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SimDesign (version 1.9)

Structure for Organizing Monte Carlo Simulation Designs

Description

Provides tools to help safely and efficiently organize Monte Carlo simulations in R. The package controls the structure and back-end of Monte Carlo simulations by utilizing a general generate-analyse-summarise strategy. The functions provided control common simulation issues such as re-simulating non-convergent results, support parallel back-end and MPI distributed computations, save and restore temporary files, aggregate results across independent nodes, and provide native support for debugging. For a pedagogical introduction to the package refer to Sigal and Chalmers (2016) .

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Install

install.packages('SimDesign')

Monthly Downloads

9,287

Version

1.9

License

GPL (>= 2)

Issues

2

Pull Requests

0

Stars

62

Forks

22

Maintainer

Phil Chalmers

Last Published

January 26th, 2018

Functions in SimDesign (1.9)

ECR

Compute the empirical coverage rate for Type I errors and Power
EDR

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

Compute the relative difference
RE

Compute the relative efficiency of multiple estimators
SimFunctions

Skeleton functions for simulations
SimResults

Function to read in saved simulation results
Analyse

Compute estimates and statistics
Attach

Attach the simulation conditions for easier reference
SimAnova

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

Function to present bootstrap standard errors estimates for Monte Carlo simulation meta-statistics
RMSE

Compute the (normalized) root mean square error
Serlin2000

Empirical detection robustness method suggested by Serlin (2000)
bias

Compute (relative/standardized) bias summary statistic
boot_predict

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

Generate data with the inverse Wishart distribution
rmgh

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

Example simulation from Brown and Forsythe (1974)
BF_sim_alternative

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

Removes/cleans files and folders that have been saved
SimDesign

Structure for Organizing Monte Carlo Simulation Designs
rHeadrick

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

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

Generate a random set of values within a truncated range
runSimulation

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

Generate data
MAE

Compute the mean absolute error
SimShiny

Generate a basic Monte Carlo simulation GUI template
Summarise

Summarise simulated data using various population comparison statistics
add_missing

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

Collapse separate simulation files into a single result
rmvnorm

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

Generate data with the multivariate t distribution
rejectionSampling

Rejection sampling (i.e., accept-reject method) to draw samples from difficult probability density functions
subset.SimDesign

Subset method for SimDesign objects
rint

Generate integer values within specified range