Package: simDAG 1.0.0

Introduction | Motivation | Using DAGs to define data generation processes | Comparison with existing software | Organization of this article | The workflow | Included functions | Defining the DAG | Supported node types | Simulating crossectional data | Simulating longitudinal data with few points in time | Simulating longitudinal data with many points in time | Formal description | A simple example | Simulating adverse events after Covid-19 vaccination | Additionally supported features | Computational considerations | Discussion | Computational details | Acknowledgments | Appendix A: Further Features of Discrete-Time Simulation | Time-Dependent Base Probabilities | Time-Dependent Effects | Non-Linear Effects | Multiple Interrelated Binary Time-Dependent Variables | Using Baseline Covariates | Using Categorical Time-Dependent Variables | Using Continuous Time-Dependent Variables | Ordered Events | Literature

Introduction | What are networks-based simulations? | Networks in regular simulations | A single network | Multiple networks | Weighted networks | Directed Networks | Neighborhood order | Networks as a function of other variables | Networks in discrete-time simulation | Static networks | Dynamic networks | Random new networks at each point in time | Adjusting a network over time | Discussion | References

Introduction | What are causal DAGs and why use them? | Defining the DAG | Root node types | Child node types | Defining nodes manually | Defining nodes using existing data | Time-varying covariates | References

Introduction | What is Discrete-Event Simulation and Why Use it? | Defining the DAG | A single time-dependent variable | Discrete-Time approach | Discrete-Event approach | Two interrelated time-dependent variables | Some things to consider | Time-Dependent probabilities and effects | Categorical / Count / Continuous variables | Discussion | References

Introduction | What is Discrete-Time Simulation and Why Use it? | Defining the DAG | A Simple Example - One Terminal Event | Extending the Simple Example - Recurrent Events | References

Introduction | Simulating Randomized Controlled Trials | Two Treatment Groups | Three or More Treatment Groups | Multiple Outcome Measurements | Non-Compliance to Treatment Assignment | With Cluster Randomization | Simulating Observational Studies | Crossectional Data | Longitudinal Data | Cox Model with Time-Varying Covariates | Aalen Additive Hazards Model with Time-Dependent Covariates | Miscellaneous Simulations | Simulating Multi-Level Data | Simulating Mixture Distributions | Simulating Outliers | Simulating Missing Values | Simulating Measurement Error

Introduction | Root Nodes | Requirements | Examples | Child Nodes | Time-Dependent Nodes | Time-Dependent Root Nodes | Time-Dependent Child Nodes | Using the sim_time Argument | Using the past_states Argument | Using the Formula Interface | Some General Comments

Introduction | How to get started | 1.) Formulate the goal of your research project in a detailed fashion. | 2.) Build a theoretical model of the system you want to simulate. | 3.) Identify the parts of the system that you are most interested in. | 4.) Obtain and analyze real data. | 5.) Simulate data for $t = 0$ (if needed). | 6.) Write functions for each time-varying node, one at a time. | 7.) Inspect the resulting data for inconsistencies. | Our research goal and the theoretical model | Research goal | Theoretical model | Implementing the model | Part 1: Adding vaccination, covid and sickness | Part 2: Adding adverse effects of vaccination and covid | Part 3: Making the vaccine useful | Part 4: Sick people don't get vaccinated | Generating Data using the final model | Going even further | References

Introduction | A simple example | Using a Categorical Parent Variable | Using Interaction Effects | Using Cubic Terms | Using Functions in formula | Using Special Characters in formula | Using Random Effects and Random Slopes | Using External Coefficients (Advanced Usage) | Using Formulas in Custom Node Types (Advanced Usage)