Monte Carlo simulations are computer experiments designed to study the performance of statistical methods under known data-generating conditions (Morris, White, & Crowther, 2019). Methodologists use simulations to examine questions such as: (1) how does ordinary least squares regression perform if errors are heteroskedastic? (2) how does the presence of missing data affect treatment effect estimates from a propensity score analysis? (3) how does cluster robust variance estimation perform when the number of clusters is small? To answer such questions, we conduct experiments by simulating thousands of datasets based on pseudo-random sampling, applying statistical methods, and evaluating how well those statistical methods recover the true data-generating conditions (Morris et al., 2019).

The goal of `simhelpers`

is to assist in running simulation studies. The main tools in the package consist of functions to calculate measures of estimator performance like bias, root mean squared error, rejection rates. The functions also calculate the associated Monte Carlo standard errors (MCSE) of the performance measures. These functions are divided into three major categories of performance criteria: absolute criteria, relative criteria, and criteria to evaluate hypothesis testing. The functions use the `tidyeval`

principles, so that they play well with `dplyr`

and fit easily into a `%>%`

-centric workflow (Wickham et al., 2019).

In addition to the set of functions that calculates performance measures and MCSE, the package also includes a function, `create_skeleton()`

, that generates a skeleton outline for a simulation study. Another function, `evaluate_by_row()`

, runs the simulation for each combination of conditions row by row. This function uses `future_pmap()`

from the `furrr`

package, making it easy to run the simulation in parallel (Vaughan & Dancho, 2018). The package also includes several datasets that contain results from example simulation studies.

You can install the development version from GitHub with:

Our explanation of MCSE formulas and our general simulation workflow is closely aligned with the approach described by Morris et al. (2019). We want to recognize several other R packages that offer functionality for conducting Monte Carlo simulation studies. In particular, the `rsimsum`

package (which has a lovely name that makes me hungry) also calculates Monte Carlo standard errors (Gasparini, 2018). The `SimDesign`

package implements a generate-analyze-summarize model for writing simulations; it also includes tools for error handling and parallel computing (Chalmers, 2019).

In contrast to the two packages mentioned above, our package uses `tidyeval`

and outputs tibbles, which can then easily be used with `dplyr`

, `tidyr`

and `purrr`

syntax (Wickham et al., 2019). The functions that calculate MCSEs are easy to run on grouped data. For parallel computing, we use the `furrr`

and `future`

packages (Bengtsson, 2020; Vaughan & Dancho, 2018). Moreover, in contrast to the `rsimsum`

and `SimDesign`

packages, `simhelpers`

provides jack-knife MCSE for variance estimators. It also provides jack-knife MCSE estimates for root mean squared error.

Another related project is `DeclareDesign`

, a suite of packages that allow users to declare and diagnose research designs, fabricate mock data, and explore tradeoffs between different designs (Blair et al., 2019). This project follows a similar model for how simulation studies are instantiated, but it uses a higher-level API, which is tailored for simulating certain specific types of research designs. In contrast, our package is a simpler set of general-purpose utility functions.

Other packages that have similar aims to `simhelpers`

include: MonteCarlo, parSim, simsalapar, simulator, simstudy, simTool, simSummary, and ezsim.

We are grateful for the feedback provided by Danny Gonzalez, Sangdon Lim and Man Chen.

Bengtsson, H. (2020). future: Unified parallel and distributed processing in r for everyone. Retrieved from https://CRAN.R-project.org/package=future

Blair, G., Cooper, J., Coppock, A., & Humphreys, M. (2019). Declaring and diagnosing research designs. American Political Science Review, 113(3), 838–859. Retrieved from https://declaredesign.org/paper.pdf

Chalmers, P. (2019). SimDesign: Structure for organizing Monte Carlo simulation designs. Retrieved from https://CRAN.R-project.org/package=SimDesign

Gasparini, A. (2018). rsimsum: Summarise results from Monte Carlo simulation studies. Journal of Open Source Software, 3(26), 739. https://doi.org/10.21105/joss.00739

Morris, T. P., White, I. R., & Crowther, M. J. (2019). Using simulation studies to evaluate statistical methods. Statistics in Medicine, 38(11), 2074–2102.

Vaughan, D., & Dancho, M. (2018). furrr: Apply mapping functions in parallel using futures. Retrieved from https://CRAN.R-project.org/package=furrr

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686