Structure for Organizing Monte Carlo Simulation Designs
Add missing values to a vector given a MCAR, MAR, or MNAR scheme
Compute estimates and statistics
Perform a test that indicates whether a given Analyse() function sho...
Attach objects for easier reference
Compute (relative/standardized) bias summary statistic
Compute prediction estimates for the replication size using bootstrap ...
Bradley's (1978) empirical robustness interval
Compute congruence coefficient
Set RNG sub-stream for Pierre L'Ecuyer's RngStreams
Form Column Standard Deviation and Variances
Create the simulation design object
Compute empirical coverage rates
Compute the empirical detection/rejection rate for Type I errors and P...
Expand the simulation design object for array computing
Expand the replications to match expandDesign
Generate data
Perform a test that indicates whether a given Generate() function sh...
Generate random seeds
Get job array ID (e.g., from SLURM or other HPC array distributions)
Compute the integrated root mean-square error
List All Available Notifiers
Compute the mean absolute error
Increase the intensity or suppress the output of an observed message
Manage specific warning messages
Compute the relative performance behavior of collections of standard e...
Auto-named Concatenation of Vector or List
Create a Pushbullet Notifier
Create a Telegram Notifier
S3 method to send notifications via Pushbullet
Send a simulation notification
S3 method to send notifications through the Telegram API.
Probabilistic Bisection Algorithm
Suppress verbose function messages
Compute the relative absolute bias of multiple estimators
Combine two separate SimDesign objects by row
Compute the relative difference
Compute the relative efficiency of multiple estimators
Rejection sampling (i.e., accept-reject method)
Run a summarise step for results that have been saved to the hard driv...
Generate non-normal data with Headrick's (2002) method
Generate integer values within specified range
Generate data with the inverse Wishart distribution
Generate data with the multivariate g-and-h distribution
Compute the (normalized) root mean square error
Generate data with the multivariate normal (i.e., Gaussian) distributi...
Generate data with the multivariate t distribution
Robbins-Monro (1951) stochastic root-finding algorithm
Compute the relative standard error ratio
Generate a random set of values within a truncated range
Run a Monte Carlo simulation using array job submissions per condition
Run a Monte Carlo simulation given conditions and simulation functions
Generate non-normal data with Vale & Maurelli's (1983) method
Empirical detection robustness method suggested by Serlin (2000)
Surrogate Function Approximation via the Generalized Linear Model
Function for decomposing the simulation into ANOVA-based effect sizes
Check for missing files in array simulations
Removes/cleans files and folders that have been saved
Collapse separate simulation files into a single result
Structure for Organizing Monte Carlo Simulation Designs
Function to extract extra information from SimDesign objects
Template-based generation of the Generate-Analyse-Summarise functions
Function to read in saved simulation results
Generate a basic Monte Carlo simulation GUI template
One Dimensional Root (Zero) Finding in Simulation Experiments
Summarise simulated data using various population comparison statistic...
Format time string to suitable numeric output
Provides tools to safely and efficiently organize and execute Monte Carlo simulation experiments in R. The package controls the structure and back-end of Monte Carlo simulation experiments by utilizing a generate-analyse-summarise workflow. The workflow safeguards against common simulation coding issues, such as automatically re-simulating non-convergent results, prevents inadvertently overwriting simulation files, catches error and warning messages during execution, implicitly supports parallel processing with high-quality random number generation, and provides tools for managing high-performance computing (HPC) array jobs submitted to schedulers such as SLURM. For a pedagogical introduction to the package see Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>. For a more in-depth overview of the package and its design philosophy see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.