Compute the relative difference
Compute the relative efficiency of multiple estimators
Check the status of the simulation's temporary results
Removes/cleans files and folders that have been saved
(Alternative) Example simulation from Brown and Forsythe (1974)
Summarise simulated data using various population comparison statistics
Add missing values to a vector given a MCAR, MAR, or MNAR scheme
Bradley's (1978) empirical robustness interval
Run a summarise step for results that have been saved to the hard drive
Attach objects for easier reference
Compute congruence coefficient
Example simulation from Brown and Forsythe (1974)
Compute the (normalized) root mean square error
Generate integer values within specified range
Rejection sampling (i.e., accept-reject method)
Generate data with the inverse Wishart distribution
Compute the relative standard error ratio
Compute the mean absolute error
Compute the relative performance behavior of collections of standard errors
Compute empirical coverage rates
Robbins-Monro (1951) stochastic root-finding algorithm
Template-based generation of the Generate-Analyse-Summarise functions
Empirical detection robustness method suggested by Serlin (2000)
Function to read in saved simulation results
Function for decomposing the simulation into ANOVA-based effect sizes
Surrogate Function Approximation via the Generalized Linear Model
Compute prediction estimates for the replication size using bootstrap MSE estimates
Compute the empirical detection/rejection rate for Type I errors and Power
Generate data
Wrapper to convert all/specific warning messages to errors
Create the simulation design object
Generate non-normal data with Vale & Maurelli's (1983) method
Form Column Standard Deviation and Variances
Perform a test that indicates whether a given Generate() function should be executed
Get job array ID (e.g., from SLURM or other HPC array distributions)
Combine two separate SimDesign objects by row
Run a Monte Carlo simulation using array job submissions per condition
Run a Monte Carlo simulation given conditions and simulation functions
Compute the integrated root mean-square error
Auto-named Concatenation of Vector or List
Compute estimates and statistics
Perform a test that indicates whether a given Analyse() function should be executed
Create the simulation design object
Structure for Organizing Monte Carlo Simulation Designs
Function to extract extra information from SimDesign objects
Generate data with the multivariate t distribution
Collapse separate simulation files into a single result
Generate random seeds
Compute (relative/standardized) bias summary statistic
Generate a random set of values within a truncated range
Probabilistic Bisection Algorithm
Compute the relative absolute bias of multiple estimators
Generate a basic Monte Carlo simulation GUI template
Suppress function messages and Concatenate and Print (cat)
One Dimensional Root (Zero) Finding in Simulation Experiments
Generate non-normal data with Headrick's (2002) method
Generate data with the multivariate g-and-h distribution
Generate data with the multivariate normal (i.e., Gaussian) distribution