUE
)

Arguments

Value

connected_components() returns a named list of one-based node vectors. largest_connected_component() returns nodes, size, and the induced submatrix.

See Also

adjacency_neighbors(), shortest_path_distances()

Examples

A <- matrix(c(0, 1, 0, 1, 0, 0, 0, 0, 0), 3, 3)
connected_components(A, use_cpp = FALSE)
largest_connected_component(A, use_cpp = FALSE)

Description

Selects a spectral regularizer using the DKEST eigenspectral-ratio criterion.

Usage

dkest_tune_regularizer(
  A,
  K,
  tau_candidates,
  use_laplacian = TRUE,
  use_dcbm = TRUE,
  dcbm_est_method = c("plugin", "spectral"),
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE),
  seed = NULL,
  verbose = TRUE,
  force_windows = FALSE,
  ram_check = FALSE,
  failure_handling = c("stop", "omit"),
  retain_intermediates = c("all", "minimal")
)

Arguments

Details

Each candidate regularizes the observed matrix, optionally constructs its normalized Laplacian, clusters the fixed-K embedding, estimates SBM or DCBM probabilities, and computes the ratio between selected residual and fitted eigenvalues. Candidate failures are audited explicitly.

Value

A netcrop_regularizer object containing tau_hat, the selected DK statistic, per-candidate numerator, denominator, and ratio, diagnostics, seeds, worker counts, timing, RAM information, and optional raw fits.

See Also

netcrop_tune_regularizer(), mult_reg_spectral_cluster()

Examples

A <- generate_sbm(n = 200, K = 3, alpha = 0.5, beta = 0.1,
                  seed = 35, ncores = 1)
dkest_tune_regularizer(A, K = 3, tau_candidates = c(0, 0.1),
                       ncores = 1, seed = 36, verbose = FALSE)

Description

Selects a block-model community count or RDPG dimension by repeated edge sampling. netOP provides self-contained wrappers around an ECV implementation derived from the CRAN package randnet; randnet is not a package dependency and its ECV-specific helpers remain internal.

References

Li, T., Levina, E., and Zhu, J. (2020). Network cross-validation by edge sampling. Biometrika, 107(2), 257-276. doi:10.1093/biomet/asaa006

Description

Runs repeated edge-sampling cross-validation over every community count from one through max_K using the preserved ECV block-model implementation.

Usage

ecv_stability_blockmodel(
  A,
  max_K,
  train_proportion = 0.9,
  cv = 3L,
  nrep = 20L,
  tau = 0,
  losses = "sse",
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE),
  seed = NULL,
  verbose = TRUE,
  force_windows = FALSE,
  ram_check = FALSE,
  failure_handling = c("stop", "omit"),
  retain_intermediates = c("all", "minimal")
)

Arguments

Details

The wrapper validates binary undirected loop-free input, candidate and sampling feasibility, dependencies, seeds, workers, and losses. Repetitions run through uni_mclapply() while folds within each repetition remain sequential. Raw ECV results are audited and converted to the shared tidy loss schema; failed repetitions either stop or are explicitly omitted.

Value

A netcrop_blockmodel object with algorithm = "ECV", tidy losses, selections, candidate metadata, realized seeds, failures, worker counts, timing, RAM diagnostics, and optional raw output.

References

Li, T., Levina, E., and Zhu, J. (2020). Network cross-validation by edge sampling. Biometrika, 107(2), 257-276. doi:10.1093/biomet/asaa006

See Also

ecv_stability_rdpg(), ncv_stability_blockmodel(), netcrop_blockmodel()

Examples

A <- generate_sbm(n = 200, K = 3, alpha = 0.5, beta = 0.1,
                  seed = 6, ncores = 1)
ecv_stability_blockmodel(A, max_K = 5, cv = 2, nrep = 1,
                         ncores = 1, seed = 7, verbose = FALSE)

Description

Runs repeated edge-sampling cross-validation over every symmetric-RDPG dimension from one through max_d.

Usage

ecv_stability_rdpg(
  A,
  max_d,
  cv = 3L,
  nrep = 1L,
  train_proportion = 0.9,
  losses = c("mse", "bin_dev", "auc_as_loss"),
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE),
  seed = NULL,
  verbose = TRUE,
  force_windows = FALSE,
  ram_check = FALSE,
  failure_handling = c("stop", "omit"),
  retain_intermediates = c("all", "minimal")
)

Arguments

Details

The preserved RDPG ECV reconstruction routines run inside an isolated internal environment. The wrapper validates graph and holdout feasibility, supplies namespace-safe dependencies, audits all results, converts them to tidy losses, and retains independently realized repetition seeds.

Value

A netcrop_rdpg object with algorithm = "ECV", tidy loss curves, selections, holdout metadata, realized seeds, failures, worker counts, timings, RAM diagnostics, and optional legacy output.

References

Li, T., Levina, E., and Zhu, J. (2020). Network cross-validation by edge sampling. Biometrika, 107(2), 257-276. doi:10.1093/biomet/asaa006

See Also

ecv_stability_blockmodel(), netcrop_rdpg()

Examples

A <- as.matrix(generate_rdpg(n = 200, d = 3, average_degree = 8,
                             seed = 33, ncores = 1))
ecv_stability_rdpg(A, max_d = 5, cv = 2, nrep = 1,
                   ncores = 1, seed = 34, verbose = FALSE)

Description

Computes selected eigenpairs of a finite symmetric matrix. Partial decomposition falls back to base::eigen() unless force_engine = TRUE. The "irlba" option is accepted for compatibility but redirected because an SVD is not a signed eigendecomposition.

Usage

eig_decomp(
  A,
  d,
  only_values = FALSE,
  scale_by = c("none", "dimension", "sparsity"),
  use_laplacian = FALSE,
  engine = c("rspectra", "base", "irlba"),
  force_engine = FALSE,
  order_by = c("magnitude", "value"),
  safe_d_multiplier = 1,
  validate_inputs = TRUE
)

Arguments

Value

A list with values and, unless only_values, vectors.

