Designing Monte Carlo simulations can be a fun and rewarding experience. Whether you are interested in evaluating the performance of a new optimizer, re-evaluating previous research claims (like the ANOVA is ‘robust’ to violations of normality), determine power rates for an upcoming research proposal, or simply to appease a strange thought in your head about a new statistical idea you heard about, designing Monte Carlo simulations can be incredibly rewarding and are extremely important to those who are statistically oriented. However, organizing simulations can be a challenge, and all too often coders resort to the dreaded “for-loop” strategy, *for*-ever resulting in confusing, error prone, and simulation specific code. The package `SimDesign`

is one attempt to fix these and other issues that often arise when designing Monte Carlo simulation experiments.

Generally speaking, Monte Carlo simulations can be broken into three major components:

**generate**your data from some model/probability density function given various**design**conditions to be studied (e.g., sample size, distributions, group sizes, etc),**analyse**the generated data using whatever statistical analyses you are interested in (e.g., \(t\)-test, ANOVA, SEMs, IRT, etc), and collect the statistics/CIs/\(p\)-values/parameter estimates you are interested in, and**summarise**the results after repeating the simulations \(R\) number of times to obtain empirical estimates of the population’s behavior.

Each operation above represents the essential components of the `SimDesign`

package. The **design** component is represented by a `data.frame`

object containing the simulation conditions to be investigated, while **generate**, **analyse**, and **summarise** represent user-defined functions which comprise the three steps in the simulation. Each of these components are constructed and passed to the `runSimulation()`

function where the simulation steps are evaluated, ultimately returning a `data.frame`

object containing the simulation results.

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