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SimDesign (version 1.14)

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

8,027

Version

1.14

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

June 28th, 2019

Functions in SimDesign (1.14)

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

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

Compute estimates and statistics

Compute the relative performance behavior of collections of standard errors

Generate integer values within specified range

Structure for Organizing Monte Carlo Simulation Designs

Attach the simulation conditions for easier reference

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

aggregate_simulations

Collapse separate simulation files into a single result

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

Example simulation from Brown and Forsythe (1974)

BF_sim_alternative

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

Compute the mean absolute error

subset.SimDesign

Subset method for SimDesign objects

Summarise simulated data using various population comparison statistics

Compute prediction estimates for the replication size using bootstrap MSE estimates

Compute (relative/standardized) bias summary statistic

Generate data with the inverse Wishart distribution

Removes/cleans files and folders that have been saved

Empirical detection robustness method suggested by Serlin (2000)

Suppress function messages and Concatenate and Print (cat)

Function to read in saved simulation results

Generate data with the multivariate t distribution

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

Generate a basic Monte Carlo simulation GUI template

Compute the integrated root mean-square error

Compute the relative absolute of multiple estimators

Generate data with the multivariate g-and-h distribution

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

Compute the relative difference

Function for decomposing the simulation into ANOVA-based effect sizes

Generate a random set of values within a truncated range

Skeleton functions for simulations

Compute empirical coverage rates

Compute the empirical detection rate for Type I errors and Power

Compute the relative efficiency of multiple estimators

Compute the (normalized) root mean square error

rejectionSampling

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

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