Package: SimDesign 2.17.1

SimDesign: Structure for Organizing Monte Carlo Simulation Designs

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>.

Authors:Phil Chalmers [aut, cre], Matthew Sigal [ctb], Ogreden Oguzhan [ctb], Mikko Ronkko [ctb]

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SimDesign.pdf |SimDesign.html✨
SimDesign/json (API)
NEWS

# Install 'SimDesign' in R:
install.packages('SimDesign', repos = c('https://philchalmers.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/philchalmers/simdesign/issues

Datasets:

On CRAN:

monte-carlo-simulationsimulationsimulation-framework

60 exports 61 stars 5.77 score 51 dependencies 42 dependents 4 mentions 223 scripts 8.0k downloads

Last updated 1 months agofrom:6644e6adc0. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 15 2024
R-4.5-winOKSep 15 2024
R-4.5-linuxOKSep 15 2024
R-4.4-winOKSep 15 2024
R-4.4-macOKSep 15 2024
R-4.3-winOKSep 15 2024
R-4.3-macOKSep 15 2024

Exports:add_missingaddMissingaggregate_simulationsAnalyseIfAttachbiasboot_predictbootPredictBradley1978CCclusterSetRNGSubStreamcolSDscolVarscreateDesignECREDRERRexpandDesignGenerateIfgenSeedsgetArrayIDIRMSEMAEmanageMessagesmanageWarningsMSRSEncPBAquietRABRDRErejectionSamplingreSummariserHeadrickrintrinvWishartrmghRMSDRMSErmvnormrmvtRobbinsMonroRSErtruncaterunArraySimulationrunSimulationrValeMaurelliSerlin2000SFASimAnovaSimCheckSimCleanSimCollectSimExtractSimFunctionsSimResultsSimShinySimSolvetimeFormater

Dependencies:audiobeeprbriocallrclicodetoolscrayoncurldescdiffobjdigestdplyrevaluatefansifsfuturefuture.applygenericsglobalsgluejsonlitelifecyclelistenvmagrittrparallellypbapplypillarpkgbuildpkgconfigpkgloadpraiseprocessxprogressrpsR.methodsS3R.ooR.utilsR6rematch2rlangrprojrootRPushbulletsessioninfosnowtestthattibbletidyselectutf8vctrswaldowithr

Exporting objects and functions from the workspace

Phil Chalmers

Rendered fromFixed_obj_fun.Rmdusingknitr::rmarkdownon Sep 15 2024.

Last update: 2024-05-22
Started: 2017-10-25

Distributing jobs for high-performance computing (HPC) clusters (e.g., via Slurm)

Phil Chalmers

Rendered fromHPC-computing.Rmdusingknitr::rmarkdownon Sep 15 2024.

Last update: 2024-08-16
Started: 2024-04-04

Introduction to the SimDesign package

Phil Chalmers

Rendered fromSimDesign-intro.Rmdusingknitr::rmarkdownon Sep 15 2024.

Last update: 2024-05-22
Started: 2017-10-25

Managing warning and error messages

Phil Chalmers

Rendered fromCatch_errors.Rmdusingknitr::rmarkdownon Sep 15 2024.

Last update: 2024-07-14
Started: 2017-10-25

Multiple analysis functions

Phil Chalmers

Rendered fromMultipleAnalyses.Rmdusingknitr::rmarkdownon Sep 15 2024.

Last update: 2024-05-22
Started: 2021-07-29

Parallel computing information

Phil Chalmers

Rendered fromParallel-computing.Rmdusingknitr::rmarkdownon Sep 15 2024.

Last update: 2024-07-31
Started: 2018-05-15

Saving simulation results and state

Phil Chalmers

Rendered fromSaving-results.Rmdusingknitr::rmarkdownon Sep 15 2024.

