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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 discription 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

2.15.1

License

GPL (>= 2)

Issues

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Repository

https://github.com/philchalmers/SimDesign

Homepage

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

Maintainer

Phil Chalmers

Last Published

April 24th, 2024

Functions in SimDesign (2.15.1)

Compute the mean absolute error

Compute the relative difference

Compute the relative efficiency of multiple estimators

Compute the relative performance behavior of collections of standard errors

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

Function to read in saved simulation results

Empirical detection robustness method suggested by Serlin (2000)

Example simulation from Brown and Forsythe (1974)

Function for decomposing the simulation into ANOVA-based effect sizes

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

Check the status of the simulation's temporary results

Compute the integrated root mean-square error

Removes/cleans files and folders that have been saved

Compute the relative absolute bias of multiple estimators

Probabilistic Bisection Algorithm

Summarise simulated data using various population comparison statistics

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

Create the simulation design object

Compute prediction estimates for the replication size using bootstrap MSE estimates

rbind.SimDesign

Combine two separate SimDesign objects by row

Form Column Standard Deviation and Variances

Robbins-Monro (1951) stochastic root-finding algorithm

Suppress function messages and Concatenate and Print (cat)

Generate a basic Monte Carlo simulation GUI template

Surrogate Function Approximation via the Generalized Linear Model

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

One Dimensional Root (Zero) Finding in Simulation Experiments

Generate data with the inverse Wishart distribution

Generate integer values within specified range

Generate random seeds

Generate a random set of values within a truncated range

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

Generate data with the multivariate t distribution

Compute (relative/standardized) bias summary statistic

Compute the relative standard error ratio

aggregate_simulations

Collapse separate simulation files into a single result

rejectionSampling

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

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

Compute the (normalized) root mean square error

runArraySimulation

Run a Monte Carlo simulation using array job submissions per condition

Generate data with the multivariate g-and-h distribution

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

convertWarnings

Wrapper to convert all/specific warning messages to errors

Function to extract extra information from SimDesign objects

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

Auto-named Concatenation of Vector or List

Create the simulation design object

Structure for Organizing Monte Carlo Simulation Designs

Run a Monte Carlo simulation given conditions and simulation functions

BF_sim_alternative

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

Compute estimates and statistics

Compute congruence coefficient

Bradley's (1978) empirical robustness interval

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

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

Attach objects for easier reference

Compute empirical coverage rates