NEWS

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rwetools 0.5.0 (2026-09-09)

This is a BREAKING redesign of the effect-estimation API. Direct standardization has been removed, the two former effect functions have been split into three single-purpose functions, and no deprecated compatibility wrappers are provided. Propensity-score weighting estimands are unchanged.

Breaking effect API

• estimate_hr_ir() has been replaced by estimate_hr() and estimate_ir(). Hazard and rate results now have separate calls, return objects, and optional workbooks.
• estimate_rr_rd() has been renamed to estimate_risk() and rr_rd_at_timepoint to risk_at_timepoint.
• Direct standardization and its output have been removed from estimate_ir() and estimate_risk(). The old stratification_var argument is now strata_var in estimate_hr() only, where it adds survival::strata() terms and estimates a conditional common hazard or subdistribution-hazard ratio. It is not a replacement for a marginal standardized estimate.
• time_unit has been removed from estimate_hr() because proportional-hazard models are invariant to a common rescaling of follow-up time. It remains in estimate_ir() and estimate_risk().
• The Stratified_By output column and all standardized rate/risk output have been removed. Scripts reading effect workbooks must be updated accordingly.
• estimate_hr_ir() and estimate_rr_rd() are absent rather than deprecated.

To migrate a 0.4.0 call, split HR from rate estimation, rename the risk function/timepoint argument, and decide explicitly whether a former hazard stratifier should remain as strata_var. For marginal rates and risks, either include the covariate in the propensity-score design or run and combine stratum-specific analyses explicitly in caller code. Users upgrading directly from CRAN 0.2.0 must also apply the 0.3.0 and 0.4.0 breaking changes below.

Correctness and inference fixes

• exp_value and ref_value are now honored consistently throughout the PS, weighting, matching, and effect pipeline. In 0.4.0, passing exp_value = 0, ref_value = 1 to HR/IR could silently leave the original arm direction in place; estimates from such calls may therefore invert after upgrade. The two values must differ, rows matching neither value are dropped with a counted warning, and an empty analysis arm is an error. Public data outputs retain the caller's original exposure labels.
• Matched-block IR, IRD, and IRR analytical standard errors are now cluster-robust on if_match_match_id. Depending on within-pair covariance, the corrected interval may be narrower or wider; point estimates are unchanged.
• The weighted/matched Total-row IR confidence interval and standard error, which were silently NA, now use a valid robust intercept fit.
• The matched effect block now enforces its supported design: all .match_weights must equal 1, and a supplied match id must contain exactly one exposed and one reference row. Variable/incomplete 1:k, subclass, and with-replacement designs must use the documented weighted route and its stated inference limitations.
• A match id that assigns a row to multiple sets is rejected. A hazard strata_var level lacking either exposure arm is also rejected.
• Aalen-Johansen risk estimation now rejects a row marked simultaneously as the event of interest and a competing event, matching the Fine-Gray guard.
• Fine-Gray complete-case preparation no longer narrows the incidence-rate table produced by the separate estimate_ir() call.

Weighting, matching, and Table 1

• Exposure handling is centralized across estimate_ps(), the three PS-weight functions, fine stratification, and cohort matching; original labels are restored in returned data.
• Weighted build_table1() now requires an existing numeric, finite, non-missing, non-negative weight column. Zero-weight rows are reported and removed before constructing a survey design, avoiding infinite sampling probabilities while leaving weighted statistics unchanged.
• create_ps_matched_cohort() now applies .match_weights in its built-in matched Table 1 whenever they are non-unit, including subclass and variable/incomplete nearest 1:k output.
• Manual pages for the ten exported functions have been regenerated for the new API and its inference contracts.

rwetools 0.4.0 (2026-08-01)

Complete redesign of the two effect-estimation functions, estimate_hr_ir() and estimate_rr_rd() (API v2). This is a BREAKING release for both functions; the propensity-score, weighting, matching, and Table-1 functions are unchanged.

Breaking API changes (both effect functions)

• Data blocks replace the old unweighted/weighted pair: in_df_crude plus either in_df_weight (with if_weight_weight_var) or in_df_match (optionally with if_match_match_id) — supplying both weighted and matched blocks is an error; weight- or match-only calls without a crude block are allowed. Output column suffixes are now _Crude / _Weighted / _Matched (previously _Unwt / _Wted).
• Renames: survival_time -> followuptime_var; stratify_by -> stratification_var; rr_rd_per_individuals -> risk_per_individuals; weight_var -> if_weight_weight_var.
• Analytical confidence intervals are ALWAYS computed and the CI-method selector arguments are gone: hr_conf_int_method, cluster_var, conf_int_method, irr_method, and irr_covariates have been removed. Each estimate uses one fixed, documented method (below).
• The percentile bootstrap is opt-in via if_bootstrap_count (plus if_bootstrap_n_cores / if_bootstrap_seed) and is always fixed-weight ("frozen"): analysis weights and standardization shares are held at their original values, so propensity-score-estimation uncertainty is not propagated (a message states this on every bootstrap run). Matched blocks resample matched sets by if_match_match_id. Only percentile intervals are provided (no BCa).
• time_unit gains "months" (person-years = months / 12) in estimate_hr_ir().

