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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 remotes, type the following (assuming you have already installed the remotes package from CRAN).

library('remotes')
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

9,905

Version

2.25

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

March 31st, 2026

Functions in SimDesign (2.25)

Compute the relative efficiency of multiple estimators

Compute the relative difference

Probabilistic Bisection Algorithm

Compute the relative standard error ratio

Compute the relative absolute bias of multiple estimators

Compute the integrated root mean-square error

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

Compute the mean absolute error

Compute the relative performance behavior of collections of standard errors

Check for missing files in array simulations

Robbins-Monro (1951) stochastic root-finding algorithm

Extract extra information from SimDesign objects

Extract Simulation Errors

Empirical detection robustness method suggested by Serlin (2000)

Decompose the simulation into ANOVA-based effects

Removes/cleans files and folders that have been saved

Structure for Organizing Monte Carlo Simulation Designs

Collapse separate simulation files into a single result

Read in saved simulation results

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

Summarise simulated data using various population comparison statistics

Generate a basic Monte Carlo simulation GUI template

Surrogate Function Approximation via the Generalized Linear Model

Compute prediction estimates for the replication size using bootstrap MSE estimates

Compute (relative/standardized) bias summary statistic

Extract Simulation Warnings

One Dimensional Root (Zero) Finding in Simulation Experiments

Read simulation files

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

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

Generate random seeds

Increase the intensity or suppress the output of an observed message

listAvailableNotifiers

List All Available Notifiers

Form Column Standard Deviation and Variances

clusterSetRNGSubStream

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

Manage specific warning messages

new_PushbulletNotifier

Create a Pushbullet Notifier

new_TelegramNotifier

Create a Telegram Notifier

Create the simulation design object

Compute univariate descriptive statistics

expandReplications

Expand the replications to match expandDesign

Expand the simulation design object for array computing

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

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

Auto-named Concatenation of Vector or List

notify.PushbulletNotifier

S3 method to send notifications via Pushbullet

Suppress verbose function messages

Generate data with the multivariate g-and-h distribution

rejectionSampling

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

Generate data with the inverse Wishart distribution

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

rbind.SimDesign

Combine two separate SimDesign objects by row

Objects exported from other packages

notify.TelegramNotifier

S3 method to send notifications through the Telegram API.

Send a simulation notification

Generate data with the multivariate t distribution

Generate a random set of values within a truncated range

Generate integer values within specified range

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

runArraySimulation

Run a Monte Carlo simulation using array job submissions per condition

Format time string to suitable numeric output

Run a Monte Carlo simulation given conditions and simulation functions