- Perform discrete-time dynamic simulation, i.e.: transform a single set of micro-units
- transformations can occur each iteration
- transformations include results from
- logistic, binomial, Poisson, negative binomial, and normal regression models with coefficients specified via a file
- transformations according to discrete probabilities specified in code or a file
- Generate descriptive statistics from the results of each iteration including frequencies, means, quantiles, and summaries
- Statistics can be generated for the whole population, or subsets
- Statistics can be grouped by base variables (ie: variables that don’t change during the simulation)
- Perform multiple simulation runs and average tracked descriptive statistics across multiple runs
- Scenario testing via the modification of simulation variables so the flow-on effects can be observed
- continuous variables can be modified before the simulation begins
- categorical variables can be modified before the simulation begins, or during the simulation for specific iterations
- For details on performance and limits see PerformanceLimits
- Available as an R package
- Areas of development not yet explored:
- Scenario testing via the modification of model parameters, rather than simulation variables
- Continuous time simulation and simulation with event queues
- Simulation with large populations
- Simulation with more than one population at a time
- Calibration and alignment
Developed by the Centre of Methods and Policy Application in the Social Sciences (COMPASS) within the Faculty of Arts of The University of Auckland. A component of the New Zealand Social Science Data Service (NZSSDS). Supported by the Health Research Council of New Zealand (HRC), the Science and Innovation (MSI) section of the Ministry of Business, Innovation & Employment (MBIE), Royal Society of New Zealand, and Te Punaha Matatini Centre of Research Excellence.