NEWS.md

In WeightIt: Weighting for Covariate Balance in Observational Studies

WeightIt News and Updates

WeightIt 2.0.0

Censoring weights (IPCW)

• Added support for estimating inverse probability of censoring weights (IPCW). Censoring is now its own treatment type, distinct from binary, multi-category, and continuous treatments. Wrapping a censoring indicator in .cens() on the left side of a model formula requests it, as in weightit(.cens(C) ~ x1 + x2, data = d, method = "glm"). The convention is that the indicator is 1 for units that are censored and 0 for those still under observation; censored units receive a weight of exactly 0, and the rest receive $1/P(C = 0 | X)$. .cens() is documented at ?.cens.

• .cens() can also be called outside a formula to tag a variable as a censoring indicator, which routes the lower-level interfaces to the censoring path: weightit.fit(covs, treat = .cens(C), method = "glm") and get_w_from_ps(ps, treat = .cens(C)) estimate and compute censoring weights, the latter returning (1 - C)/(1 - ps), where ps is the probability of being censored. Arguments that do not apply (estimand, focal, treated, subclass, stabilize) are ignored with a warning.

• Each weighting method has a dedicated implementation for censoring weights, rather than reusing the binary-treatment machinery. Because inverse probability of censoring weights are only ever needed for the units still under observation, these estimate weights for that group alone, targeting the covariate distribution of the full at-risk sample. This avoids the wasted (and sometimes infeasible) problem of solving for weights among the censored units, which matters most when few units are censored. Censoring weights are available for method values "glm", "bart", "cbps", "ebal", "energy", "gbm", "ipt", "optweight", "super", and "cfd", as well as method = NULL (which yields complete-case weights) and a supplied ps. They are not available for method = "npcbps". M-estimation-based standard errors are available for the same methods that support them for binary treatments.

• weightitMSM() supports censoring models interleaved with the treatment models in formula.list in temporal order, as in list(A_1 ~ X_0, A_2 ~ X_1 + A_1, .cens(C_2) ~ X_1 + A_1, A_3 ~ X_2 + A_2). Each model is fit only among the units still under observation at that time point, missing values are permitted in later treatments for units already censored, and the censoring weights are folded into the product of weights across time points. The new cens.list, cens.covs.list, cens.formula.list, cens.time, and at.risk components of the output describe the censoring models and are documented in the "Value" section of ?weightitMSM. The print() method reports how many units were censored at each censoring time point and the covariates used to model it.

• In weightitMSM(), censoring time points are stabilized exactly as treatment time points are. With stabilize = TRUE, the numerator of a censoring weight is a model for that censoring indicator saturated in the preceding treatments (an intercept when no treatment precedes it), and num.formula adds stabilization factors to it as it does for a treatment. When num.formula is supplied as a list, it must therefore have one entry per entry of formula.list, censoring entries included.

• The numerator of a stabilized censoring weight is fit as a censoring model, using the same method as the denominator, in both weightit() and weightitMSM(). The stabilized weight is $P(C = 0 | V)/P(C = 0 | X)$ for the units still under observation, where $V$ are the stabilizing variables (usually just prior treatments, if any), and exactly 0 for those censored.

• A censoring formula may have an empty right hand side, as in .cens(C) ~ 1, requesting a marginal censoring model in which censoring does not depend on the covariates. The weights are then $1/P(C = 0)$ for the units still under observation and 0 for those censored. This works in both weightit() and weightitMSM(), with any method (see the note on empty formulas under Other new features), and leaves the rest of the censoring machinery intact, including the risk sets, the missing values permitted after censoring, by, stabilize, and M-estimation.

• Censoring weights are also available with method = "cbps" and is.MSM.method = TRUE in weightitMSM(), i.e., estimating one set of weights that satisfies the balance conditions at all time points simultaneously. Each condition is evaluated among the units still under observation at that time point, so the covariates and treatments that are missing for already-censored units never enter the estimating equations. The treatment conditions use the cumulative weights, which include the censoring factors, so they account for censoring; each censoring condition uses its own factor.

• glm_weightit(), lm_weightit(), multinom_weightit(), ordinal_weightit(), and coxph_weightit() now tolerate missing values in the model variables for units with a weight of 0, which contribute neither to the fit nor to its variance. This makes it possible to fit an outcome model after censoring, where the outcome is unobserved for censored units. Missing values in units with a nonzero weight continue to produce an error. Relatedly, coxph_weightit() now allows weights of 0, which survival::coxph() rejects; such units are omitted from the model fit.