See Also

singular_decomp(), ase()

Examples

eig_decomp(diag(c(3, 2, 1)), d = 2, engine = "base")

Description

Spectral and latent-space embedders, SBM/DCBM probability estimators, and deterministic or exact label-alignment helpers.

Description

Estimates SBM or DCBM parameters from dense, sparse, full, or supported NCV adjacency layouts.

Usage

estimate_sbm(
  A,
  g,
  K = max(g),
  fold_nodes = NULL,
  directed = FALSE,
  self_loops = FALSE,
  validate_inputs = TRUE,
  stabilizer = 0.01
)

estimate_dcbm(
  A,
  g,
  K = max(g),
  method = c("plugin", "spectral"),
  fold_nodes = NULL,
  row_norm = NULL,
  psi_omit = 0L,
  stabilizer = 0.01,
  spectral_engine = c("rspectra", "irlba", "base"),
  spectral_options = list(),
  validate_inputs = TRUE
)

Arguments

Details

With fold_nodes = NULL, A is the full network. With fold nodes, A may be the full matrix, non-fold rows by all columns, all rows by fold columns, or non-fold rows by fold columns. The rectangular SBM estimator counts represented dyads, pools both available block orientations for undirected input, and identifies genuine self-pairs from original node indices.

estimate_dcbm() uses either the historical stabilized degree/block-sum plug-in estimator or spectral row norms. Full square networks use eig_decomp(); rectangular NCV layouts use singular_decomp(). row_norm may be a full-network vector, a vector for the columns of A when all row nodes occur there, or a named list with row and col components matching the dimensions of A.

Value

A fitted block-model object.

Functions

• estimate_dcbm(): Estimate degree-corrected block and node parameters.

See Also

estimate_sbm_P_hat(), estimate_dcbm_P_hat(), spectral_cluster()

Examples

A <- generate_sbm(n = 200, K = 3, alpha = 0.4, beta = 0.1,
                  seed = 11, ncores = 1)
g <- get_generator_parameters(A)$g_true
B_hat <- estimate_sbm(A, g, K = 3)
dim(B_hat)

Description

Reconstructs fitted SBM or DCBM edge probabilities.

Usage

estimate_sbm_P_hat(
  A,
  g,
  K = max(g),
  fold_nodes = NULL,
  directed = FALSE,
  self_loops = FALSE,
  lower_clip = 0,
  upper_clip = 1
)

estimate_dcbm_P_hat(
  A,
  g,
  K = max(g),
  method = c("plugin", "spectral"),
  fold_nodes = NULL,
  row_norm = NULL,
  psi_omit = 0L,
  stabilizer = 0.01,
  spectral_engine = c("rspectra", "irlba", "base"),
  spectral_options = list(),
  self_loops = FALSE,
  lower_clip = 0,
  upper_clip = 1
)

Arguments

Details

In NCV mode, estimate_sbm_P_hat() returns the fold-by-fold probability matrix implied by the block estimate; otherwise it returns probabilities for all nodes.

estimate_dcbm_P_hat() computes $\hat P_{ij} = \hat\psi_i \hat\psi_j \hat B_{g_i,g_j}$. In NCV mode, degree parameters are estimated for fold nodes and the result is fold-by-fold. With psi_omit, the result covers retained trailing nodes.

Value

A fitted edge-probability matrix.

Functions

• estimate_dcbm_P_hat(): Reconstruct degree-corrected block-model probabilities.

See Also

estimate_sbm(), estimate_dcbm(), clip_probabilities()

Examples

A <- generate_sbm(n = 200, K = 3, alpha = 0.4, beta = 0.1,
                  seed = 12, ncores = 1)
g <- get_generator_parameters(A)$g_true
P_hat <- estimate_sbm_P_hat(A, g, K = 3)
dim(P_hat)

Description

softmax() and softmin() return normalized exponential weights. hardmax() and hardmin() return zero-one selectors marking all ties. The ⁠which_soft*()⁠ functions sample one index from the soft weights; the ⁠which_hard*()⁠ functions return every tied hard-extremum index.

Usage

softmax(x, temperature = 1, na_rm = FALSE)

softmin(x, temperature = 1, na_rm = FALSE)

hardmax(x, na_rm = FALSE)

hardmin(x, na_rm = FALSE)

which_softmax(x, temperature = 1, na_rm = FALSE)

which_softmin(x, temperature = 1, na_rm = FALSE)

which_hardmax(x, na_rm = FALSE)

which_hardmin(x, na_rm = FALSE)

Arguments

Value

A numeric weight vector or selected index vector, according to the function called.

See Also

softplus(), sigmoid()

Examples

softmax(c(1, 2, 3))
hardmin(c(2, 1, 1))
which_hardmax(c(1, 3, 3))

Description

Samples a dense or sparse adjacency matrix from P. Explicit row-specific seeds make results reproducible across worker counts.

Usage

generate_adjacency(
  P,
  representation = c("sparse", "dense"),
  directed = FALSE,
  self_loops = FALSE,
  seed = NULL,
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE),
  validate_inputs = TRUE,
  n = NULL
)

Arguments

Value

A base matrix or sparse dgCMatrix, according to representation.

See Also

generate_er(), generate_rdpg(), generate_sbm()

Examples

P <- matrix(0.03, 200, 200)
diag(P) <- 0
A <- generate_adjacency(P, seed = 3, ncores = 1)
dim(A)

Description

Produces community memberships for an SBM, DCBM, or latent-space model.

Usage

generate_community_labels(
  n = NULL,
  K = NULL,
  g_true = NULL,
  community_probabilities = NULL,
  seed = NULL
)

Arguments

Value

An integer vector of length n with values from 1 through K.

