NEWS.md
In sbwadjust: Stable Balancing Weights for Covariate Adjustment in Trials

sbwadjust 0.3.0

Removed get_ipws_for_study() and the weight_type / ipw_use_glm arguments of boot_km_ratio(), which now always uses SBW weights. The package is scoped to SBW; inverse-probability weighting was only a comparison method for the paper's simulations. Code that needs it can install v0.2.0 (remotes::install_github("kaylairish/sbwadjust@v0.2.0")).
Renamed km_ratio_loglog_greenwood() to km_ratio_greenwood(). Its CI was always a Wald CI on the log scale; the old name described an intermediate log-log step that cancels out. That step is gone: se(log S) is now computed directly as se(S) / S, which gives identical results except that an arm with no events by t0 (S = 1) now contributes zero to se_log instead of making it NaN.
boot_km_ratio(): dropped the unused verbose argument; the SBW clipping summaries are now NA (not -Inf/NaN with a warning) when no bootstrap resample's SBW fit succeeds; the help page now documents that a failed resample uses the unadjusted KM ratio.
km_ratio_greenwood() now accepts a factor treatment indicator, and its help page notes that the SE treats the weights as fixed.
sbw_estimate() now stops with a clear message when the outcome has a different length from the data the weights were fit on (e.g. an outcome variable missing from data that R found elsewhere), instead of failing later with an unrelated bootstrap error.
Fixed: sbw_estimate() returned an NaN standard error and CI when some bootstrap resamples gave an infinite estimate (e.g. "RR" with a rare outcome, where a resample can draw no control events). Those resamples now count as failed and are reported in boot_fail_rate.
sbw_estimate() now gives a clear error when a Surv() outcome is used with an estimand other than "survival_ratio".
sbw_weights() now accepts the treatment column name as a string (treatment = "arm", or a variable holding it), not only unquoted. Previously a quoted name failed with a misleading "exactly two levels" error.
sbw_weights() now gives clear errors for a treatment with missing values or with only one arm, which previously failed inside the solver with unrelated messages (e.g. "system is exactly singular").
?sbw_weights now states which level of a factor or character treatment is treated (the second level; alphabetical for character), and print.sbw_fit() names the treated and control levels in that case.
sbw_weights() now gives clear errors when the balance covariates are collinear within an arm (including a factor level that never occurs in one arm) or when exact balance with nonnegative weights is infeasible, instead of the solver's "system is exactly singular" or quadprog's "constraints are inconsistent, no solution!".
print.sbw_fit() now reports how many units in each arm got weight 0, replacing the "weight(s) clipped at 0" note, which counted rounding noise in the solver rather than dropped units. summary() does the same: its n_clipped / max_abs_clipped elements are replaced by n_zero (by arm), and its balance table prints to 4 significant digits.
summary() no longer reports an effective sample size. The Kish ESS measures how concentrated the weights are, not the precision of the treatment-effect estimate, and was easy to misread as the latter.
Fixed: plot.sbw_fit() failed when given main, xlim, xlab or ylab (documented as passed on to plot()); these now override the defaults. The plot also lists covariates top-down in formula order and widens the left margin so long covariate names are not cut off.

sbwadjust 0.2.0

"Usable by a stranger" release: a user-facing formula API on top of the v0.1 core.

New sbw_weights(): formula/data/treatment front end to the core SBW solver, returning an sbw_fit object with print(), summary(), plot(), and weights() methods.
New sbw_estimate(): treatment-effect estimation from an sbw_fit, with a bootstrap confidence interval, for a closed menu of estimands: average treatment effect ("ATE"), relative risk ("RR"), survival ratio ("survival_ratio"), Mann-Whitney win probability ("mann_whitney", uncensored outcomes only), and quantile contrasts ("quantile_diff" / "quantile_ratio").
Quantile contrasts use a new .weighted_quantile() internal helper: the generalized-inverse (step-function / type 1) SBW-weighted empirical quantile, matching the empirical-process framework the paper's differentiability results use.
sbw_estimate(estimand = "survival_ratio") now warns when the SBW point estimate or bootstrap SE is non-finite and the unadjusted Kaplan-Meier ratio is returned in its place (previously flagged only by mc_fail).
sbw_estimate() gains a data argument, so weights can be fit on baseline data before outcomes exist and outcomes supplied at analysis time. Rows must line up one-to-one with the fitted data; row count and any shared columns are checked.
Fixed: sbw_estimate()'s bootstrap failed on every resample when sbw_weights() was given treatment as a vector rather than a column name. The bootstrap now resamples the stored 0/1 treatment directly.
Surv() in a survival_ratio outcome formula now resolves without attaching the survival package, and missing survival times are rejected instead of silently dropped.
?sbw_estimate now notes that the row bootstrap assumes simple randomization and does not account for stratified or covariate-adaptive designs.
CRAN Repository Policy compliance pass: fixed a roxygen markdown escaping bug (\%in\% rendering with stray backslashes in ?sbw_estimate), added a runnable @examples block to sbw_estimate(), fixed the DESCRIPTION citation style, added URL/BugReports fields, and added inst/WORDLIST for spelling::spell_check().

sbwadjust 0.1.0

Initial public release. Core stable-balancing-weight machinery, consolidated from the four near-duplicate copies used across the paper's simulations:

get_weights_for_group_neg(), get_weights_for_group_nonneg(), get_sbws_for_study() — closed-form SBW solve with a nonnegative quadratic-programming fallback.
km_ratio_loglog_greenwood(), boot_km_ratio() — weighted Kaplan-Meier survival-ratio point estimates and bootstrap confidence intervals.
get_ipws_for_study() — inverse-probability weights, used as a comparison method.