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(): Decompose the simulation into ANOVA-based effects
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
SimErrors(): Extract Simulation Errors
SimExtract(): Extract extra information from SimDesign objects
SimFunctions(): Template-based generation of the Generate-Analyse-Summarise functions
SimRead(): Read simulation files
SimResults(): 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
SimWarnings(): Extract Simulation Warnings
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
descript() get_descriptFuns(): Compute univariate descriptive statistics
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