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get_adjusted_ci() with
adjustments = list(decorrelation = "CA"). Correlations are
computed separately within each between-subject group after cell
aggregation and incomplete-subject removal. Cousineau-Morey
("CM") remains the default.get_adjusted_ci() no longer looks up
SE.<name>/CI.<name> precision
functions by name; for summary functions without a built-in formula,
pass errorbar as a function.plot_pointrange() to use the same interval
columns for between- and within-subject summaries, including margin
summaries.brms::posterior_samples() with
brms::as_draws_matrix(), retaining exact coefficient
selection and optional transformations.format_results(type = "plotmath") returning
NULL when the input contains no comma.T/F logical abbreviations,
documented return classes and meanings (including deprecated functions),
added package links, and corrected HTML rendering of column-name
placeholders.\donttest{}, replaced the slow BayesFactor example with a
small t-test, and added regression coverage for interval calculations,
formatting, and plot summaries.get_adjusted_ci() now supports correlation-adjusted
(decorrelation = "CA") intervals for within-subject and
mixed designs, using the mean pairwise condition correlation separately
within each between-subject group. The default remains Cousineau-Morey
("CM").superb; regression tests
compare against it when available.summarySE(),
summarySEwithin() and normDataWithin()
helpers; plot_pointrange() now computes margins with
get_adjusted_ci(), which also fixes
add_margin = TRUE with
within_subj = TRUE.get_adjusted_ci() no longer warns that the
drop argument is ignored when reporting incomplete
cells.base_breaks() no longer prints the computed
breaks.apa.BFBayesFactor() example faster.memory_noise example dataset derived from
Chetverikov & Hansmann-Roth (2026), using unequal-noise trials and
density-asymmetry bias scores; Exp. 1A vs. Exp. 1B is the
between-subject factor and target relative noise is the within-subject
factor.faces example dataset; maintained
examples now use memory_noise or built-in datasets.get_adjusted_ci() as the general interval engine
for between-subject, within-subject, and mixed designs;
plot_pointrange() now uses it for both between- and
within-subject summaries.superb/reshape2 dependency and the
development-branch Remotes entry while documenting the
implementation’s basis in the superb framework.get_adjusted_ci() supports arbitrary scalar summary
functions, with superb-compatible SE/CI formulas for common statistics
and custom precision-function hooks. The old
get_superb_ci() name remains as a deprecated compatibility
wrapper.apa() support for stats::aov()
models with Error() terms (aovlist objects),
fixing #9.apa() support for afex::aov_ez,
afex::aov_car, and afex::aov_4 results via
afex_aov dispatch.describe.afex() compatibility
wrapper.apa.glm support for
lmerTest::lmer model objects via
lmerModLmerTest dispatch.apa.glm support for
lme4::glmer model objects via glmerMod
dispatch.apa.glm mixed-model class checks so
lmerTest fits use the correct coefficient, df, and p-value
handling.term returns a formatted
summary table for all coefficients and added a
lmerTest::lmer example.apastats2-intro) and updated
README documentation.ggplot2::aes_string() usage with
tidy-eval mapping.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.