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SimDesign (version 2.7)

Structure for Organizing Monte Carlo Simulation Designs

Description

Provides tools to safely and efficiently organize and execute Monte Carlo simulation experiments in R. The package controls the structure and back-end of Monte Carlo simulation experiments by utilizing a generate-analyse-summarise workflow. The workflow safeguards against common simulation coding issues, such as automatically re-simulating non-convergent results, prevents inadvertently overwriting simulation files, catches error and warning messages during execution, and implicitly supports parallel processing. For a pedagogical introduction to the package see Sigal and Chalmers (2016) . For a more in-depth overview of the package and its design philosophy see Chalmers and Adkins (2020) .

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Install

install.packages('SimDesign')

Monthly Downloads

6,711

Version

2.7

License

GPL (>= 2)

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Repository

https://github.com/philchalmers/SimDesign

Homepage

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

Maintainer

Phil Chalmers

Last Published

August 13th, 2021

Functions in SimDesign (2.7)

Compute estimates and statistics

Compute the empirical detection rate for Type I errors and Power

Compute empirical coverage rates

Compute the relative efficiency of multiple estimators

Compute the (normalized) root mean square error

Compute the mean absolute error

Compute the integrated root mean-square error

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

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

Compute the relative performance behavior of collections of standard errors

Function to read in saved simulation results

Structure for Organizing Monte Carlo Simulation Designs

Function to extract extra information from SimDesign objects

aggregate_simulations

Collapse separate simulation files into a single result

rbind.SimDesign

Combine two separate SimDesign objects by row

Compute the relative absolute bias of multiple estimators

Generate a basic Monte Carlo simulation GUI template

Compute the relative difference

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

Summarise simulated data using various population comparison statistics

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

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

Generate data with the multivariate g-and-h distribution

Generate data with the inverse Wishart distribution

Suppress function messages and Concatenate and Print (cat)

Create the simulation Design object

Generate data with the multivariate t distribution

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

Attach objects for easier reference

Example simulation from Brown and Forsythe (1974)

Compute congruence coefficient

Removes/cleans files and folders that have been saved

BF_sim_alternative

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

Check the status of the simulation's temporary results

Function for decomposing the simulation into ANOVA-based effect sizes

Compute (relative/standardized) bias summary statistic

Compute prediction estimates for the replication size using bootstrap MSE estimates

Empirical detection robustness method suggested by Serlin (2000)

rejectionSampling

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

Generate integer values within specified range

Run a Monte Carlo simulation given a data.frame of conditions and simulation functions

Generate a random set of values within a truncated range

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