Fixed analytical methods (per estimate)

• Crude/matched IR: Garwood exact-Poisson (gamma) intervals (unchanged).
• Standardized IR arm CIs: Fay-Feuer (1997) gamma intervals replace the log-normal interval; a single stratum collapses exactly to the Garwood interval. The Total row keeps the crude Garwood interval; the IRD keeps the normal-Wald delta interval.
• Weighted IR / IRD: design-based robust (sandwich) variance from a joint weighted quasi-Poisson cell model (Lumley 2004), replacing the previous exact-Poisson interval on the rounded weighted event sum, which ignored the weighting variance and could severely under-cover under IPTW. Standardized weighted variants use the joint sandwich/delta method.
• HR (estimate_hr_ir()): crude block = Cox model SE; weighted block = robust sandwich SE; matched block = robust SE clustered on the match id (Lin-Wei 1989; Austin 2016). The old "auto" dispatch is now the only behavior.
• Fine-Gray subdistribution hazards MOVED from estimate_rr_rd() (fg_regression removed) into estimate_hr_ir(hr_model = "Fine-Gray") with if_fg_competing_event_var; weighted expansions keep fgwt = IPCW x PS weight, and matched blocks cluster the robust SE on the match id. estimate_rr_rd() keeps risk_estimator = c("KM", "AJ") with the competing-event indicator renamed to if_aj_competing_event_var (AJ-only).
• IRR is now always reported by estimate_hr_ir() as a marginal rate ratio: crude/matched blocks use a Poisson rate model (model SE, equal to sqrt(1/D1 + 1/D0)); the weighted block uses survey::svyglm quasi-Poisson (robust SE). The former quasi-Poisson option for unweighted fits is gone. Stratified IRRs, previously an error, are now direct-standardized.
• Risk / CIF arm CIs (estimate_rr_rd()): the log-scale interval is replaced by the complementary log-log (cloglog) interval applied to the final (standardized/weighted) estimate with its combined SE (Kalbfleisch & Prentice 2002). A crude Kaplan-Meier risk now reproduces survfit(conf.type = "log-log") exactly. A risk of exactly 0 or 1 has no defined cloglog interval: NA is returned and a message recommends the bootstrap. RD (normal-Wald) and RR (log-delta) intervals are unchanged.

Other

• New internal analytical engines with unit tests: Fay-Feuer gamma CI, cloglog risk CI, joint-sandwich weighted rate cells with delta-method contrasts, direct-standardized IRR, and matched-set bootstrap resampling.
• v0.3.0 numeric fixtures ship in tests/testthat/fixtures/ and guard the redesign: every method-unchanged output reproduces the 0.3.0 values exactly; only the weighted IR/IRD CIs (robust), standardized IR arm CIs (Fay-Feuer), Risk/CIF arm CIs (cloglog), and the crude IRR SE (Poisson) changed, each by design.
• The package now declares Depends: R (>= 3.5.0) (serialized test fixtures use serialization format version 3).

rwetools 0.3.0

(Not submitted to CRAN; folded into 0.4.0.)

