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.