Overview | Key Definitions | Category 1: Attribute Dependence Terms ($g_i$ Terms) | attribute_x | attribute_y | cov_x(data = v) | cov_y(data = v) | attribute_xy(mode = "global" | "local" | "alocal") | Category 2: Network Dependence Terms ($h_{i,j}$ Terms) | degrees | edges(mode = "global" | "local" | "alocal") | mutual(mode = "global" | "local" | "alocal") | cov_z(data = w, mode = "global" | "local" | "alocal") | cov_z_out(data = v, mode = "global" | "local" | "alocal") | cov_z_in(data = v, mode = "global" | "local" | "alocal") | isolates | nonisolates | gwdegree(mode = "global" | "local", decay = α) | gwodegree(mode = "global" | "local", decay = α) | gwidegree(mode = "global" | "local", decay = α) | transitive | gwesp_symm(mode = "global" | "local", decay = α) | gwesp(mode = "global" | "local", type = "OTP" | "ISP" | "OSP" | "ITP", decay = α) | gwdsp_symm(mode = "local", decay = α) | gwdsp(mode = "global" | "local", type = "OTP" | "ISP" | "OSP" | "ITP", decay = α) | Category 3: Joint Attribute/Network Dependence Terms ($h_{i,j}$ Terms) | attribute_xz(mode = "local") | attribute_yz(mode = "local") | edges_x_match(mode = "global" | "local") | edges_y_match(mode = "global" | "local") | outedges_x(mode = "global" | "local" | "alocal") | inedges_x(mode = "global" | "local" | "alocal") | outedges_y(mode = "global" | "local" | "alocal") | inedges_y(mode = "global" | "local" | "alocal") | spillover_xx(mode = "local") | spillover_xx_scaled(mode = "global" | "local") | spillover_yy(mode = "local") | spillover_yy_scaled(mode = "global" | "local") | spillover_xy(mode = "local") | spillover_xy_scaled(mode = "global" | "local") | spillover_yx(mode = "local") | spillover_yx_scaled(mode = "global" | "local") | spillover_yc(mode = "local", data = v) | Quick-Reference Table | Example: Combining Multiple Terms | References

Overview | Statistic Transformations | Endogenous Network Statistics | 1. Intercept (Intercept / intercept) | 2. Inertia (inertia / number_interaction) | 3. Reciprocity (reciprocity) | 4. Duration (duration / current_interaction) | 5. Participation Shifts (P-shifts) | Triadic Closure and Shared Partners | Common Partners (general_common_partners / current_common_partners) | Triangles (general_triangle / current_triangle) | Degree and Centrality Statistics | Degree Statistics (general_degree_out_sender, etc.) | Count Statistics (general_count_out_sender, etc.) | Exogenous Statistics | 1. Dyadic Covariate (dyadic_cov) | 2. Monadic Covariate (monadic_cov) | Developer Guide: Adding Custom Sufficient Statistics | Step 1: Implement the Update Logic in C++ | Step 2: Write the R Initializer Function

Abstract | Theoretical Framework | Summary Statistics | Fine-tuning with \code | Example: Simulating and Fitting a DEM | Data Preparation | Model Fitting | Interpretation | Simulation and Model Diagnostics | Predicting and Simulating Events | Residual Analysis | References

Abstract | Theoretical Framework: Relational Event Models | Summary Statistics and Degree Effects | Example: Simulating and Fitting a REM | Data Preparation | Model Fitting | Interpretation | Simulation and Model Diagnostics | Predicting and Simulating Events | Residual Analysis

Overview | Model Specification | Model Simulation | Model Estimation | Model Assessment

Exponential Random Graph Models for Large Networks | Model Specification | Between-block Model | Within-block Model | Estimation | Installation | A simple example | Simulation | Goodness-of-fit | When you work with large networks | References