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mlmr 0.1.0

Public beta preparation

  • Published GitHub prerelease v0.1.0-alpha.2 for structured beta testing.
  • Added a structured beta testing guide for early users and linked it from the documentation site.
  • Expanded the GitHub feedback template so testers report model structure, exported-code checks, and app section details.
  • Exported core backend helpers for model specification, formula generation, fitting, equations, APA tables, and software/package reporting.
  • Added mlm_supported_models() and a “Supported Models and Production Scope” vignette so users can see supported, experimental, planned, and out-of-scope features before using the app in real analyses.
  • Added APA-style software and R package reporting for reproducible manuscripts, including Shiny app display, Quarto report output, and LaTeX export.
  • Added optional papaja citation code generation for users who want APA-style R/package citations and BibTeX references in R Markdown manuscripts.
  • Added package citation metadata and expanded multi-platform check preparation.
  • Polished README installation, demo, documentation, and feedback guidance.
  • Added package-level documentation and runnable interactive example for run_mlmr().
  • Added CRAN submission comments and release checklist scaffolding.
  • Added a GitHub Actions workflow for building and publishing the pkgdown documentation site.
  • Kept root development app files out of the built package; the installed Shiny app is shipped through inst/app/.

Development version

  • Added the initial Shiny app for mixed-effects and multilevel model building.
  • Added the HSB-style example data workflow.
  • Added model specification, formula generation, centering, fitting, diagnostics, APA-style tables, model equations, Tau matrix display, and reproducible code export.
  • Added Quarto, R script, LaTeX, HTML, and Word-compatible export scaffolds.
  • Added package metadata, GitHub URLs, roadmap, and initial backend tests.
  • Added model-readiness checks before fitting custom models.
  • Added direct data upload support for CSV, TSV/TXT, Excel, SPSS, SAS, and Stata files.
  • Added loading indicators for slower diagnostics plots.