Introduction | Reading GEXF files | Creating GEXF files | Embedding gexf-js in quarto/rmarkdown documents

Overview | 1 A simple coloured graph | 1.1 Creating the graph | 1.2 Plotting the graph | 2 The Les Misérables network | 2.1 Reading the file | 2.2 Default plot | 2.3 Adding node borders for contrast | 3 Using the legacy gexf-js viewer | Session info

A working example | Coloring vertices: vertex.color = ~ attr | Shaping vertices: vertex.nsides = ~ attr | Putting it together | Scaling edges: edge.width = ~ attr | Applying formulas after the fact

Introduction | Reading GEXF files | Creating GEXF files | Embedding gexf-js in quarto/rmarkdown documents

Introduction | Putting all together | Nodes | Edges | Edge width

Overview | 1 A simple coloured graph | 1.1 Creating the graph | 1.2 Plotting the graph | 2 The Les Misérables network | 2.1 Reading the file | 2.2 Default plot | 2.3 Adding node borders for contrast | 3 Using the legacy gexf-js viewer | Session info

Set up | Creating the model | Outbreak size | Hospitalizations | Scenario without Quarantine | Final comments

Introduction | A minimal model built from scratch | Extending the model to SEIRD

Building the model | Running the model | Adding post-exposure prophylaxis (PEP)

Introduction | An SEIR model with mixing | Investigating the history

Introduction | Example Scenario: Simultaneous COVID-19 and Flu Outbreaks | Model Setup | Add the Flu Virus | Add a Tool (Vaccine) | Add Events | Full Model Summary | Reproductive Numbers

Example 1: Simulating an SIR model | Setup and running the model | Plot | Extracting information | Adding more viruses/viruses | Tools

Introduction | General flow of the models | Computing probabilities | Transmission probability | Adjusted probabilities | Transmission in connected models[^models]

Introduction | Setup The SIR Model | Setup LFMCMC | Run LFMCMC Simulation | Results

Introduction | Example: Simulating a SEIRCONN Model 50 Times | Setup and Running Model | Generating a Saver | Plotting

Imaginary Network Motifs: Testing Systematic Perception Errors | Setup | Data Overview | Creating the CSS Graphs | Observed Motif Counts | Null-Model Testing | Results | Full Summary | Tie-Level Accuracy | Conclusion

Krackhardt Advice Network | Data and Perceptions | Individual Accuracy | Perceptual Structure | Organizational Takeaways

Perceptual Error Analysis | Setup: A Small Example Network | Reciprocity Errors | Imaginary Census | Individual Accuracy | Null Model Testing | Key Takeaways

Introduction | What Are Cognitive Social Structures? | Key Features | Creating Barry Graph Objects | From a List of Matrices | From a Block-Diagonal Matrix | Inspecting Graph Attributes

Life expectancy in the US | Operating within the loop | Using ith_step() with multiple chains | Saving information as it happens

The slurmR R package | Socket clusters | Job arrays using the *apply family | Sockets vs Arrays | Example simulating Pi | Single node (machine), multi-core simulation | Multi-job submission with job arrays | Multi-node cluster object

Setup | Simulation | Inference

Introduction | Definitions | Submitting jobs via sbatch

Motivating example | Reusing fmcmc_kernel objects | Accessing other elements of the chain

Introduction | Size parameter in a binomial distribution

Introduction | Checking logs | Job-resubmission

log-likelihood | Gradient | Hessian | Limiting values | Log-likelihood | References

Example: Bayesian inference with fivenets

Data preprocessing | Methods | Statistical test | Retrieving the data to create a panel/envent history/longitudinal data

Diffusion process | Multiple simulations using rdiffnet_multiple

Introduction | Raw network data | Edge lists | Example 1: Loading a static diffusion network via as_diffnet | Example 2: Loading a static diffusion network via edgelist_to_diffnet | Survey data: Nomination networks | Example 3: Cross section diffusion data | Example 4: Longitudinal diffusion data

Infectiousness | Susceptibility