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