Helper Functions for Simulation Studies
Calculate one or multiple bootstrap confidence intervals
Calculate one or multiple bootstrap p-values
Bundle functions into a simulation driver function
Calculate absolute performance criteria and MCSE
Calculate confidence interval coverage, width and MCSE
Calculate rejection rate and MCSE
Calculate jack-knife Monte Carlo SE for variance estimators
Calculate relative performance criteria and MCSE
Open a simulation skeleton
Evaluate a simulation function on each row of a data frame or tibble
Extrapolate coverage and width using sub-sampled bootstrap confidence ...
Extrapolate coverage and width using sub-sampled bootstrap confidence ...
Repeat an expression multiple times and (optionally) stack the results...
Calculates performance criteria measures and associated Monte Carlo standard errors for simulation results. Includes functions to help run simulation studies, following a general simulation workflow that closely aligns with the approach described by Morris, White, and Crowther (2019) <DOI:10.1002/sim.8086>. Also includes functions for calculating bootstrap confidence intervals (including normal, basic, studentized, percentile, bias-corrected, and bias-corrected-and-accelerated) with tidy output, as well as for extrapolating confidence interval coverage rates and hypothesis test rejection rates following techniques suggested by Boos and Zhang (2000) <DOI:10.1080/01621459.2000.10474226>.
Useful links