See Also

generate_sbm(), generate_dcbm(), generate_lsm_positions()

Examples

g <- generate_community_labels(n = 200, K = 3, seed = 5)
table(g)

Description

generate_dcbm() generates a degree-corrected stochastic block model; generate_sbm() is its ordinary-block-model twin with unit degree effects. Both return dense or sparse adjacency matrices.

generate_sbm() fixes every degree parameter to one.

Usage

generate_dcbm(
  n = NULL,
  K = NULL,
  g_true = NULL,
  community_probabilities = NULL,
  P_block = NULL,
  alpha = 0.2,
  beta = 0.2,
  psi = NULL,
  degree_distribution = generate_inverse_beta_degree_parameters,
  degree_args = NULL,
  degree_scale = c("none", "max_by_community", "mean_one_by_community"),
  sparsity_multiplier = 1,
  average_degree = NULL,
  average_degree_method = c("naive", "calibrated"),
  lower_clip = 0,
  upper_clip = 1,
  representation = c("sparse", "dense"),
  directed = FALSE,
  self_loops = FALSE,
  seed = NULL,
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE)
)

generate_sbm(
  n = NULL,
  K = NULL,
  g_true = NULL,
  community_probabilities = NULL,
  P_block = NULL,
  alpha = 0.2,
  beta = 0.2,
  sparsity_multiplier = 1,
  average_degree = NULL,
  average_degree_method = c("naive", "calibrated"),
  lower_clip = 0,
  upper_clip = 1,
  representation = c("sparse", "dense"),
  directed = FALSE,
  self_loops = FALSE,
  seed = NULL,
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE)
)

Arguments

Value

A generated adjacency matrix; see get_generator_parameters().

See Also

generate_community_labels(), estimate_sbm(), estimate_dcbm()

Examples

A <- generate_sbm(n = 200, K = 3, alpha = 0.4, beta = 0.1,
                  seed = 3, ncores = 1)
get_generator_parameters(A)

Description

Returns nonnegative node degree parameters, optionally normalized within communities.

Usage

generate_degree_parameters(
  n = NULL,
  psi = NULL,
  degree_distribution = generate_inverse_beta_degree_parameters,
  degree_args = NULL,
  degree_scale = c("none", "max_by_community", "mean_one_by_community"),
  g_true = NULL,
  seed = NULL
)

Arguments

Value

A nonnegative numeric vector of length n.

See Also

generate_inverse_beta_degree_parameters(), generate_dcbm()

Examples

g <- generate_community_labels(n = 200, K = 3, seed = 6)
psi <- generate_degree_parameters(n = 200, g_true = g, seed = 7)
length(psi)

Description

Generates a dense or sparse Erdos-Renyi adjacency matrix and stores its generating parameters as lightweight metadata.

Usage

generate_er(
  n,
  p = NULL,
  average_degree = NULL,
  representation = c("sparse", "dense"),
  directed = FALSE,
  self_loops = FALSE,
  seed = NULL,
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE)
)

Arguments

Value

A generated adjacency matrix; see get_generator_parameters().

See Also

generate_sbm(), generate_rdpg()

Examples

A <- generate_er(n = 200, average_degree = 5, seed = 1, ncores = 1)
get_generator_parameters(A)

Description

Draws the historical default degree parameters used by the DCBM generator.

Usage

generate_inverse_beta_degree_parameters(n, shape_1 = 4, shape_2 = 1)

Arguments

Value

A nonnegative numeric vector of length n.

See Also

generate_degree_parameters(), generate_dcbm()

Examples

psi <- generate_inverse_beta_degree_parameters(200)
length(psi)

Description

Returns a finite n-by-d latent-position matrix, either supplied by the caller or sampled from latent_distribution.

Usage

generate_latent_positions(
  n = NULL,
  d = NULL,
  Z = NULL,
  latent_distribution = stats::runif,
  latent_args = NULL,
  seed = NULL
)

Arguments

Value

A numeric matrix with n rows and d columns.

See Also

generate_rdpg(), generate_lsm_positions()

Examples

Z <- generate_latent_positions(n = 200, d = 3, seed = 4)
dim(Z)

Description

Generates a dense or sparse Hoff/Ma-style latent-space-model adjacency matrix from supplied or generated positions, memberships, and intercepts.

Usage

generate_lsm(
  n = NULL,
  d = NULL,
  K = 1L,
  g_true = NULL,
  community_probabilities = NULL,
  alpha = NULL,
  Z = NULL,
  Z_left = NULL,
  Z_right = NULL,
  normalize_Z = TRUE,
  distance_adjustment = TRUE,
  mean_lower = -1,
  mean_upper = 1,
  noise_lower = -2,
  noise_upper = 2,
  average_degree = NULL,
  average_degree_method = c("naive", "calibrated"),
  naive_iterations = 10L,
  lower_clip = 0,
  upper_clip = 1,
  representation = c("sparse", "dense"),
  directed = FALSE,
  self_loops = FALSE,
  seed = NULL,
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE)
)

Arguments

Value

A generated adjacency matrix; see get_generator_parameters().

See Also

generate_lsm_positions(), generate_lsm_alpha(), lsm_pgd(), netcrop_lsm()

Examples

A <- generate_lsm(n = 200, d = 3, K = 3, seed = 7, ncores = 1)
get_generator_parameters(A)

Description

Validates or generates the node intercept used by the latent-space model.

Usage

generate_lsm_alpha(n, alpha = NULL, seed = NULL)

Arguments

Value

A finite numeric vector of length n.

See Also

generate_lsm(), generate_lsm_positions()

Examples

alpha <- generate_lsm_alpha(n = 200, seed = 6)
length(alpha)

Description

Generates an n-by-d latent-position matrix from a finite Gaussian mixture with K communities.