• BREAKING: create_ps_weights() has been removed. The single function with weight_method + estimand arguments is replaced by one function per estimand:
  • create_iptw() — inverse-probability weights for the ATE (stabilize = TRUE for stabilized IPTW).
  • create_matching_weights() — matching weights, now correctly labeled as targeting the ATM (Li & Greene 2013), not the ATT.
  • create_overlap_weights() — overlap weights for the ATO (Li et al. 2018). IPTW for the ATT (SMR weights) is not supported in this version. The broken stabilized-IPTW + ATO path has been removed.
• create_iptw(), create_matching_weights(), create_overlap_weights(), and create_ps_matched_cohort() gain optional propensity-score trimming (trim_method: "crump" symmetric or "sturmer" asymmetric). create_iptw() additionally gains weight truncation/winsorizing (truncate_method: "percentile" or "cap"). All default to off; both trimming and truncation change the analytic population and the interpretation of the estimand.
• create_ps_matched_cohort() gains a method argument ("nearest"/"subclass"), variable-ratio matching (min_controls / max_controls / m_order), and subclass_n. For "subclass" the returned cohort carries matching weights in .match_weights, which must be used for the matched Table 1 and any downstream effect estimation.
• Balance diagnostics and Love plots now use the raw (0-1) standardized-mean- difference scale consistently, fixing a bug where the "balanced" flag compared |SMD| against threshold * 100 and the Love-plot reference line was drawn at 10 (off-screen) on raw-scale data.
• estimate_hr_ir() gained a hr_conf_int_method argument controlling how the hazard-ratio confidence interval (and the reported lnHR_SE) was computed:
  • "auto" (default): model-based SE for the unweighted fit and robust sandwich SE for the weighted fit (Austin 2016).
  • "model": model-based (naive) Cox SE with a Wald CI for both fits.
  • "robust": robust sandwich SE with a Wald CI for both fits ("sandwich" accepted as an alias).
  • "bootstrap": percentile bootstrap CI with a bootstrap SE (lnHR_SE = sd(log(HR))); no p-value reported. (Removed again in 0.4.0: the "auto" dispatch is now the only behavior.)
• estimate_hr_ir() gained cluster_var, bootstrap_count, n_cores, and seed arguments for the robust/bootstrap methods (removed again in 0.4.0 in favor of the fixed per-block methods and if_bootstrap_*).
• estimate_hr_ir() gained an optional incidence-rate-ratio rate model via irr_method ("none"/"poisson"/"quasipoisson") with optional irr_covariates; weighted fits used survey::svyglm(quasipoisson) (reworked in 0.4.0: IRR is always-on and marginal).
• estimate_rr_rd() gained a Fine-Gray subdistribution-hazard regression via fg_regression + competing_event_var (moved to estimate_hr_ir() in 0.4.0).
• estimate_ps() gained a separation diagnostic for the propensity-score logistic model and uses na.exclude so the returned PS column aligns with the input rows.

rwetools 0.2.0 (2026-06-21)

• create_ps_weights() and create_ps_matched_cohort() now also emit the unified PS-distribution figures (density, within-group histogram, and a histogram+density overlay for the unweighted/pre and weighted/post samples, plus a weight box plot for the weighting methods) that create_ps_fs_weights() already produced. The existing faceted / mirror plots are still written. A new internal helper .plot_ps_distribution_set() is shared by all three functions, so the figure style is now consistent across PS methods. create_ps_fs_weights() figure output is unchanged.
• create_ps_matched_cohort() gains a caliper_scale argument controlling the scale on which matching and the caliper are applied: "logit_ps_sd" matches on logit(PS) with a caliper of caliper x SD(logit(PS)) (Austin 2011); "raw_ps_sd" matches on PS with a caliper of caliper x SD(PS); "raw" applies a flat caliper on the raw PS scale (e.g. caliper = 0.01).
• BREAKING: the default matching caliper scale changed. rwetools <= 0.1.x matched on the raw PS with a standardized caliper (caliper x SD(PS)); the new default (caliper_scale = "logit_ps_sd") matches on logit(PS) (caliper x SD(logit(PS))). Pass caliper_scale = "raw_ps_sd" to reproduce the previous results exactly. The verbose/report label that previously described the raw-PS caliper as "SD of logit(PS)" is now accurate for each scale.

rwetools 0.1.2 (2026-05-28)

• CRAN resubmission fixes:
  • DESCRIPTION: single-quoted 'Excel' per CRAN software-name convention.
  • DESCRIPTION: added method references in the Description field (Rosenbaum and Rubin 1983 doi:10.1093/biomet/70.1.41; Austin 2011 doi:10.1080/00273171.2011.568786; Desai et al. 2017 doi:10.1097/EDE.0000000000000595).
  • estimate_rr_rd(): removed hardcoded RNG seed from the internal bootstrap helper. New seed argument (default NULL) lets the user opt in to reproducibility; when NULL the function does not touch the global RNG state. In the sequential branch, .Random.seed is saved and restored on exit when a seed is supplied.
• tests: fixed the zero-count-level regression test (test-only change). The 0.1.1 test used expect_message(..., "Auto-excluding.*race_cat"), which required both substrings in a single message() call, but the verbose path emits the header and the variable name on separate lines. Switched to capture_messages() and asserts both substrings individually.

rwetools 0.1.1

• fix: estimate_ps() categorical extreme-distribution screen now detects zero-count levels via union of levels across exposure groups. Previously, a level present in only one exposure group was silently dropped by table() and could slip past the cnt < 5 rule, triggering quasi-separation in glm().

rwetools 0.1.0

• Initial CRAN release.