Learn R Programming

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.

Copy Link

Version

Install

install.packages('SimDesign')

Monthly Downloads

8,027

Version

2.19.2

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

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

Homepage

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

Maintainer

Phil Chalmers

Last Published

March 28th, 2025

Functions in SimDesign (2.19.2)

Compute the relative difference

Compute the relative efficiency of multiple estimators

Probabilistic Bisection Algorithm

Compute the relative performance behavior of collections of standard errors

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

Compute the mean absolute error

Compute the integrated root mean-square error

Compute the (normalized) root mean square error

Compute the relative absolute bias of multiple estimators

Compute the relative standard error ratio

Check for missing files in array simulations

Function to extract extra information from SimDesign objects

Robbins-Monro (1951) stochastic root-finding algorithm

Function for decomposing the simulation into ANOVA-based effect sizes

Empirical detection robustness method suggested by Serlin (2000)

Collapse separate simulation files into a single result

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

Removes/cleans files and folders that have been saved

Surrogate Function Approximation via the Generalized Linear Model

Structure for Organizing Monte Carlo Simulation Designs

Compute prediction estimates for the replication size using bootstrap MSE estimates

Function to read in saved simulation results

One Dimensional Root (Zero) Finding in Simulation Experiments

Generate a basic Monte Carlo simulation GUI template

Compute (relative/standardized) bias summary statistic

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

Form Column Standard Deviation and Variances

Create the simulation design object

clusterSetRNGSubStream

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

Summarise simulated data using various population comparison statistics

Generate random seeds

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

listAvailableNotifiers

List All Available Notifiers

Auto-named Concatenation of Vector or List

new_PushbulletNotifier

Create a Pushbullet Notifier

Manage specific warning messages

Increase the intensity or suppress the output of an observed message

notify.PushbulletNotifier

S3 method to send notifications via Pushbullet

new_TelegramNotifier

Create a Telegram Notifier

Suppress verbose function messages

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

Send a simulation notification

Create the simulation design object

notify.TelegramNotifier

S3 method to send notifications through the Telegram API.

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

Generate integer values within specified range

Generate data with the inverse Wishart distribution

rbind.SimDesign

Combine two separate SimDesign objects by row

rejectionSampling

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

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

Format time string to suitable numeric output

Generate data with the multivariate g-and-h distribution

Generate a random set of values within a truncated range

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

Generate data with the multivariate t distribution

runArraySimulation

Run a Monte Carlo simulation using array job submissions per condition

Run a Monte Carlo simulation given conditions and simulation functions

BF_sim_alternative

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

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

Compute congruence coefficient

Attach objects for easier reference

Compute empirical coverage rates

Compute estimates and statistics

Example simulation from Brown and Forsythe (1974)

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

Bradley's (1978) empirical robustness interval