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SimDesign (version 2.0.1)

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

7,495

Version

2.0.1

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

January 20th, 2020

Functions in SimDesign (2.0.1)

Compute the integrated root mean-square error

Compute the (normalized) root mean square error

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

Compute the relative efficiency of multiple estimators

Generate data with the multivariate t distribution

Create the simulation Design object

Suppress function messages and Concatenate and Print (cat)

Compute the relative difference

Compute (relative/standardized) bias summary statistic

Compute the relative absolute bias of multiple estimators

Structure for Organizing Monte Carlo Simulation Designs

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

Removes/cleans files and folders that have been saved

Function to read in saved simulation results

Skeleton functions for simulations

Generate a random set of values within a truncated range

Generate data with the inverse Wishart distribution

Generate data with the multivariate g-and-h distribution

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

Function for decomposing the simulation into ANOVA-based effect sizes

Empirical detection robustness method suggested by Serlin (2000)

Function to extract extra information from SimDesign objects

aggregate_simulations

Collapse separate simulation files into a single result

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

Compute prediction estimates for the replication size using bootstrap MSE estimates

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

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

Generate a basic Monte Carlo simulation GUI template

rejectionSampling

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

Generate integer values within specified range

Summarise simulated data using various population comparison statistics

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

rbind.SimDesign

Combine two separate SimDesign objects by row

Compute estimates and statistics

Compute the empirical detection rate for Type I errors and Power

Compute the mean absolute error

Compute empirical coverage rates

Example simulation from Brown and Forsythe (1974)

Compute the relative performance behavior of collections of standard errors

Attach the simulation conditions for easier reference

BF_sim_alternative

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