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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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Install

install.packages('SimDesign')

Monthly Downloads

6,641

Version

2.18

License

GPL (>= 2)

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Repository

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

Homepage

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

Maintainer

Last Published

December 14th, 2024

Functions in SimDesign (2.18)

Attach objects for easier reference

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

BF_sim_alternative

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

Compute the relative performance behavior of collections of standard errors

Compute the mean absolute error

Probabilistic Bisection Algorithm

Compute the relative absolute bias of multiple estimators

Compute the relative difference

Compute the relative standard error ratio

Compute the (normalized) root mean square error

Compute the relative efficiency of multiple estimators

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

Compute the integrated root mean-square error

Surrogate Function Approximation via the Generalized Linear Model

Empirical detection robustness method suggested by Serlin (2000)

Robbins-Monro (1951) stochastic root-finding algorithm

Collapse separate simulation files into a single result

Removes/cleans files and folders that have been saved

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

Check for missing files in array simulations

Function for decomposing the simulation into ANOVA-based effect sizes

Function to extract extra information from SimDesign objects

Structure for Organizing Monte Carlo Simulation Designs

Function to read in saved simulation results

Generate a basic Monte Carlo simulation GUI template

Create the simulation design object

Compute prediction estimates for the replication size using bootstrap MSE estimates

Form Column Standard Deviation and Variances

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

Compute (relative/standardized) bias summary statistic

One Dimensional Root (Zero) Finding in Simulation Experiments

clusterSetRNGSubStream

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

Create the simulation design object

Summarise simulated data using various population comparison statistics

Manage specific warning messages

Generate random seeds

Auto-named Concatenation of Vector or List

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

rbind.SimDesign

Combine two separate SimDesign objects by row

Increase the intensity or suppress the output of an observed message

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

Suppress verbose function messages

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

Generate data with the inverse Wishart distribution

Generate integer values within specified range

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

Generate a random set of values within a truncated range

Run a Monte Carlo simulation given conditions and simulation functions

Generate data with the multivariate t distribution

runArraySimulation

Run a Monte Carlo simulation using array job submissions per condition

Generate data with the multivariate g-and-h distribution

rejectionSampling

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

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

Format time string to suitable numeric output

Compute estimates and statistics

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

Compute empirical coverage rates

Example simulation from Brown and Forsythe (1974)

Bradley's (1978) empirical robustness interval

Compute congruence coefficient