Usage

generate_lsm_positions(
  n,
  d,
  K,
  g_true,
  mean_lower = -1,
  mean_upper = 1,
  noise_lower = -2,
  noise_upper = 2,
  seed = NULL
)

Arguments

Value

A numeric matrix with n rows and d columns.

See Also

generate_lsm(), generate_lsm_alpha(), normalize_lsm_positions()

Examples

g <- generate_community_labels(n = 200, K = 3, seed = 4)
Z <- generate_lsm_positions(n = 200, d = 3, K = 3, g_true = g, seed = 5)
dim(Z)

Description

Generates a dense or sparse RDPG adjacency matrix. Undirected models use Z; directed models use Z_left and Z_right.

Usage

generate_rdpg(
  n = NULL,
  d = NULL,
  Z = NULL,
  Z_left = NULL,
  Z_right = NULL,
  latent_distribution = stats::runif,
  latent_args = NULL,
  sparsity_multiplier = 1,
  scale_P = TRUE,
  average_degree = NULL,
  average_degree_method = c("naive", "calibrated"),
  lower_clip = 0,
  upper_clip = 1,
  representation = c("sparse", "dense"),
  directed = FALSE,
  self_loops = FALSE,
  seed = NULL,
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE)
)

Arguments

Value

A generated adjacency matrix; see get_generator_parameters().

See Also

generate_latent_positions(), ase(), netcrop_rdpg()

Examples

A <- generate_rdpg(n = 200, d = 3, seed = 2, ncores = 1)
get_generator_parameters(A)

Description

Draws standard-normal noise on a finite interval by inverse-CDF sampling.

Usage

generate_truncated_normal(n, lower_bound = -2, upper_bound = 2)

Arguments

Value

A numeric vector of length n.

See Also

generate_lsm_positions()

Examples

x <- generate_truncated_normal(200, lower_bound = -2, upper_bound = 2)
range(x)

Description

Returns the compact generating parameters stored on a network produced by a netOP generator. Both dense and sparse generated matrices are supported.

Usage

get_generator_parameters(A)

Arguments

Value

A named list of generating parameters, or NULL when none is attached.

See Also

generate_er(), generate_sbm(), generate_rdpg(), generate_lsm()

Examples

A <- generate_er(n = 200, average_degree = 5, seed = 1, ncores = 1)
get_generator_parameters(A)

Description

Constructs an unnormalized or symmetric normalized graph Laplacian. For positive tau, the adjacency is first regularized by adding tau * mean(degree) / n to every entry; tau = 0 gives the usual matrix.

Usage

graph_laplacian(A, normalized = TRUE, tau = 0)

Arguments

Value

A dense or sparse Laplacian matrix.

See Also

spectral_cluster(), ase()

Examples

A <- matrix(c(0, 1, 1, 0), 2, 2)
graph_laplacian(A, normalized = FALSE)

Description

Tests symmetry for either a base matrix or a sparse Matrix object without requiring users to attach Matrix.

Usage

is_symmetric_network_matrix(A, tolerance = 1e-10)

Arguments

Value

A single logical value.

See Also

is_symmetric_matrix()

Description

Aligns labels using greedy assignment or exact brute-force matching.

Usage

label_match_greedy(
  match_this,
  standard,
  K = max(c(match_this, standard)),
  algorithm = c("greedy", "hungarian"),
  return_mapping = FALSE
)

label_match_brute_force(
  match_this,
  standard,
  K = max(c(match_this, standard)),
  return_mapping = FALSE,
  confirm_large = NULL
)

Arguments

Details

The greedy function uses exact shortcuts for identical and two-community inputs, followed by historical maximum-overlap assignment. Its "hungarian" option uses a dependency-free cubic-time assignment and maximizes total agreement globally.

label_match_brute_force() generates permutations recursively and scores them one at a time. Runtime remains factorial. For K > 8, an interactive call requires confirmation and noninteractive callers must set confirm_large = TRUE.

Value

A relabeled vector, optionally with mapping diagnostics.

Functions

• label_match_brute_force(): Match labels by enumerating every permutation.

See Also

spectral_cluster(), sonnet()

Examples

labels <- c(2, 2, 3, 3, 1, 1)
standard <- c(1, 1, 2, 2, 3, 3)
label_match_greedy(labels, standard, K = 3)$matched_labels

Description

Estimates latent positions and intercepts for a dense or sparse network.

Usage

lsm_pgd(
  A,
  d,
  step_size = 0.3,
  niter = 100L,
  trace = FALSE,
  Z_init = NULL,
  alpha_init = NULL,
  epsilon = 1e-06,
  use_cpp = TRUE,
  ram_check = FALSE
)

Arguments

Details

Initialization uses USVT, a stable logit transform, the solution of $(n I + 1 1^T)\alpha = \operatorname{rowSums}(\theta)$, and double-centering without constructing a dense centering matrix. The diagonal is excluded from the objective and gradient. Direct initial values may replace either spectral initializer.

Value

A fitted latent-space-model object.

See Also

generate_lsm(), netcrop_lsm()

Examples

A <- generate_lsm(n = 200, d = 3, K = 3, seed = 10, ncores = 1)
fit <- lsm_pgd(A, d = 3, niter = 2, use_cpp = FALSE)
dim(fit$Z_hat)

Description

Returns the fraction of matrix entries that are nonzero. This is the entry density called "sparsity" by the decomposition APIs.

Usage

matrix_density(A)

Arguments

Value

A numeric scalar between zero and one.

See Also

generate_adjacency()

Examples

matrix_density(diag(3))

Description

The functions mean(), diag(), rowMeans(), rowSums(), colMeans(), and colSums() are re-exported from Matrix. The sum() wrapper delegates to base::sum(), which dispatches to Matrix methods for sparse inputs. These functions support common summaries of sparse network matrices.

Usage

sum(..., na.rm = FALSE)

Arguments

Value

sum() and mean() return a numeric scalar. diag() returns the diagonal vector of a matrix. rowMeans() and rowSums() return one value per row; colMeans() and colSums() return one per column. See the corresponding Matrix help for other supported inputs.

