Monte Carlo Simulation-Based Sample-Size Planning for Item Response Theory R package irtsim version 0.1.1 Provides a pipeline application programming interface (API) for Monte Carlo simulation-based sample-size planning in item response theory (IRT). Implements the 10-decision framework from Schroeders and Gnambs (2025) <doi:10.1177/25152459251314798> as a three-step workflow: specify the data-generating model with irt_design(), add study conditions with irt_study(), and run simulations with irt_simulate(). Supports one-parameter logistic (1PL), two-parameter logistic (2PL), and graded response models with missing-completely-at-random (MCAR), missing-at-random (MAR), booklet, and linking missingness mechanisms. Results include mean squared error (MSE), bias, root mean squared error (RMSE), standard error (SE), and coverage criteria with summary and plot methods. Software Depends: R (>= 4.1.0) Imports: cli, future.apply, ggplot2, mirt, rlang Suggests: future, knitr, R.rsp (>= 0.46.0), rmarkdown, scales, testthat (>= 3.0.0) Stephen Ward <stephen_ward+irtsim@abhome.co> Comprehensive R Archive Network (CRAN) Stephen Ward [aut, cre] GPL (>= 3) 2026-04-23 application/tgz https://CRAN.R-project.org/package=irtsim doi:10.32614/CRAN.package.irtsim en-US