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SimDesign (version 2.5)

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 issues, such as automatically re-simulating non-convergent results, prevents inadvertently overwriting of 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

6,019

Version

2.5

License

GPL (>= 2)

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Maintainer

Phil Chalmers

Last Published

June 1st, 2021

Functions in SimDesign (2.5)

Analyse

Compute estimates and statistics
Generate

Generate data
IRMSE

Compute the integrated root mean-square error
CC

Compute congruence coefficient
BF_sim_alternative

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

Attach objects for easier reference
EDR

Compute the empirical detection rate for Type I errors and Power
MAE

Compute the mean absolute error
ECR

Compute empirical coverage rates
BF_sim

Example simulation from Brown and Forsythe (1974)
MSRSE

Compute the relative performance behavior of collections of standard errors
SimCheck

Check the status of the simulation's temporary results
SimAnova

Function for decomposing the simulation into ANOVA-based effect sizes
aggregate_simulations

Collapse separate simulation files into a single result
bias

Compute (relative/standardized) bias summary statistic
Serlin2000

Empirical detection robustness method suggested by Serlin (2000)
createDesign

Create the simulation Design object
RMSE

Compute the (normalized) root mean square error
boot_predict

Compute prediction estimates for the replication size using bootstrap MSE estimates
rinvWishart

Generate data with the inverse Wishart distribution
rint

Generate integer values within specified range
rHeadrick

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

Suppress function messages and Concatenate and Print (cat)
rbind.SimDesign

Combine two separate SimDesign objects by row
rValeMaurelli

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

Generate a random set of values within a truncated range
rmvt

Generate data with the multivariate t distribution
SimResults

Function to read in saved simulation results
SimClean

Removes/cleans files and folders that have been saved
SimDesign

Structure for Organizing Monte Carlo Simulation Designs
SimShiny

Generate a basic Monte Carlo simulation GUI template
RD

Compute the relative difference
RE

Compute the relative efficiency of multiple estimators
Summarise

Summarise simulated data using various population comparison statistics
runSimulation

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

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

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

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

Compute the relative absolute bias of multiple estimators
SimExtract

Function to extract extra information from SimDesign objects
SimFunctions

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

Generate data with the multivariate g-and-h distribution
rmvnorm

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