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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

6,019

Version

2.20.0

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

July 16th, 2025

Functions in SimDesign (2.20.0)

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

Compute the relative absolute bias of multiple estimators

Compute the relative difference

Compute the integrated root mean-square error

Compute the (normalized) root mean square error

Robbins-Monro (1951) stochastic root-finding algorithm

Check for missing files in array simulations

Removes/cleans files and folders that have been saved

Summarise simulated data using various population comparison statistics

Structure for Organizing Monte Carlo Simulation Designs

Collapse separate simulation files into a single result

One Dimensional Root (Zero) Finding in Simulation Experiments

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

Surrogate Function Approximation via the Generalized Linear Model

Compute (relative/standardized) bias summary statistic

new_PushbulletNotifier

Create a Pushbullet Notifier

Function to read in saved simulation results

Function for decomposing the simulation into ANOVA-based effect sizes

Generate random seeds

Compute the relative efficiency of multiple estimators

Function to extract extra information from SimDesign objects

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

Form Column Standard Deviation and Variances

Create the simulation design object

new_TelegramNotifier

Create a Telegram Notifier

Empirical detection robustness method suggested by Serlin (2000)

Generate a basic Monte Carlo simulation GUI template

Manage specific warning messages

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

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

rbind.SimDesign

Combine two separate SimDesign objects by row

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

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

Compute prediction estimates for the replication size using bootstrap MSE estimates

Suppress verbose function messages

notify.TelegramNotifier

S3 method to send notifications through the Telegram API.

Generate a random set of values within a truncated range

Auto-named Concatenation of Vector or List

runArraySimulation

Run a Monte Carlo simulation using array job submissions per condition

rejectionSampling

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

Generate integer values within specified range

listAvailableNotifiers

List All Available Notifiers

Increase the intensity or suppress the output of an observed message

Generate data with the inverse Wishart distribution

notify.PushbulletNotifier

S3 method to send notifications via Pushbullet

Send a simulation notification

Format time string to suitable numeric output

Generate data with the multivariate g-and-h distribution

Run a Monte Carlo simulation given conditions and simulation functions

clusterSetRNGSubStream

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

expandReplications

Expand the replications to match expandDesign

Expand the simulation design object for array computing

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

Generate data with the multivariate t distribution

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

Compute estimates and statistics

BF_sim_alternative

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

Compute congruence coefficient

Attach objects for easier reference

Compute empirical coverage rates

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

Bradley's (1978) empirical robustness interval

Compute the relative standard error ratio

Compute the mean absolute error

Example simulation from Brown and Forsythe (1974)

Probabilistic Bisection Algorithm

Compute the relative performance behavior of collections of standard errors