Changes in version 1.0.0 (2026-10-21)                  

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    unpublished manuscript) studied single-factor, two-level comparisons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    This is a resource DOI, separate from the unpublished manuscript and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.