Package index • sir

Model Fitting

Fit Social Influence Regression models to network data

sir(): Social Influence Regression (SIR) Model
sir_alsfit(): Fit SIR Model via Alternating Least Squares (ALS)
sir_optfit(): Fit SIR Model via Direct Optimization

S3 Methods

Standard R methods for sir model objects

coef(<sir>): Extract Model Coefficients from a SIR Model
vcov(<sir>): Variance-Covariance Matrix for SIR Model Parameters
confint(<sir>): Confidence Intervals for SIR Model Parameters
nobs(<sir>): Extract Number of Observations from a SIR Model
fitted(<sir>): Extract Fitted Values from a SIR Model
residuals(<sir>): Extract Residuals from a SIR Model
logLik(<sir>): Extract Log-Likelihood from a SIR Model
AIC(<sir>): Akaike Information Criterion for a SIR Model
BIC(<sir>): Bayesian Information Criterion for a SIR Model
predict(<sir>): Predictions from a Fitted SIR Model
summary(<sir>): Summary of a Fitted SIR Model
print(<sir>): Print a Fitted SIR Model
print(<summary.sir>): Print a SIR Model Summary
plot(<sir>): Diagnostic Plots for a Fitted SIR Model

Visualization

Network visualization of influence matrices

plot_sir_network(): Network Graph Visualization of Influence Matrices

Bootstrap & Inference

Bootstrap standard errors and robust inference

boot_sir(): Bootstrap Inference for SIR Model Parameters
print(<boot_sir>): Print Bootstrap SIR Results
summary(<boot_sir>): Summary of Bootstrap SIR Results
confint(<boot_sir>): Confidence Intervals from Bootstrap SIR Results

Counterfactual Scenarios

Build counterfactual scenario arrays for prediction

get_scen_vals(): Compute Summary Statistics for Scenario Construction
get_scen_array(): Build Counterfactual Scenario Array

Simulation & Data Preparation

Simulate network data and prepare inputs for SIR models

sim_sir(): Simulate Data from a Social Influence Regression Model
rel_covar(): Construct Relational Covariates from a Network Array
cast_array(): Cast Directed Dyadic Data into Array Format
eta_tab(): Calculate Linear Predictor (eta) for SIR Model
mll_sir(): Calculate Negative Log-Likelihood for SIR Model

C++ Backend

Optimized C++ routines for matrix operations

cpp_tprod_A_X_Bt(): Tensor Product for SIR Model (A * X * B')
cpp_amprod_W_v(): Array-Matrix Product for Influence Matrices
cpp_construct_Wbeta_design(): Construct Design Matrix for Alpha Updates in ALS
cpp_construct_Walpha_design(): Construct Design Matrix for Beta Updates in ALS
cpp_construct_Wbeta_design_dyn(): Construct Design Matrix for Alpha Updates with Dynamic W
cpp_construct_Walpha_design_dyn(): Construct Design Matrix for Beta Updates with Dynamic W
cpp_mll_gH(): Calculate Gradient and Hessian for Direct Optimization
cpp_mll_gH_dyn(): Calculate Gradient and Hessian with Dynamic W

Internal Helpers

Internal utility functions (not exported)

flatten_Y(): Flatten Y Array for GLM Input
flatten_Z(): Flatten Z Array for GLM Input
prepare_Z_list(): Prepare Z Array for C++ Consumption
amprod(): Array-matrix product (R implementation)
mat(): Matricization (R implementation)
tprod(): Tucker product (R implementation)