new_sbm_network()
Builds a new sbm_network object from data
save_sbm_network()
Save sbm_network object
load_sbm_network()
Load sbm_network object
Function to fit or investigate fit of SBM model
mcmc_sweep()
Run a single MCMC sweep over nodes
collapse_blocks()
Agglomeratively merge blocks
collapse_run()
Run Agglomerative Merging to target a range of block numbers
choose_best_collapse_state()
Choose and load best model state from agglomerative collapsing algorithm
Functions to visualize the structure of network and/or results of modeling
visualize_collapse_results()
Visualize agglomerative collapse results
visualize_mcmc_trace()
Trace plot of multiple MCMC sweep sweeps
visualize_network()
Visualize network stucture
visualize_propensity_dist()
Visualize pairwise propensity distribution
visualize_propensity_network()
Plot network of nodes connected by pairwise block propensity
Functions to generate networks using the stochastic block model. Usefull for testing etc..
sim_basic_block_network()
Simulate stochastic block model of given number of blocks
sim_random_network()
Simulate completely random network
sim_sbm_network()
Simulate network using stochastic block model
Extract various properties from the model
get_block_edge_counts()
Get counts between blocks at a level
get_collapse_results()
Get collapse results from model
get_combination_indices()
Get Combination Indices
get_entropy()
Compute entropy for current model state
get_node_to_block_edge_counts()
Get node's edge counts to blocks
get_num_blocks()
Get number of blocks currently in model
get_state()
Dump state of current SBM model to dataframe
get_sweep_pair_counts()
Get pairwise group sharing counts from model
get_sweep_results()
Get mcmc sweep results
Functions that are used to modify the model at a low-level. These are usually reserved for more advanced use-cases.
add_edge()
Add edge between two nodes in network
add_node()
Add a new node to network
set_node_parent()
Set the parent of a node
initialize_blocks()
Assign blocks for all nodes
update_state()
Update SBM model state
verify_model()
Verify model for sbm_network object exists
Various small functions used for internals of package
build_score_fn()
Build score function from heuristic
rolling_mean()
Calculate a lagged rolling mean
sbmR-package
Package reference
print(<sbm_network>)
Print network