irtsim 0.1.1

CRAN resubmission. Documentation-only changes; no user-facing API or behavior changes.

• DESCRIPTION: expanded all acronyms on first use (API, IRT, 1PL, 2PL, MCAR, MAR, MSE, RMSE, SE) per CRAN reviewer request.
• man/: replaced \dontrun{} with \donttest{} in irt_simulate, summary.irt_results, plot.irt_results, plot.summary_irt_results, recommended_n, print.irt_results, and print.summary_irt_results examples per CRAN reviewer request. Examples remain wrapped (not unwrapped) because each depends on a ~300-fit irt_simulate() call that exceeds the 5-second CRAN example-execution budget.

irtsim 0.1.0

Initial CRAN release.

Core pipeline

• irt_design() specifies the data-generating IRT model (items, parameters, theta distribution).
• irt_study() adds study conditions (sample sizes, missing-data mechanism, optional separate estimation model).
• irt_simulate() runs the Monte Carlo simulation loop with deterministic seeding and optional parallelism.
• summary(), plot(), and recommended_n() methods extract simulation-based sample-size recommendations from irt_results objects.

Supported IRT models

• 1PL (Rasch)
• 2PL
• Graded response model (GRM)

Supported missing-data mechanisms

• "none" — complete data
• "mcar" — missing completely at random
• "mar" — missing at random (monotone, trait-dependent)
• "booklet" — structured booklet assignment with common-item overlap
• "linking" — two-form linked design with user-supplied linking matrix

Performance criteria

• Mean squared error (mse), root mean squared error (rmse), bias, absolute bias, standard error (se), empirical coverage, Monte Carlo SE of MSE (mcse_mse).
• Criterion metadata (direction of improvement, display label) centralized in R/criterion_registry.R.
• Custom per-iteration criteria via the criterion_fn argument to summary.irt_results() — callbacks receive estimates, true_value, ci_lower, ci_upper, and converged and return named numeric vectors appended to item_summary.

Model misspecification

• irt_study(estimation_model = ...) allows fitting a different IRT model than the one used to generate data (e.g., generate 2PL, fit 1PL). Compatible cross-pairs: (1PL, 2PL), (2PL, 1PL), same-model. GRM is not cross-compatible with dichotomous models.

Parallelization

• irt_simulate(parallel = TRUE) dispatches iterations across workers via future.apply::future_lapply().
• Reproducibility contract: within-mode (identical results on re-run for a given parallel setting) guaranteed. Cross-mode results differ because serial uses Mersenne-Twister and parallel uses L’Ecuyer-CMRG substreams — both statistically valid.
• Users control backend via future::plan().

User experience

• cli::cli_progress_bar() replaces cat()-based progress reporting (suppressible with progress = FALSE).
• Structured cli::cli_abort() error messages with valid-option enumerations for invalid model, criterion, missing mechanism, and estimation_model arguments.

Documentation

• Five vignettes reproduce or extend the three examples from Schroeders and Gnambs (2025):
  • Paper Example 1 — faithful reproduction of the linked-test design with 1PL estimation.
  • Paper Example 1b — extension showing bias-variance tradeoff when a 2PL-generated dataset is fit with a 1PL model.
  • Paper Example 2 — MCAR-only partial reproduction with custom-criterion-callback feature demonstration.
  • Paper Example 3 — GRM item parameter recovery partial reproduction.
  • Paper reproduction status — scorecard documenting what the current API can and cannot reproduce end-to-end.
• Vignettes are shipped as static HTML via R.rsp::asis because re-running the Monte Carlo simulations during package checks would exceed CRAN’s build-time budget. The source .Rmd files and data-raw/precompute_vignettes.R are available in the GitHub repository for users who wish to reproduce results locally.

Dependencies

• Imports: cli, future.apply, ggplot2, mirt, rlang
• Suggests: future, knitr, R.rsp, rmarkdown, scales, testthat

Reference

Schroeders, U., and Gnambs, T. (2025). Sample size planning in item response theory: A 10-decision framework. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/25152459251314798