Examples

A <- Matrix::Matrix(matrix(c(0, 1, 1, 0), nrow = 2), sparse = TRUE)
sum(A)
mean(A)
diag(A)
rowSums(A)
colMeans(A)

Description

Runs an expression or function through peakRAM::peakRAM() while retaining both the evaluated result and the measurement table. The process is evaluated exactly once.

Usage

measure_peak_ram(process, ...)

Arguments

Value

A list containing result, the process value, and metrics, a one-row data frame with elapsed seconds, total RAM used in MiB, and peak RAM used in MiB.

See Also

available_ram(), report_ram_preflight()

Examples

if (requireNamespace("peakRAM", quietly = TRUE)) {
  measure_peak_ram(sum, 1:10)
}

Description

Returns the most frequent value in x, using the first value to break ties. If na_rm = FALSE, NA is a possible modal value; if removal leaves no observations, a typed missing value is returned.

Usage

modal(x, na_rm = FALSE)

Arguments

Value

A scalar with the same basic type as x.

See Also

nmi()

Examples

modal(c(1, 2, 2, 3))

Description

Fits SONNET across a requested regularization grid for one network or a matched list.

Usage

mult_reg_sonnet(
  A,
  K,
  tau_candidates,
  num_subnetworks = NULL,
  overlap_size = NULL,
  extra_nrep = 0L,
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE),
  seed = NULL,
  matching_method = c("greedy", "hungarian", "brute_force"),
  confirm_large = TRUE,
  verbose = TRUE,
  force_windows = FALSE,
  ram_check = FALSE,
  share_overlap = FALSE,
  parameter_select_options = list(),
  failure_handling = c("stop", "omit"),
  retain_fits = TRUE,
  ...
)

Arguments

Details

Candidate fits run sequentially so ncores remains available to SONNET's internal subnetwork computations. The same realized seed is reused across regularization candidates within each network.

Value

A mult_reg_clustering object containing SONNET labels and optional fits by network and regularizer, parameters, tasks, failures, seeds, and timing.

See Also

sonnet(), mult_reg_spectral_cluster()

Examples

A <- generate_sbm(n = 200, K = 3, alpha = 0.5, beta = 0.1,
                  seed = 39, ncores = 1)
mult_reg_sonnet(
  A, K = 3, tau_candidates = c(0, 0.1),
  num_subnetworks = 2, overlap_size = 20, ncores = 1,
  seed = 40, verbose = FALSE, spectral_engine = "base"
)

Description

Fits spectral clustering across a requested regularization grid for one network or a matched list of networks.

Usage

mult_reg_spectral_cluster(
  A,
  K,
  tau_candidates,
  laplacian = FALSE,
  normalize_laplacian = TRUE,
  handle_zero_degree_nodes = c("none", "random_label", "remove"),
  row_normalize = FALSE,
  spectral_method = c("eigen", "svd"),
  spectral_engine = c("RSpectra", "irlba", "base"),
  spectral_options = list(),
  cluster_engine = c("clara", "kmeans", "pam"),
  cluster_options = list(),
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE),
  seed = NULL,
  verbose = TRUE,
  force_windows = FALSE,
  ram_check = FALSE,
  failure_handling = c("stop", "omit"),
  retain_fits = TRUE
)

Arguments

Details

Network-by-candidate tasks are parallelized. The same realized seed is used across candidates within a network so stochastic clustering comparisons are fair. Results can retain complete fitted objects or labels only.

Value

A mult_reg_clustering object containing labels and optional fits by network and candidate, resolved parameters, task metadata, failures, seeds, workers, timing, and RAM diagnostics.

See Also

dkest_tune_regularizer(), netcrop_tune_regularizer()

Examples

A <- generate_sbm(n = 200, K = 3, alpha = 0.5, beta = 0.1,
                  seed = 37, ncores = 1)
fits <- mult_reg_spectral_cluster(
  A, K = 3, tau_candidates = c(0, 0.1), ncores = 1,
  seed = 38, verbose = FALSE, spectral_engine = "base",
  cluster_engine = "kmeans"
)

Description

Repeated node cross-validation for selecting the number of block-model communities. This is the netOP authors' implementation of Chen and Lei (2018), with numerical-stability checks and explicit failure handling.

References

Chen, K. and Lei, J. (2018). Network Cross-Validation for Determining the Number of Communities in Network Data. Journal of the American Statistical Association, 113(521), 241-251. doi:10.1080/01621459.2016.1246365

Description

Repeats node cross-validation over every community count from one through max_K for both SBM and DCBM candidates.

Usage

ncv_stability_blockmodel(
  A,
  max_K,
  cv = 3L,
  nrep = 20L,
  dc_est = c("spectral", "plugin"),
  tau = 0,
  use_laplacian = FALSE,
  losses = "sse",
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE),
  seed = NULL,
  verbose = TRUE,
  force_windows = FALSE,
  ram_check = FALSE,
  failure_handling = c("stop", "omit"),
  retain_intermediates = c("all", "minimal")
)

Arguments

Details

Each repetition partitions nodes into folds, fits candidates on rectangular training blocks, and evaluates held-out dyads. Repetitions are parallelized, fold stages remain sequential, and probabilities are clipped consistently for logarithmic losses. Failed repetitions can stop or be omitted.

Value

A netcrop_blockmodel object with algorithm = "NCV", tidy fold and repetition losses, selections, fold metadata, realized seeds, failures, worker counts, timing, and optional raw output.

