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wwc_robustness(): reports how stable a
baseline-equivalence verdict is to the computation choices an analyst
could defensibly make differently (standardizing by the pooled versus
the comparison-group standard deviation, and applying the WWC
small-sample correction or not), flagging whether each covariate’s
category, and the overall verdict, changes. A multiverse view of a
single WWC determination.attrition_boundary(): classifies a study as low or
high attrition against the WWC attrition boundary (Standards Handbook
v4.1, Table II.1), under the cautious or optimistic assumption.
Complements attrition(), which reports the rates but leaves
the classification to the user.wwc_rating(): applies the WWC group-design rating
logic (Standards Handbook v4.1, Section II) to attrition and baseline
equivalence, returning “Meets Without Reservations”, “Meets With
Reservations”, or “Does Not Meet”.cluster_correction(): the WWC clustering correction
for mismatched analyses (Procedures Handbook v4.1, Appendix F, after
Hedges 2007). Corrects the t statistic and its degrees of freedom for
clustering and returns the clustering-corrected p value and significance
decision, with the WWC default ICCs (0.20 achievement, 0.10 other).
Validated against 1,800+ clustered findings in the official WWC study
database.cox_index() follows Procedures Handbook
v4.1 [VI.1.2] exactly: the Cox index carries no small-sample correction
(that correction applies to Hedges’ g only).hedges_g() and cox_index() now handle
missing values consistently when na.rm = FALSE: both reject
missing input up front with the message “Missing values present; set
na.rm = TRUE to drop them.” Previously
hedges_g() raised an opaque internal error while
cox_index() silently returned NA (#12).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.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.