Package index • SimDesign

All functions
`Analyse()`	Compute estimates and statistics
`AnalyseIf()`	Perform a test that indicates whether a given `Analyse()` function should be executed
`Attach()`	Attach objects for easier reference
`BF_sim`	Example simulation from Brown and Forsythe (1974)
`BF_sim_alternative`	(Alternative) Example simulation from Brown and Forsythe (1974)
`Bradley1978()`	Bradley's (1978) empirical robustness interval
`CC()`	Compute congruence coefficient
`ECR()`	Compute empirical coverage rates
`EDR()`	Compute the empirical detection/rejection rate for Type I errors and Power
`Generate()`	Generate data
`GenerateIf()`	Perform a test that indicates whether a given `Generate()` function should be executed
`IRMSE()`	Compute the integrated root mean-square error
`MAE()`	Compute the mean absolute error
`MSRSE()`	Compute the relative performance behavior of collections of standard errors
`PBA()` `print(<PBA>)` `plot(<PBA>)`	Probabilistic Bisection Algorithm
`RAB()`	Compute the relative absolute bias of multiple estimators
`RD()`	Compute the relative difference
`RE()`	Compute the relative efficiency of multiple estimators
`RMSE()` `RMSD()`	Compute the (normalized) root mean square error
`RSE()`	Compute the relative standard error ratio
`RobbinsMonro()` `print(<RM>)` `plot(<RM>)`	Robbins-Monro (1951) stochastic root-finding algorithm
`SFA()` `print(<SFA>)`	Surrogate Function Approximation via the Generalized Linear Model
`Serlin2000()`	Empirical detection robustness method suggested by Serlin (2000)
`SimAnova()`	Function for decomposing the simulation into ANOVA-based effect sizes
`SimCheck()`	Check for missing files in array simulations
`SimClean()`	Removes/cleans files and folders that have been saved
`SimCollect()` `aggregate_simulations()`	Collapse separate simulation files into a single result
`SimDesign` `SimDesign-package`	Structure for Organizing Monte Carlo Simulation Designs
`SimExtract()`	Function to extract extra information from SimDesign objects
`SimFunctions()`	Template-based generation of the Generate-Analyse-Summarise functions
`SimResults()`	Function to read in saved simulation results
`SimShiny()`	Generate a basic Monte Carlo simulation GUI template
`SimSolve()` `summary(<SimSolve>)` `plot(<SimSolve>)`	One Dimensional Root (Zero) Finding in Simulation Experiments
`Summarise()`	Summarise simulated data using various population comparison statistics
`addMissing()`	Add missing values to a vector given a MCAR, MAR, or MNAR scheme
`bias()`	Compute (relative/standardized) bias summary statistic
`bootPredict()` `boot_predict()`	Compute prediction estimates for the replication size using bootstrap MSE estimates
`clusterSetRNGSubStream()`	Set RNG sub-stream for Pierre L'Ecuyer's RngStreams
`colVars()` `colSDs()`	Form Column Standard Deviation and Variances
`createDesign()` `print(<Design>)` `[`(<Design>) `rbindDesign()`	Create the simulation design object
`expandDesign()`	Expand the simulation design object for array computing
`expandReplications()`	Expand the replications to match `expandDesign`
`genSeeds()` `gen_seeds()`	Generate random seeds
`getArrayID()`	Get job array ID (e.g., from SLURM or other HPC array distributions)
`listAvailableNotifiers()`	List All Available Notifiers
`manageMessages()`	Increase the intensity or suppress the output of an observed message
`manageWarnings()`	Manage specific warning messages
`nc()`	Auto-named Concatenation of Vector or List
`new_PushbulletNotifier()`	Create a Pushbullet Notifier
`new_TelegramNotifier()`	Create a Telegram Notifier
`quiet()`	Suppress verbose function messages
`rHeadrick()`	Generate non-normal data with Headrick's (2002) method
`rValeMaurelli()`	Generate non-normal data with Vale & Maurelli's (1983) method
`rbind(<SimDesign>)`	Combine two separate SimDesign objects by row
`reSummarise()`	Run a summarise step for results that have been saved to the hard drive
`rejectionSampling()`	Rejection sampling (i.e., accept-reject method)
`rint()`	Generate integer values within specified range
`rinvWishart()`	Generate data with the inverse Wishart distribution
`rmgh()`	Generate data with the multivariate g-and-h distribution
`rmvnorm()`	Generate data with the multivariate normal (i.e., Gaussian) distribution
`rmvt()`	Generate data with the multivariate t distribution
`rtruncate()`	Generate a random set of values within a truncated range
`runArraySimulation()`	Run a Monte Carlo simulation using array job submissions per condition
`runSimulation()` `summary(<SimDesign>)` `print(<SimDesign>)`	Run a Monte Carlo simulation given conditions and simulation functions
`timeFormater()`	Format time string to suitable numeric output

Reference

All functions