References

Chen, K. and Lei, J. (2018). Network Cross-Validation for Determining the Number of Communities in Network Data. Journal of the American Statistical Association, 113(521), 241-251. doi:10.1080/01621459.2016.1246365

See Also

ecv_stability_blockmodel(), netcrop_blockmodel()

Examples

A <- generate_sbm(n = 200, K = 3, alpha = 0.55, beta = 0.08,
                  seed = 24, ncores = 1)
ncv_stability_blockmodel(A, max_K = 5, cv = 2, nrep = 1,
                         ncores = 1, seed = 26, verbose = FALSE)

Description

Selects the number of communities and either the stochastic block model (SBM), degree-corrected block model (DCBM), or both by overlapping-subnetwork cross-validation. The procedure obtains spectral labels on each subnetwork, estimates block-model parameters, aligns labels through the common overlap, and evaluates predictions between every pair of non-overlap pieces.

Usage

netcrop_blockmodel(
  A,
  K_candidates,
  num_subnetworks = NULL,
  overlap_size = NULL,
  nrep = 1L,
  losses = "sse",
  model_candidates = c("SBM", "DCBM"),
  sbm_est_options = list(),
  dcbm_est_options = list(),
  matching_method = c("greedy", "hungarian", "brute_force"),
  confirm_large = NULL,
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE),
  seed = NULL,
  verbose = TRUE,
  force_windows = FALSE,
  ram_check = FALSE,
  parameter_select_options = list(),
  retain_intermediates = c("all", "minimal"),
  laplacian = FALSE,
  regularize_tau = 0
)

## S3 method for class 'netcrop_blockmodel'
print(x, ...)

## S3 method for class 'netcrop_blockmodel'
summary(object, ...)

## S3 method for class 'summary.netcrop_blockmodel'
print(x, ...)

## S3 method for class 'netcrop_blockmodel'
plot(x, aggregate = TRUE, ...)

Arguments

Value

netcrop_blockmodel() returns an object of class netcrop_blockmodel containing candidate-wise cross-validation losses, repetition and overall selections, split and alignment diagnostics, retained intermediates, resource information, and timing. summary.netcrop_blockmodel() returns a compact summary object; print methods return their input invisibly, and the plot method returns a ggplot object.

See Also

ecv_stability_blockmodel(), ncv_stability_blockmodel()

Examples

A <- generate_sbm(n = 200, K = 3, alpha = 0.5, beta = 0.1,
                  seed = 5, ncores = 1)
fit <- netcrop_blockmodel(
  A, K_candidates = 1:5, num_subnetworks = 2, overlap_size = 30,
  model_candidates = "SBM", ncores = 1, seed = 6, verbose = FALSE
)
fit
summary(fit)

Description

Selects a latent-space-model (LSM) dimension by overlapping-subnetwork cross-validation. The procedure fits every candidate on every subnetwork, exactly reparameterizes each fitted inner-product model as a squared-distance model, rigidly aligns fits through the overlap, and evaluates every unordered pair of non-overlap pieces.

Usage

netcrop_lsm(
  A,
  d_candidates,
  num_subnetworks = NULL,
  overlap_size = NULL,
  nrep = 1L,
  losses = "sse",
  lsm_options = list(),
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE),
  seed = NULL,
  verbose = TRUE,
  force_windows = FALSE,
  ram_check = FALSE,
  parameter_select_options = list(),
  retain_intermediates = c("all", "minimal")
)

## S3 method for class 'netcrop_lsm'
print(x, ...)

## S3 method for class 'netcrop_lsm'
summary(object, ...)

## S3 method for class 'summary.netcrop_lsm'
print(x, ...)

## S3 method for class 'netcrop_lsm'
plot(x, aggregate = TRUE, ...)

Arguments

Value

netcrop_lsm() returns an object of class netcrop_lsm containing candidate-wise cross-validation losses, repetition and overall dimension selections, split diagnostics, fitted LSM options, retained intermediates, resource information, and timing. summary.netcrop_lsm() returns a compact summary object; print methods return their input invisibly, and the plot method returns a ggplot object.

See Also

generate_lsm(), lsm_pgd()

Examples

A <- generate_lsm(n = 200, d = 3, K = 3, seed = 9, ncores = 1)
fit <- netcrop_lsm(
  A, d_candidates = 1:5, num_subnetworks = 2, overlap_size = 30,
  ncores = 1, seed = 10, verbose = FALSE,
  lsm_options = list(niter = 20)
)
fit
summary(fit)

Description

Converts a requested test proportion and one or more overlap-range positions into NETCROP subnetwork counts and overlap proportions. An o_range value of exactly one is replaced by 0.8 to avoid the singular upper endpoint. When n is supplied, remainder nodes augment the overlap so the remaining nodes divide evenly among the selected subnetworks.

Usage

netcrop_param_select(test_prop = 0.02, n = NULL, o_range = c(0, 0.8))

Arguments

Value

A named list describing candidate subnetwork counts and overlap proportions. When n is supplied, it also contains integer overlap and piece sizes, remainder adjustments, and realized test proportions.

See Also

netcrop_blockmodel(), netcrop_rdpg(), netcrop_lsm()

Examples

netcrop_param_select(test_prop = 0.05, n = 200, o_range = c(0, 0.8))

Description

Selects a symmetric random-dot-product-graph (RDPG) dimension by overlapping-subnetwork cross-validation. The procedure decomposes every subnetwork, constructs each candidate embedding, aligns non-reference embeddings through the overlap, and evaluates every unordered pair of non-overlap pieces.

Usage

netcrop_rdpg(
  A,
  d_candidates,
  num_subnetworks = NULL,
  overlap_size = NULL,
  nrep = 1L,
  losses = "sse",
  eig_options = list(),
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE),
  seed = NULL,
  verbose = TRUE,
  force_windows = FALSE,
  ram_check = FALSE,
  parameter_select_options = list(),
  retain_intermediates = c("all", "minimal")
)

## S3 method for class 'netcrop_rdpg'
print(x, ...)

## S3 method for class 'netcrop_rdpg'
summary(object, ...)

## S3 method for class 'summary.netcrop_rdpg'
print(x, ...)

## S3 method for class 'netcrop_rdpg'
plot(x, aggregate = TRUE, ...)

Arguments

Value

netcrop_rdpg() returns an object of class netcrop_rdpg containing candidate-wise cross-validation losses, repetition and overall dimension selections, split and eigenvalue diagnostics, retained intermediates, resource information, and timing. summary.netcrop_rdpg() returns a compact summary object; print methods return their input invisibly, and the plot method returns a ggplot object.

