Package: simhelpers 0.3.0.9999

simhelpers: Helper Functions for Simulation Studies

Calculates performance criteria measures and associated Monte Carlo standard errors for simulation results. Includes functions to help run simulation studies. Our derivation and explanation of formulas and our general simulation workflow is closely aligned with the approach described by Morris, White, and Crowther (2019) <doi:10.1002/sim.8086>.

Authors:Megha Joshi [aut, cre], James Pustejovsky [aut]

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

# Install 'simhelpers' in R:

install.packages('simhelpers', repos = c('https://meghapsimatrix.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/meghapsimatrix/simhelpers/issues

Datasets:

Tipton_Pusto - Results for Figure 2 of Tipton & Pustejovsky
alpha_res - Cronbach's alpha simulation results
t_res - T-test simulation results
welch_res - Welch t-test simulation results

On CRAN:

7.18 score 11 stars 38 scripts 217 downloads 13 exports 31 dependencies

Last updated 1 months agofrom:d9711e8ac3. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 20 2024
R-4.5-win	OK	Nov 20 2024
R-4.5-linux	OK	Nov 20 2024
R-4.4-win	OK	Nov 20 2024
R-4.4-mac	OK	Nov 20 2024
R-4.3-win	OK	Nov 20 2024
R-4.3-mac	OK	Nov 20 2024

Exports:bootstrap_CIs bootstrap_pvals bundle_sim calc_absolute calc_coverage calc_rejection calc_relative calc_relative_var create_skeleton evaluate_by_row extrapolate_coverage extrapolate_rejection repeat_and_stack

Dependencies:cli codetools cpp11 digest dplyr fansi furrr future generics globals glue lifecycle listenv magrittr parallelly pillar pkgconfig purrr R6 rbibutils Rdpack rlang rstudioapi stringi stringr tibble tidyr tidyselect utf8 vctrs withr

Presenting Results from Simulation Studies

Rendered fromvisualization.Rmdusingknitr::rmarkdownon Nov 20 2024.

Last update: 2020-10-12
Started: 2020-03-14

Simulation Performance Criteria and MCSE

Rendered fromMCSE.Rmdusingknitr::rmarkdownon Nov 20 2024.

Last update: 2024-09-04
Started: 2020-01-05

Simulation Workflow

Rendered fromsimulation_workflow.Rmdusingknitr::rmarkdownon Nov 20 2024.

Last update: 2024-09-03
Started: 2020-01-18

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Cronbach's alpha simulation results	alpha_res
Calculate one or multiple bootstrap confidence intervals	bootstrap_CIs
Calculate one or multiple bootstrap p-values	bootstrap_pvals
Bundle functions into a simulation driver function	bundle_sim
Calculate absolute performance criteria and MCSE	calc_absolute
Calculate confidence interval coverage, width and MCSE	calc_coverage
Calculate rejection rate and MCSE	calc_rejection
Calculate relative performance criteria and MCSE	calc_relative
Calculate jack-knife Monte Carlo SE for variance estimators	calc_relative_var
Open a simulation skeleton	create_skeleton
Evaluate a simulation function on each row of a data frame or tibble	evaluate_by_row
Extrapolate coverage and width using sub-sampled bootstrap confidence intervals.	extrapolate_coverage
Extrapolate coverage and width using sub-sampled bootstrap confidence intervals.	extrapolate_rejection
Repeat an expression multiple times and (optionally) stack the results.	repeat_and_stack
t-test simulation results	t_res
Results for Figure 2 of Tipton & Pustejovsky (2015)	Tipton_Pusto
Welch t-test simulation results	welch_res