Learn R Programming

⚠️There's a newer version (2.25) of this package.Take me there.

SimDesign (version 1.3)

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.

Copy Link

Version

Install

install.packages('SimDesign')

Monthly Downloads

9,649

Version

1.3

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Maintainer

Phil Chalmers

Last Published

September 26th, 2016

Functions in SimDesign (1.3)

bias

Compute (relative) bias summary statistic
ECR

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

Generate data
BF_sim_alternative

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

Example simulation from Brown and Forsythe (1974)
Analyse

Compute estimates and statistics
EDR

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

Attach the simulation conditions for easier reference
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
MAE

Compute the mean absolute error
RE

Compute the relative efficiency of multiple estimators
SimDesign

Structure for Organizing Monte Carlo Simulation Designs
SimFunctions

Skeleton functions for simulations
SimAnova

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

Summarise simulated data using various population comparison statistics
SimClean

Removes/cleans files and folders that have been saved
RMSE

Compute the (normalized) root mean square error
runSimulation

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