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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.
Health stats visible at Monitor.