Changes in version 0.3.0.9000                      

                 Changes in version 0.3.0 (2026-10-10)                  

  - 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.

                        Changes in version 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().

                        Changes in version 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.