Package index • dbn

Model Fitting

Fit DBN models to temporal network data

dbn(): Dynamic Bilinear Network Analysis
dbn_static(): Static DBN MCMC
dbn_dynamic(): Dynamic DBN MCMC
dbn_lowrank(): DBN Low-rank Model
dbn_lowrank_accurate(): DBN Low-rank Model (Accurate)
dbn_hmm(): DBN HMM Model
dbn-options: DBN Package Options
dbn-methods: S3 Methods for DBN Objects

Simulation

Generate synthetic network data

simulate: Simulate Data from DBN Models
simulate_test_data(): Simulate Simple Test Data
simulate_static_dbn(): Simulate from Static DBN Model
simulate_dynamic_dbn(): Simulate from Dynamic DBN Model
simulate_piecewise_dbn(): Simulate Data from Piecewise DBN
simulate_lowrank_dbn(): Simulate from Low-Rank DBN Model
simulate_hmm_dbn(): Simulate from HMM DBN Model

Posterior Inference

Posterior prediction, draws, and forecasting

posterior_predict_dbn(): Generate Posterior Predictive Samples
derive_draws(): Derive new quantities from posterior draws
regime_probs(): Extract regime probabilities for HMM models

Impulse Response Analysis

Compute and analyze network shocks

compute_irf(): Compute Network-Level Impulse Response Functions
impulse_response_const(): Compute impulse response for constant A,B matrices
impulse_response_dynamic(): Compute impulse response for time-varying A,B matrices
build_shock(): Build Shock Matrix for IRF Analysis
debug_irf(): Debug IRF

Network Analysis

Summarize and analyze network structure

network_summary(): Network-level posterior summary
edge_prob(): Posterior edge probability
theta_summary(): Summarize Theta over posterior draws
theta_credible(): Posterior credible intervals for Theta
param_summary(): Summarize Scalar Parameters
latent_summary(): Summarize Baseline Mean M
tidy_dbn_lowrank(): Tidy Extractor for Low-Rank Factor Trajectories
theta_slice(): Extract Theta slices from posterior draws
net_snapshot(): Network Snapshot
dyad_path(): Plot Dyad Trajectory
role_trajectory(): Track Role Evolution
get_group_influence(): Extract Group Influence Trajectories
compare_group_influence(): Compare Group Influences

Network Statistics

Compute descriptive network statistics

stat_density(): Network Statistic: Density
stat_in_degree(): Network Statistic: In-Degree
stat_out_degree(): Network Statistic: Out-Degree
stat_reciprocity(): Network Statistic: Reciprocity
stat_transitivity(): Network Statistic: Transitivity

Visualization

Plot models, diagnostics, and results

plot_theta(): Plot Theta Heatmap
plot_trace(): Plot Parameter Trace Plots
plot_regime_probs(): Plot Regime Probabilities
plot_ppc_density(): Posterior predictive density plot
plot_ppc_ecdf(): Posterior predictive ECDF plot

Model Methods

S3 methods for dbn objects

print(<dbn_derived>): Print method for derived quantities
print(<dbn_irf>): Print IRF
print(<dbn_ppd>): Print method for posterior predictive distribution
summary(<dbn>): Summarize a Fitted DBN Model
summary_dbn(): Summary method for DBN objects
predict(<dbn>): Predict from a Fitted DBN Model
plot(<dbn>): Plot Diagnostics for a Fitted DBN Model
plot(<dbn_irf>): Plot IRF
plot_dbn(): Plot method for DBN objects
plot_group_influence(): Plot Group Influence Profile
plot_ppc_density(): Posterior predictive density plot
plot_ppc_ecdf(): Posterior predictive ECDF plot
plot_regime_probs(): Plot Regime Probabilities
plot_theta(): Plot Theta Heatmap
plot_trace(): Plot Parameter Trace Plots
compare_dbn(): Compare Multiple DBN Models
compare_blocks(): Compare Influence Matrices Across Blocks
check_convergence(): Check MCMC Convergence

Utilities

Configuration and computational utilities

estimate_memory(): Estimate Memory Usage for Dynamic DBN
get_dbn_threads(): Get the current number of threads used by dbn
set_dbn_threads(): Set the number of threads used by dbn
ffbs_theta_struct_5arg_cpp(): Structured FFBS for Theta

Data

Example datasets

example_data: Example Dynamic Bilinear Network Data