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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Effect Sizes Using the Framework of Graph Perception and Data Visualization (Shuo Zang and Denis Cousineau, 2026, unpublished manuscript) studied single-factor, two-level comparisons using Cohen’s d; broader designs, other effect estimators, and generalized ER recommendations remain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 This is a resource DOI, separate from the unpublished manuscript and software citation.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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.