Changelog

Source: NEWS.md

ipw (development version)

New features

• ipwtm(): type = "all" now works for family = "multinomial" and "ordinal" (time-varying categorical exposures that switch back and forth).

• ipwtm(): type = "all" now works for family = "survival", following Graffeo, Latouche, Geskus & Chevret (2018, Biometrical Journal): exposure events may recur, an Andersen-Gill Cox model is fitted on all rows, and each row’s probability uses the cumulative baseline hazard over its own interval.

• New ipwprob(): inverse probability weights from probabilities you estimated yourself (for example with machine learning or SuperLearner), for point treatments or cumulated over time within subjects.

• ipwpoint(): new families for count exposures ("poisson", "negbin") and skewed continuous exposures ("gamma", "lognormal").

• Smooth terms (s(), te(), ti(), t2()) in model formulas are fitted as generalized additive models with mgcv: in ipwpoint() for the binomial, gaussian and poisson families, and in ipwtm() for the binomial family.

• ipwpoint(): new estimand argument for a binomial exposure: "ATE" (default), "ATT" (effect in the treated) and "ATO" (overlap weights).

• Existing calls are unchanged: without smooth terms the models are still fitted with glm(), and estimand defaults to "ATE". A covariate named s, te, ti or t2 is treated as an ordinary variable.

• New ipwboot(): bootstrap standard errors and percentile confidence intervals for a whole weighted analysis, re-estimating the weights in each bootstrap sample (Austin 2016). With id, whole subjects are resampled and each sampled copy gets a new identifier; optional strata; failed samples are recorded instead of stopping. print() and confint() methods. The help pages and vignette describe, from the package’s simulation study, when the bootstrap helps (well-behaved weights) and when no standard error is reliable (extreme weights).

Documentation

• New vignette, “Inverse probability weighting with ipw” (vignette("ipw")), covering every function, with the new models and ipwboot() highlighted.
• Bootstrap standard errors. The help pages of ipwpoint() and ipwtm() have a new “Standard errors” paragraph in Details and new examples: a bootstrap that re-estimates the weights and the marginal structural model in each bootstrap sample (ipwpoint()), and a cluster bootstrap that resamples patients and re-runs both ipwtm() calls for the HAART example (ipwtm()). Robust standard errors (svyglm(), cluster()) treat the weights as known and tend to be conservative; Austin (2016, doi:10.1002/sim.7084) recommends the bootstrap.

ipw 1.3.2

CRAN release: 2026-11-01

Bug fixes

• ipwpoint(): weights.trunc was wrong whenever trunc was set. The lower and upper quantiles were recycled across rows, so every truncated weight was replaced by one of the two bounds. Weights are now clipped at the trunc and 1 - trunc quantiles, and weights between the bounds are left unchanged.
• ipwtm() with family = "multinomial" or "ordinal": predicted probabilities are now matched to the observed exposure by level name rather than by column position. 1.3.1 stopped with an error for a two-level exposure, or when an exposure level did not occur among the rows used to fit the models.
• ipwpoint() with family = "multinomial" now works for a two-level exposure; 1.3.1 stopped with an error (“Observed exposure levels do not match fitted probability levels”).
• Added a test for unstabilized weights (no numerator) with family = "multinomial" and "ordinal" in ipwpoint(). These failed in 1.3.0 and were fixed by the 1.3.1 rewrite (#3, @adcascone).
• ipwtm() with type = "first" and family = "multinomial" / "ordinal": a switch is again defined as the first record at any level other than the lowest level in the whole data set, as in ipw <= 1.2.1.1. In 1.3.1 the reference level was chosen separately for each subject.

Improvements

• ipwtm() now checks that all model variables exist and contain no missing values, and that a binomial/survival exposure is coded 0/1, with clear error messages instead of errors from inside glm() / coxph().
• ipwtm() with family = "survival" now stops with a clear message when a formula contains strata(), which is not supported.
• Fitted models returned in num.mod / den.mod no longer carry an inlined copy of the data in their call, so printing them is readable again.
• ipwpoint() and ipwtm() share one set of internal helpers for argument checks, formula construction, probability extraction and truncation.
• Removed blanket import() of whole packages; all calls use pkg::fun. methods is no longer imported.

Tests

• Test suite expanded: exact checks of truncation, row-order invariance, two-level multinomial vs binomial equivalence, and error messages.
• New regression test against ipw 1.2.1.1 (reference weights created with data-raw/make-regression-fixtures.R).

ipw 1.3.1

CRAN release: 2026-10-30

• Core Refactoring (ipwtm, ipwpoint, ipwplot, tstartfun):
  • Improved defensive input validation, parameter checking, and error messages across all core functions.
  • Modernized formula construction (as.formula()) to eliminate fragile eval(parse(text = ...)) string manipulations.
  • Fixed variable symbol evaluation issues when passing variables as function arguments rather than raw names.
  • Modularized ipwtm into dedicated family modeling engines (binomial, survival, multinomial, ordinal, gaussian).
  • Replaced manual survival hazard step-differencing with continuous baseline hazard interpolation using survival::basehaz and stats::approx.
  • Refactored tstartfun to use safe column evaluation and direct vector lag logic (c(-1, t[-length(t)])).
• Test Suite Expansion:
  • Upgraded testthat suite to 3rd edition specs, expanding coverage for multinomial, ordinal, gaussian, unstabilized models, weight truncation, and invalid parameter rejection.

ipw 1.3.0

CRAN release: 2026-01-27

• New maintainer: Hung Thai Tran.
• License changed to MIT (earlier versions: GPL (>= 2)).