oes 1.0.0

Initial public-version numbering, based on development version V1.8.

• Rechecked release-plan Steps 1–6. Plot-only fallbacks now honor and validate a supplied measurement range. Explicit original-layout limit overrides clear previous y-scale limits before plotting, restoring censored observations while retaining the scale configuration and leaving the input plot unchanged.
• Added regressions for those plot edge cases and independent numerical checks of multilevel conditional-effect selection and three-condition Cousineau–Morey intervals. Corrected the exploratory Hedges’ g warning to describe the studied calibration setting accurately.
• Added package metadata, namespace declarations, MIT licensing, and the software citation with Shuo Zang’s ORCID.
• Moved the V1.8 functions into the package’s R/ directory and declared runtime dependencies instead of attaching them with library().
• Made optimal_graph() the sole exported function. Kept oes() as an internal calculation engine, with numerical results available through optimal_graph(..., detail = TRUE)$oes. Updated examples and tests to follow this API.
• Retained the V1.8 summary grouping and colour correction, including fill-only, shape, and linetype mappings.
• Retained the V1.7 numerical corrections for Hedges’ g, Cousineau-Morey degrees of freedom, and forwarded column/factor arguments.
• Integrated the standalone numerical and plotting regressions into installed-package tests using self-contained data.
• Added a methods guide and clarified the evidence limits: Visualizing Standardized Effect Sizes Using the Framework of Graph Perception and Data Visualization (Shuo Zang and Denis Cousineau, 2026, unpublished manuscript) studied single-factor, two-level comparisons using Cohen’s d; broader designs, other effect estimators, and generalized ER recommendations remain empirically unvalidated extensions.
• Documented that the current a constants come directly from the effect-size study and may change after further research. A separate package-methods paper is planned.
• Identified the effect-size study’s associated OSF resource (Zang and Cousineau, 2025; CC BY 4.0): https://doi.org/10.17605/OSF.IO/DRCTA. This is a resource DOI, separate from the unpublished manuscript and software citation.
• Recorded the supplied error-bar study-data provenance (OSF DOI 10.17605/OSF.IO/DTYBF), the exact CSV reference, and its CC BY 4.0 license. Mandatory tests remain independent of external data and network access.
• Added a corrected manual error-bar Study 4B example for the project ZIP (development/example-error-bar-study4b.R) and included error-bar data-source attribution in inst/doc/error-bar-data-source.md.
• Identified Cousineau (2017), Varieties of confidence intervals https://doi.org/10.5709/acp-0214-z, as the primary reference for CI difference adjustment and combined repeated-measures adjustment. Retained Cousineau (2005) and Morey (2008) for normalization and bias correction. Clarified that ci95_corr omits sqrt(2), while ci95_dif_corr applies both adjustments; the article supports CI construction, not OES calibration.
• Added recommended graphing workflows: oes for y-axis ranges with superb::superb() for adjustable error bars, custom ggplot2 graphs with oes, or graphs generated directly by optimal_graph(). Cited Cousineau, Goulet, and Harding (2021), https://doi.org/10.1177/25152459211035109.
• Prepared automated checks for Linux, Windows, and macOS. Release validation is in progress; publication follows completed checks.