NEWS
oes 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Effect Sizes Using the Framework of Graph Perception and Data Visualization (Shuo Zang and Denis Cousineau, 2026, unpublished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separate from the unpublished manuscript and software citation.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.