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2026, unpublished manuscript) studied single-factor, two-level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resource DOI, separate from the unpublished manuscript and software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.