Learn R Programming

⚠️There's a newer version (2.19.2) of this package.Take me there.

SimDesign (version 2.3)

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) , and for an in-depth overview of the package and its design philosophy see Chalmers and Adkins (2020) .

Copy Link

Version

Install

install.packages('SimDesign')

Monthly Downloads

8,027

Version

2.3

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/philchalmers/SimDesign

Homepage

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

Maintainer

Phil Chalmers

Last Published

April 7th, 2021

Functions in SimDesign (2.3)

Example simulation from Brown and Forsythe (1974)

Compute estimates and statistics

Compute the integrated root mean-square error

Compute congruence coefficient

Compute the empirical detection rate for Type I errors and Power

Attach the simulation conditions for easier reference

Compute empirical coverage rates

Compute the mean absolute error

BF_sim_alternative

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

Function for decomposing the simulation into ANOVA-based effect sizes

Removes/cleans files and folders that have been saved

Function to extract extra information from SimDesign objects

Summarise simulated data using various population comparison statistics

Generate integer values within specified range

Empirical detection robustness method suggested by Serlin (2000)

Structure for Organizing Monte Carlo Simulation Designs

Generate a basic Monte Carlo simulation GUI template

rejectionSampling

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

Compute the (normalized) 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

Compute the relative difference

Compute the relative efficiency of multiple estimators

Generate data with the inverse Wishart distribution

Compute the relative absolute bias of multiple estimators

Generate data with the multivariate g-and-h distribution

Compute prediction estimates for the replication size using bootstrap MSE estimates

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

Compute (relative/standardized) bias summary statistic

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

aggregate_simulations

Collapse separate simulation files into a single result

Suppress function messages and Concatenate and Print (cat)

Create the simulation Design object

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

Generate data with the multivariate t distribution

Generate a random set of values within a truncated range

rbind.SimDesign

Combine two separate SimDesign objects by row

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

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