Random effects in PS models

• method = "glm" now supports multilevel (mixed-effects) propensity score models. When the model formula supplied to weightit() contains lme4-style random effects terms (e.g., treat ~ x1 + x2 + (1 | school)), a multilevel model is fit to estimate the propensity scores: lme4::glmer() for binary treatments, lme4::lmer() for continuous treatments, and mclogit::mblogit() for multi-category treatments. The estimated propensity scores are cluster-specific (they include the estimated random effects).

• method = "bart" also supports multilevel propensity score models. When the model formula supplied to weightit() contains lme4-style random effects terms (e.g., treat ~ x1 + x2 + (1 | school)), a multilevel BART model is fit using stan4bart::stan4bart(), which combines a BART sum-of-trees for the covariates with a Stan-estimated random-effects component. The full flexibility of lme4::glmer() random effects is available, including multiple grouping factors and random slopes. bart2()-style control arguments are translated to their stan4bart() equivalents. The estimated propensity scores are cluster-specific.

• Added an re_ok field to the entries of .weightit_methods indicating whether each method supports random effects terms in the model formula.

Other new features

• multinom_weightit() and ordinal_weightit() gain a br argument, which, when set to TRUE, performs mean bias reduction by solving the bias-reducing adjusted score equations of Firth (1993) instead of the usual maximum likelihood score equations. This yields estimates with smaller asymptotic bias that are always finite, even when the maximum likelihood estimates are not (e.g., under separation or when an end category of an ordinal outcome is unobserved). This mirrors br in glm_weightit() but uses in-house implementations rather than brglm2, so it requires no additional dependencies and composes with M-estimation, cluster-robust standard errors, and bootstrapping.

• For multinom_weightit(), the adjustment is that of Kosmidis and Firth (2011); because the model uses the canonical link, the adjusted score is the gradient of the log-likelihood penalized by the Jeffreys invariant prior, and estimates align with brglm2::brmultinom().

• For ordinal_weightit(), the adjustment is the one derived for cumulative link models by Kosmidis (2014) and works for all supported links; the adjusted score equations are solved by quasi-Fisher scoring, controlled by the new br.maxit and br.tol components of control. Note this differs from brglm2::bracl(), which fits an adjacent category logit model rather than a cumulative link model (including with parallel = TRUE, which constrains the adjacent category logits rather than the cumulative ones to be parallel).

• With method = "glm" and a multi-category treatment, bias-reduced propensity score models are now fit by WeightIt itself rather than by brglm2. A br. prefix on link requests mean bias reduction, as before, but it is now handled by multinom_weightit()'s and ordinal_weightit()'s machinery: multi.method = "weightit" accepts "br.logit" for unordered treatments and any of "br.logit", "br.probit", "br.loglog", "br.cloglog", and "br.cauchit" for ordered ones. Because MASS::polr() cannot fit bias-reduced models, supplying a br. link with multi.method = "polr" uses the WeightIt implementation as well. The practical gain is that M-estimation-based standard errors are now available for bias-reduced propensity score models, which they were not when brglm2 did the fitting; brglm2 is also no longer needed for this.

• M-estimation-based standard errors are now available when by is supplied to weightit() and weightitMSM(). Previously, specifying by omitted the M-estimation components entirely; now the per-stratum components are combined so that glm_weightit() and friends produce standard errors asymptotically equivalent to those from estimating the weights from a single model in which the by variable is fully interacted with all the covariates (at all time points for weightitMSM()). This works for methods that support M-estimation and composes with stabilize.

• link = "softplus" can be used with method = "cbps" and method = "ipt" to use the softplus link.

• weightit.fit() gains a subset argument, restricting which units are used to fit the model.

• The formulas in formula.list supplied to weightitMSM() can now have an empty right hand side (e.g., A_1 ~ 1), which requests a marginal model at that time point. Previously this produced an error, even though weightit() has always allowed it.

• An empty model formula now works with every method. With no covariates there is nothing for any method to model or balance, and every method's target is met by the same weights, but most methods could not run at all on a covariate-free model, failing with errors from the packages they call that said nothing about the cause. Those weights are now computed by fitting an intercept-only GLM whatever method is supplied, ignoring any method-specific arguments. This is an implementation shortcut only: method is reported as supplied, and the package for the requested method need not be installed. weightit(), weightitMSM(), and weightit.fit() all behave this way. method = NULL, a user-defined method function, a supplied ps, and a formula whose only terms are lme4-style random effects (e.g., A ~ (1 | school)) are unaffected. Relatedly, method = "cbps" with is.MSM.method = TRUE now accepts a time point with no covariates, which contributes only its intercept balance condition.

