Package index • simhelpers

Performance Criteria and MCSE

Functions for calculating performance critieria and MCSE

calc_absolute(): Calculate absolute performance criteria and MCSE

calc_relative(): Calculate relative performance criteria and MCSE

calc_relative_var(): Calculate jack-knife Monte Carlo SE for variance estimators

calc_rejection(): Calculate rejection rate and MCSE

calc_coverage(): Calculate confidence interval coverage, width and MCSE

Simulating bootstrap processes

Specialized functions for simulations involving bootstrap hypothesis tests or bootstrap confidence intervals

bootstrap_pvals(): Calculate one or multiple bootstrap p-values

bootstrap_CIs(): Calculate one or multiple bootstrap confidence intervals

extrapolate_rejection(): Extrapolate coverage and width using sub-sampled bootstrap confidence intervals.

extrapolate_coverage(): Extrapolate coverage and width using sub-sampled bootstrap confidence intervals.

Simulation Workflow

Functions for facilitating simulation workflows

create_skeleton(): Open a simulation skeleton

repeat_and_stack(): Repeat an expression multiple times and (optionally) stack the results.

bundle_sim(): Bundle functions into a simulation driver function

evaluate_by_row(): Evaluate a simulation function on each row of a data frame or tibble

Example Datasets

Example datasets from simulation studies

Tipton_Pusto: Results for Figure 2 of Tipton & Pustejovsky (2015)

alpha_res: Cronbach's alpha simulation results

t_res: t-test simulation results

welch_res: Welch t-test simulation results