wwc_summary(): collapse a
baseline_equivalence() table into an overall WWC verdict
(satisfied / satisfied_with_adjustment /
not_satisfied) plus per-category counts and the largest
absolute effect size.attrition(): overall and differential attrition for
a two-group design — the inputs to the WWC attrition standard.tutoring: a simulated
quasi-experimental tutoring evaluation (400 students) whose covariates
span all three WWC equivalence categories. See
data-raw/tutoring.R for how it is generated.love_plot(): a Love plot of standardized effect
sizes across covariates, with WWC threshold reference lines and points
coloured by category (requires ggplot2).gt_baseline(): render a baseline equivalence table
as a formatted gt table with readable labels and rounded
statistics (requires gt).ggplot2 and gt added to Suggests; both
functions error gracefully if the package is not installed.cox_index(): WWC Cox index standardized effect size
for binary (dichotomous) covariates.baseline_equivalence() now handles binary
covariates (numeric 0/1, logical, or two-level
factor) via the Cox index, in addition to continuous covariates via
Hedges’ g. A covariate with exactly two unique values is treated as
binary.type column
("continuous" / "binary"), and the effect-size
column is renamed from hedges_g to the estimator-agnostic
effect_size (breaking change). For binary
covariates, mean_treatment and mean_comparison
report event proportions.gt /
flextable).Initial scaffold.
hedges_g(): standardized mean difference (Hedges’ g)
between a treatment and a comparison group, with the WWC small-sample
correction factor.wwc_classify(): classify standardized mean differences
into the three WWC baseline-equivalence categories.baseline_equivalence(): build a report-ready baseline
equivalence table for continuous covariates.