\(\newcommand{\Prob}{\text{Pr}}\) \(\newcommand{\E}{\mathbb{E}}\) \(\newcommand{\M}{\mathbb{M}}\) \(\newcommand{\Q}{\mathbb{Q}}\) \(\newcommand{\Var}{\text{Var}}\) \(\newcommand{\Bias}{\text{Bias}}\) \(\newcommand{\RMSE}{\text{RMSE}}\) \(\newcommand{\Cov}{\text{Cov}}\) \(\newcommand{\cor}{\text{cor}}\) \(\newcommand{\diag}{\text{diag}}\) \(\newcommand{\mat}[1]{\mathbf{#1}}\) \(\newcommand{\bs}{\boldsymbol}\) \(\newcommand{\trace}{\text{tr}}\)

B Further readings and resources

We close with a list of things of interest we have discovered while writing this text.

Morris, White, & Crowther (2019). Using simulation studies to evaluate statistical methods.
- High-level simulation design considerations.
- Details about performance criteria calculations.
- Stata-centric.
SimDesign R package (Chalmers, 2019)
- Tools for building generic simulation workflows.
- Chalmers & Adkin (2019). Writing effective and reliable Monte Carlo simulations with the SimDesign package.
DeclareDesign (Blair, Cooper, Coppock, & Humphreys)
- Specialized suite of R packages for simulating research designs.
- Design philosophy is very similar to “tidy” simulation approach.