Summary | Description | Inputs | Code Path

Extraction and transformation | Level selection | Aside: base and keep arguments:

Overview | Core Concepts | Network Wrappers: ErgmCppNetwork and ErgmCppWtNetwork | Model Terms: ErgmCppModelTerm and ErgmCppWtModelTerm | Proposals: ErgmCppProposal and ErgmCppWtProposal | Helper macros | Examples | Notes and Best Practices

Summary | Overview | Types of storage and types of term | Code path | Formal API definition | Evaluation API | R side | InitErgmTerm.* and InitWtErgmTerm.* functions | Expects | Returns | C side | ModelTerm and WtModelTerm data structures | c_, d_, and s_ functions | Macros | Side-effects | Storage API | C side: Modifying Storage | i_ functions: Initializer/Constructor | u_ functions: Updater | f_ functions: Finalizer/Destructor | C side: Accessing Storage | Private storage | Public storage | x_ functions: eXtensions | w_ functions: save extended state | Memory management | Miscellaneous helpers | MHproposal storage API | Auxiliaries

r packageDescription('tsna')$Package : r packageDescription('tsna')$Title | Overview | Requirements | Introduction | Temporal Paths and metrics | Explanation of paths in network | Highlite possible paths found on Moody's example. | More fun facts about paths | Other types of temporal paths | Compare earliest forward set sizes | Static projections of temporal networks | Rates of Change | Static graph metrics as time series | Using sna package metrics | Using ergm terms as static metrics | Durations and densities | Distributions of edge durations | Re-occuring edges | Finding vertex activity durations | Finding connected times of vertices | Difference between degree and tiedDuration | Compare duration measures on various example networks | Density of activity | Measures of sequence | pShift | Next steps | Citation | Bibliography

Introduction | Software installation | 1. Statistical network modeling with ERGMs | The general form for an ERGM | The model statistics $g(y)$: ERGM terms | Coding new ergm terms | ERGM probabilities: at the tie-level | The summary and ergm functions, and supporting functions | Network data | Model specification | A simple Bernoulli ("Erdos/Renyi") model | Triad formation | Nodal covariates: effects on mean degree | Nodal covariates: Homophily | Directed ties | 2. Missing data | 4. Assessing convergence for dyad dependent models: MCMC Diagnostics | What it looks like when a model converges properly | 5. Network simulation: the simulate command and network.list objects | 6. Examining the quality of model fit -- GOF | 7. Diagnostics: troubleshooting and checking for model degeneracy | 8. Working with egocentrically sampled network data | 9. Additional functionality in statnet and other package | Current statnet packages | Additional functionality in base ergm | Extensions by other developers | The statnet development team | Further reading | Dealing with Model Degeneracy | Temporal ERGMs | Egocentric ERGMS | References

Overview | Types of storage and types of term | Formal API definition | New types | New struct elements | Network and WtNetwork | New functions | NB: Undefined Behaviour Sanitisers

The ukFaculty dataset | A first attempt | Attempting to fit with ERGM

Obtaining data | Data summaries | Reproducing ERGM fits | Diagnostics | References

Interactive searching | Basic / Frequently-used term keyword matrix | Complete term keyword matrix | Term definitions table | Term index by keyword

Coverage | Modeling ordinal relational data using ergm.rank | References | Terms | Example

Introduction | Estimation | Simulation | From a fitted tergm object | From a network (or networkDynamic)

Introduction to LOLOG | Getting Started | Statistical Network Modeling: The lolog Command and lolog Object | An Erdos-Renyi Model | A Triangle Model | The Effect of Wealth | Does Large Wealth Inequality Matter? | Reciprocity | Modeling Larger Networks | Specifying Vertex Inclusion Order | Model Terms Available for LOLOG Estimation and Simulation | Terms Provided with lolog | Coding New Terms | Network Simulation and Computation | Working with BinaryNet Objects | Order Dependent Terms | Examining the Quality of Model Fit - GOF