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>): Create the simulation design object

expandDesign(): Create the simulation design object

genSeeds() gen_seeds(): Generate random seeds

getArrayID(): Get job array ID (e.g., from SLURM or other HPC array distributions)

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

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