Last update: 2024-05-22
Started: 2017-10-25

Readme and manuals

Help Manual

Help pageTopics
Add missing values to a vector given a MCAR, MAR, or MNAR schemeaddMissing add_missing
Compute estimates and statisticsAnalyse
Perform a test that indicates whether a given 'Analyse()' function should be executedAnalyseIf
Attach objects for easier referenceAttach
Example simulation from Brown and Forsythe (1974)BF_sim
(Alternative) Example simulation from Brown and Forsythe (1974)BF_sim_alternative
Compute (relative/standardized) bias summary statisticbias
Compute prediction estimates for the replication size using bootstrap MSE estimatesbootPredict boot_predict
Bradley's (1978) empirical robustness intervalBradley1978
Compute congruence coefficientCC
Set RNG sub-stream for Pierre L'Ecuyer's RngStreamsclusterSetRNGSubStream
Form Column Standard Deviation and VariancescolSDs colVars
Create the simulation design objectcreateDesign print.Design
Compute empirical coverage ratesECR
Compute the empirical detection/rejection rate for Type I errors and PowerEDR ERR
Create the simulation design objectexpandDesign
Generate dataGenerate
Perform a test that indicates whether a given 'Generate()' function should be executedGenerateIf
Generate random seedsgenSeeds gen_seeds
Get job array ID (e.g., from SLURM or other HPC array distributions)getArrayID
Compute the integrated root mean-square errorIRMSE
Compute the mean absolute errorMAE
Increase the intensity or suppress the output of an observed messagemanageMessages
Manage specific warning messagesmanageWarnings
Compute the relative performance behavior of collections of standard errorsMSRSE
Auto-named Concatenation of Vector or Listnc
Probabilistic Bisection AlgorithmPBA plot.PBA print.PBA
Suppress verbose function messagesquiet
Compute the relative absolute bias of multiple estimatorsRAB
Combine two separate SimDesign objects by rowrbind.SimDesign
Compute the relative differenceRD
Compute the relative efficiency of multiple estimatorsRE
Rejection sampling (i.e., accept-reject method)rejectionSampling
Run a summarise step for results that have been saved to the hard drivereSummarise
Generate non-normal data with Headrick's (2002) methodrHeadrick
Generate integer values within specified rangerint
Generate data with the inverse Wishart distributionrinvWishart
Generate data with the multivariate g-and-h distributionrmgh
Compute the (normalized) root mean square errorRMSD RMSE
Generate data with the multivariate normal (i.e., Gaussian) distributionrmvnorm
Generate data with the multivariate t distributionrmvt
Robbins-Monro (1951) stochastic root-finding algorithmplot.RM print.RM RobbinsMonro
Compute the relative standard error ratioRSE
Generate a random set of values within a truncated rangertruncate
Run a Monte Carlo simulation using array job submissions per conditionrunArraySimulation
Run a Monte Carlo simulation given conditions and simulation functionsprint.SimDesign runSimulation summary.SimDesign
Generate non-normal data with Vale & Maurelli's (1983) methodrValeMaurelli
Empirical detection robustness method suggested by Serlin (2000)Serlin2000
Surrogate Function Approximation via the Generalized Linear Modelprint.SFA SFA
Function for decomposing the simulation into ANOVA-based effect sizesSimAnova
Check for missing files in array simulationsSimCheck
Removes/cleans files and folders that have been savedSimClean
Collapse separate simulation files into a single resultaggregate_simulations SimCollect
Structure for Organizing Monte Carlo Simulation DesignsSimDesign-package SimDesign
Function to extract extra information from SimDesign objectsSimExtract
Template-based generation of the Generate-Analyse-Summarise functionsSimFunctions
Function to read in saved simulation resultsSimResults
Generate a basic Monte Carlo simulation GUI templateSimShiny
One Dimensional Root (Zero) Finding in Simulation Experimentsplot.SimSolve SimSolve summary.SimSolve
Summarise simulated data using various population comparison statisticsSummarise
Format time string to suitable numeric outputtimeFormater