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SimDesign (version 2.8)

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

8,027

Version

2.8

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

December 8th, 2021

Functions in SimDesign (2.8)

Compute the integrated root mean-square error

Compute estimates and statistics

Compute congruence coefficient

Example simulation from Brown and Forsythe (1974)

Attach objects for easier reference

Compute empirical coverage rates

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

Compute the empirical detection rate for Type I errors and Power

BF_sim_alternative

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

Compute the mean absolute error

Compute the relative standard error ratio

Check the status of the simulation's temporary results

Function for decomposing the simulation into ANOVA-based effect sizes

Compute the (normalized) root mean square error

Compute the relative efficiency of multiple estimators

Removes/cleans files and folders that have been saved

Compute (relative/standardized) bias summary statistic

Compute the relative performance behavior of collections of standard errors

aggregate_simulations

Collapse separate simulation files into a single result

Structure for Organizing Monte Carlo Simulation Designs

Compute the relative absolute bias of multiple estimators

Compute the relative difference

Summarise simulated data using various population comparison statistics

Empirical detection robustness method suggested by Serlin (2000)

Function to read in saved simulation results

Generate a basic Monte Carlo simulation GUI template

Compute prediction estimates for the replication size using bootstrap MSE estimates

Create the simulation Design object

Generate integer values within specified range

Generate data with the inverse Wishart distribution

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

rbind.SimDesign

Combine two separate SimDesign objects by row

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

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

Suppress function messages and Concatenate and Print (cat)

Function to extract extra information from SimDesign objects

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

rejectionSampling

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

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

Generate data with the multivariate t distribution

Generate a random set of values within a truncated range

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

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

Generate data with the multivariate g-and-h distribution