See Also

ecv_stability_rdpg(), ase()

Examples

A <- generate_rdpg(n = 200, d = 3, seed = 7, ncores = 1)
fit <- netcrop_rdpg(
  A, d_candidates = 1:5, num_subnetworks = 2, overlap_size = 30,
  ncores = 1, seed = 8, verbose = FALSE
)
fit
summary(fit)

Description

Selects a spectral regularization parameter by overlapping-subnetwork cross-validation for an SBM or DCBM.

Usage

netcrop_tune_regularizer(
  A,
  K,
  tau_candidates,
  use_dcbm = FALSE,
  num_subnetworks = NULL,
  overlap_size = NULL,
  nrep = 1L,
  use_laplacian = FALSE,
  dcbm_est_method = c("plugin", "spectral"),
  losses = "sse",
  loss_types = NULL,
  label_reference = c("full_network", "leave_pair_out"),
  spectral_options = list(),
  cluster_engine = c("clara", "kmeans", "pam"),
  cluster_options = list(),
  estimator_options = list(),
  matching_method = c("greedy", "hungarian", "brute_force"),
  confirm_large = NULL,
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE),
  seed = NULL,
  verbose = TRUE,
  force_windows = FALSE,
  ram_check = FALSE,
  parameter_select_options = list(),
  retain_intermediates = c("all", "minimal")
)

## S3 method for class 'netcrop_regularizer'
print(x, ...)

## S3 method for class 'netcrop_regularizer'
summary(object, ...)

## S3 method for class 'summary.netcrop_regularizer'
print(x, ...)

## S3 method for class 'netcrop_regularizer'
plot(x, aggregate = TRUE, ...)

Arguments

Details

The function clusters overlapping subnetworks for every candidate, aligns their labels, estimates block-model probabilities, and evaluates held-out edges between non-overlap pieces. Missing partition parameters are selected with netcrop_param_select(). K = 1 returns invisibly because no clustering-based regularizer selection is required. The spectral and clustering defaults match netcrop_blockmodel(): eigenvectors from RSpectra, CLARA clustering, Euclidean distance for SBM, Manhattan distance for DCBM, and row normalization for DCBM. User option lists override the corresponding defaults without changing tuner-controlled inputs.

Value

A netcrop_regularizer object containing candidate loss curves, per-repetition and overall selections, resolved options, partition and matching diagnostics, worker counts, timings, and optional intermediates.

See Also

dkest_tune_regularizer(), mult_reg_spectral_cluster()

Examples

A <- generate_sbm(n = 200, K = 3, alpha = 0.5, beta = 0.1,
                  seed = 31, ncores = 1)
fit <- netcrop_tune_regularizer(
  A, K = 3, tau_candidates = c(0, 0.1),
  num_subnetworks = 2, overlap_size = 20,
  nrep = 1, ncores = 1, seed = 32, verbose = FALSE
)
fit

Description

Read and write edge lists and generate ER, SBM, DCBM, RDPG, and latent-space networks. Generators return an adjacency matrix with compact truth metadata available through get_generator_parameters().

Description

Computes empirical mutual information divided by joint entropy. This normalization differs from definitions based on marginal entropies. A constant joint labeling has zero entropy and returns NaN.

Usage

nmi(g_1, g_2)

Arguments

Value

A numeric agreement score.

See Also

label_match_greedy(), pair_nmi_loss()

Examples

nmi(c(1, 1, 2, 2), c(2, 2, 1, 1))

Description

Centers and scales a latent-position matrix using the LSM convention.

Usage

normalize_lsm_positions(Z)

Arguments

Value

A centered and normalized matrix with the same dimensions as Z.

See Also

generate_lsm_positions(), generate_lsm()

Examples

Z <- matrix(stats::rnorm(200 * 3), 200, 3)
dim(normalize_lsm_positions(Z))

Description

Evaluates SONNET, spectral clustering, or both against known community labels over candidate regularizers.

Usage

oracle_plotter(
  A,
  g_true,
  K = NULL,
  netcrop_outcomes = NULL,
  dkest_outcomes = NULL,
  include_netcrop_mean = TRUE,
  include_netcrop_mode = TRUE,
  losses = NULL,
  engines = c("sonnet", "spectral_cluster"),
  include_sonnet_tau_zero = TRUE,
  include_spectral_tau_zero = TRUE,
  matching_method = c("greedy", "hungarian", "brute_force"),
  confirm_large = NULL,
  sonnet_options = list(),
  spectral_cluster_options = list(),
  ncores = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE),
  seed = NULL,
  verbose = TRUE,
  force_windows = FALSE,
  ram_check = FALSE
)

Arguments

Details

The oracle candidate grid is obtained from the supplied NETCROP and DKEST outcomes. If their grids differ, the function warns and uses their floating-point-tolerant intersection; an empty intersection is an error. Selected off-grid values are still evaluated. Optional engine-specific tau = 0 baselines do not expand the grid used to define the oracle. Accuracy is one minus the validated label-matching mismatch rate; multiple networks are summarized with means and standard deviations.

Value

Invisibly returns the rendered ggplot. Raw accuracy data, aggregated plotting data, effective fit metadata, and diagnostics are attached as attributes.

