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SimDesign

Structure for Organizing Monte Carlo Simulation Designs

Installation

To install the latest stable version of the package from CRAN, please use the following in your R console:

install.packages('SimDesign')

To install the Github version of the package with devtools, type the following (assuming you have already installed the devtools package from CRAN).

library('devtools')
install_github('philchalmers/SimDesign')

Getting started

For a description pertaining to the philosophy and general workflow of the package it is helpful to first read through the following: Chalmers, R. Philip, Adkins, Mark C. (2020) Writing Effective and Reliable Monte Carlo Simulations with the SimDesign Package, The Quantitative Methods for Psychology, 16(4), 248-280. doi: 10.20982/tqmp.16.4.p248

Coding examples found within this article range from relatively simple (e.g., a re-implementation of one of Hallgren's (2013) simulation study examples, as well as possible extensions to the simulation design) to more advanced real-world simulation experiments (e.g., Flora and Curran's (2004) simulation study). For additional information and instructions about how to use the package please refer to the examples in the associated Github wiki.

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Version

Install

install.packages('SimDesign')

Monthly Downloads

9,287

Version

2.22

License

GPL (>= 2)

Issues

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Repository

https://github.com/philchalmers/SimDesign/wiki

Homepage

http://philchalmers.github.io/SimDesign/

Maintainer

Phil Chalmers

Last Published

December 17th, 2025

Functions in SimDesign (2.22)

Compute the (normalized) root mean square error

Compute the mean absolute error

Compute the relative difference

Perform a test that indicates whether a given Generate() function should be executed

Empirical detection robustness method suggested by Serlin (2000)

Function to extract extra information from SimDesign objects

Template-based generation of the Generate-Analyse-Summarise functions

Removes/cleans files and folders that have been saved

Check for missing files in array simulations

Function for decomposing the simulation into ANOVA-based effect sizes

Robbins-Monro (1951) stochastic root-finding algorithm

Surrogate Function Approximation via the Generalized Linear Model

Structure for Organizing Monte Carlo Simulation Designs

Add missing values to a vector given a MCAR, MAR, or MNAR scheme

Collapse separate simulation files into a single result

Compute (relative/standardized) bias summary statistic

Summarise simulated data using various population comparison statistics

Function to read in saved simulation results

One Dimensional Root (Zero) Finding in Simulation Experiments

Generate a basic Monte Carlo simulation GUI template

Create the simulation design object

Form Column Standard Deviation and Variances

Get job array ID (e.g., from SLURM or other HPC array distributions)

listAvailableNotifiers

List All Available Notifiers

Manage specific warning messages

clusterSetRNGSubStream

Set RNG sub-stream for Pierre L'Ecuyer's RngStreams

Compute prediction estimates for the replication size using bootstrap MSE estimates

Increase the intensity or suppress the output of an observed message

Generate random seeds

Expand the simulation design object for array computing

new_PushbulletNotifier

Create a Pushbullet Notifier

expandReplications

Expand the replications to match expandDesign

notify.TelegramNotifier

S3 method to send notifications through the Telegram API.

rbind.SimDesign

Combine two separate SimDesign objects by row

Run a summarise step for results that have been saved to the hard drive

Auto-named Concatenation of Vector or List

notify.PushbulletNotifier

S3 method to send notifications via Pushbullet

Generate non-normal data with Headrick's (2002) method

Send a simulation notification

Generate non-normal data with Vale & Maurelli's (1983) method

Suppress verbose function messages

new_TelegramNotifier

Create a Telegram Notifier

rejectionSampling

Rejection sampling (i.e., accept-reject method)

Generate integer values within specified range

Generate data with the multivariate t distribution

Generate data with the inverse Wishart distribution

Run a Monte Carlo simulation given conditions and simulation functions

runArraySimulation

Run a Monte Carlo simulation using array job submissions per condition

Generate data with the multivariate g-and-h distribution

Format time string to suitable numeric output

Generate data with the multivariate normal (i.e., Gaussian) distribution

Generate a random set of values within a truncated range

Compute estimates and statistics

Compute congruence coefficient

Compute empirical coverage rates

Bradley's (1978) empirical robustness interval

BF_sim_alternative

(Alternative) Example simulation from Brown and Forsythe (1974)

Compute the empirical detection/rejection rate for Type I errors and Power

Attach objects for easier reference

Perform a test that indicates whether a given Analyse() function should be executed

Example simulation from Brown and Forsythe (1974)

Compute the relative performance behavior of collections of standard errors

Compute the relative efficiency of multiple estimators

Compute the relative standard error ratio

Compute the relative absolute bias of multiple estimators

Probabilistic Bisection Algorithm

Compute the integrated root mean-square error