See Also

spectral_cluster(), sonnet()

Examples

A <- generate_sbm(n = 200, K = 3, alpha = 0.5, beta = 0.1,
                  seed = 41, ncores = 1)
truth <- get_generator_parameters(A)$g_true
netcrop_fit <- netcrop_tune_regularizer(
  A, K = 3, tau_candidates = c(0, 0.1),
  num_subnetworks = 2, overlap_size = 20,
  nrep = 1, ncores = 1, seed = 42, verbose = FALSE
)
oracle_plotter(
  A, g_true = truth, K = 3,
  netcrop_outcomes = netcrop_fit,
  engines = "spectral_cluster", ncores = 1, seed = 43,
  verbose = FALSE,
  spectral_cluster_options = list(spectral_engine = "base")
)

Description

Forms all pairwise sums, using a registered compiled implementation when requested and available and otherwise base::outer().

Usage

outer_add(x, y, operator = "+", use_cpp = TRUE)

Arguments

Value

A numeric matrix with length(x) rows and length(y) columns.

See Also

procrustes()

Examples

outer_add(1:2, 3:4, use_cpp = FALSE)

Description

Forms the Cartesian-product clustering induced by each pair of community label vectors, then compares the two induced clusterings.

Usage

pair_nmi_loss(g_1, g_2, g_3, g_4)

pair_hamming_loss(g_1, g_2, g_3, g_4)

Arguments

Details

pair_nmi_loss() returns negative normalized mutual information. If both product clusterings contain only one class, it returns -1. pair_hamming_loss() aligns the product labels with the dependency-free Hungarian implementation before returning their mismatch proportion.

Value

pair_nmi_loss() returns a numeric negative-NMI loss. pair_hamming_loss() returns a numeric mismatch proportion.

See Also

nmi(), label_match_greedy()

Examples

pair_nmi_loss(c(1, 1, 2), c(1, 2), c(1, 1, 2), c(1, 2))
pair_hamming_loss(c(1, 1, 2), c(1, 2), c(2, 2, 1), c(2, 1))

Description

Aligns X to X_star by an orthogonal transformation with optional centering and dilation. Inputs may have different column counts; the narrower configuration is padded with zero columns. A compiled translated, non-dilated path is used when requested and available.

Usage

procrustes(
  X,
  X_star,
  translate = FALSE,
  dilate = FALSE,
  sumsq = FALSE,
  use_cpp = TRUE,
  validate_inputs = TRUE
)

Arguments

Value

A list containing aligned X_new and rotation, with optional translation, scale, and sse components.

See Also

ase(), generate_latent_positions()

Examples

X <- matrix(c(0, 0, 1, 0, 0, 1), ncol = 2, byrow = TRUE)
procrustes(X, X, use_cpp = FALSE)$X_new

Description

Executes a simulation function repeatedly with optional outer-loop parallelism, reproducible per-simulation seeds, durable RDS results, resuming, and resource measurements. one_simulation must accept a named simulation argument. Values supplied through ... are forwarded unchanged.

Usage

run_simulations(
  one_simulation,
  nsim,
  ...,
  use_parallel_simulations = FALSE,
  ncores_outer = max(floor(parallel::detectCores()/2), 1L, na.rm = TRUE),
  seed = NULL,
  seeds = NULL,
  results_file = NULL,
  action = c("replace", "resume", "archive"),
  retry_failed = TRUE,
  continue_on_error = TRUE,
  measure_resources = FALSE,
  show_progress = TRUE,
  force_windows = FALSE
)

Arguments

Details

When results_file is supplied, each simulation is stored as a record with its number, seed, success status, elapsed time, optional peak RAM, returned value, and error message. Existing results can be replaced, resumed, or archived.

Value

A list of nsim simulation records, invisibly when a resumed run has no work remaining.

See Also

uni_mclapply(), get_generator_parameters()

Examples

run_simulations(
  one_simulation = function(simulation, offset) simulation + offset,
  nsim = 2, offset = 10, seed = 1, show_progress = FALSE
)

Description

Shifts an LSM logit matrix using the historical iterative method or calibrated bisection.

Usage

scale_lsm_to_average_degree(
  theta,
  average_degree,
  average_degree_method = c("naive", "calibrated"),
  self_loops = FALSE,
  lower_clip = 0,
  upper_clip = 1,
  naive_iterations = 10L,
  tolerance = 1e-08,
  max_iterations = 100L
)

Arguments

Value

A list containing the probability matrix P and intercept_shift.

See Also

generate_lsm(), scale_to_average_degree()

Examples

Z <- matrix(stats::rnorm(200 * 3), 200, 3)
theta <- -tcrossprod(Z)
scaled <- scale_lsm_to_average_degree(theta, average_degree = 5)
mean(rowSums(scaled$P))

Description

These twin functions apply calibrated bisection or the historical one-step scaling rule to a dense or sparse probability matrix.

scale_to_average_degree_naive() applies the historical one-step ratio before clipping and loop removal.

Usage

scale_to_average_degree(
  P,
  average_degree,
  self_loops,
  lower_clip,
  upper_clip,
  tolerance = 1e-08,
  max_iterations = 100L
)

scale_to_average_degree_naive(
  P,
  average_degree,
  self_loops,
  lower_clip,
  upper_clip
)

Arguments

Value

A list containing the scaled probability matrix P and multiplier.

See Also

apply_average_degree_scaling(), scale_lsm_to_average_degree()

Examples

P <- matrix(0.1, 200, 200)
diag(P) <- 0
scaled <- scale_to_average_degree(
  P, average_degree = 5, self_loops = FALSE,
  lower_clip = 0, upper_clip = 1
)
mean(rowSums(scaled$P))

Description

Computes all-pairs shortest-path distances without requiring igraph. Rows are sources, columns are destinations, diagonal entries are zero, and unreachable pairs are Inf. Undirected weighted paths use the smaller nonzero weight when reciprocal weights differ.

Usage

shortest_path_distances(
  A,
  directed = FALSE,
  weighted = FALSE,
  self_loops = c("ignore", "include"),
  use_cpp = TRUE
)