Changes in behavior

• With continuous treatments, estimation of the weights works a bit differently for method = "glm", "bart", "gbm", and "super". Previously, the numerator of the weights (corresponding to the marginal distribution of treatment) was estimated using the argument to density (by default, a normal distribution). Now, density only controls the conditional distribution used to estimate the denominator, and the numerator is estimated by marginalizing over the conditional distribution. This yields a marginal distribution that is compatible with the conditional distribution. In most cases, this will improve balance, but estimation will be a bit slower. Note this makes results of these methods no longer backward compatible. Also note that method = "cbps" is not affected by this change, even though it too estimates generalized propensity score weights.

• The plot argument can no longer be used to visualize the densities for continuous treatments.

• With method = "gbm", method = "surr" is no longer allowed, as it did nothing different from "ind". It is now set to "ind" with a warning.

• The link argument in ordinal_weightit() no longer accepts "log" and "clog" as valid links.

• An argument passed to the subset argument of weightit.fit() was previously silently ignored; it is now honored.

• nobs() on a coxph_weightit object now returns the number of units contributing to the fit (i.e., those with a nonzero weight), consistent with the other *_weightit() methods. Previously the method was defined but never registered, so dispatch fell through to survival:::nobs.coxph(), which returns the number of events. AIC() and BIC() are unaffected, as they read the "nobs" attribute that logLik() carries, but the residual degrees of freedom reported by anova() change accordingly.

• Passing family = "multinomial" to glm_weightit() now results in an error. Previously it would route the input to multinom_weightit().

• multi.method = "brmultinom" and multi.method = "bracl" in weightit() with method = "glm" are deprecated. They are accepted with a warning and now use the equivalent in-house bias-reduced fitters. Note the estimated propensity scores change for the former "bracl": brglm2::bracl() fits an adjacent category logit model, whereas ordinal_weightit() fits a cumulative link (proportional odds) model.

• For ordered multi-category treatments with method = "glm" and multi.method = "weightit", the link argument no longer accepts "identity", "log", or "clog".

Bug fixes

• method = "cbps" now warns when the just-identified CBPS (over = FALSE) fails to solve its balance conditions. Previously this was silent: optim()'s convergence flag only reports whether the iteration limit or a line search failed, and the code never checked whether the balance objective had actually been driven to zero, so a fit that settled at a point where the conditions were still unmet returned weights that did not balance the covariates with no indication anything was wrong. This is most likely with a continuous treatment and many covariates, and depends on the sample rather than the specification alone. See the new Unsolvable Balance Conditions section of ?method_cbps.

• Fixed a bug in tidy() for glm_weightit(), multinom_weightit(), ordinal_weightit(), and coxph_weightit() objects that mislabeled columns. With conf.int = TRUE the two confidence limits were returned unnamed rather than as conf.low and conf.high, and with exponentiate = TRUE every name was shifted by one — the test statistic was labeled std.error — because transforming drops the standard-error column. The names are now taken from the columns actually returned.

• plot() on a summary.weightit object for a continuous treatment no longer triggers a ggplot2 deprecation warning about the size aesthetic for lines.

• Fixed a bug in method = "glm" with a multi-category treatment and multi.method = "glm" that made every M-estimation-based standard error NA. That option fits one one-vs-rest binomial model per treatment level, and the M-estimation parts passed the treatment factor to each of those scores instead of the level's 0/1 indicator, so the score matrix (and hence the whole variance matrix) came out NA. Note this failed silently: glm_weightit() and friends returned a fit whose standard errors were all NA rather than erroring.

• Fixed a bug in method = "glm" with a continuous treatment that made every M-estimation-based standard error an error. The residual standard deviation was omitted from the M-estimation parameter vector, leaving it one element short of the model matrix, so vcov() on a glm_weightit() fit using such weights failed with "non-conformable arguments". vcov = "HC0" and the other non-M-estimation variance types were unaffected. The standard deviation used is now the sampling-weighted root mean square of the residuals, which is the value the M-estimation score for that parameter solves for.

• multinom_weightit() and ordinal_weightit() now warn when the optimization fails, which previously passed silently. Two cases are detected: optim() reporting non-convergence (e.g., from exhausting maxit), and optim() reporting success at a point that does not solve the estimating equations. The latter happens when the optimization is started far from the solution, which can occur when start is supplied from a diverging maximum likelihood fit; the coefficients returned in that case were not a solution of anything, and the score contributions did not sum to 0, so the standard errors were invalid too. The internal starting values are unaffected: multinom_weightit() starts from 0, and ordinal_weightit() with br = TRUE retries from a neutral point.

• Fixed a bug in ordinal_weightit() with link = "loglog" that made the gradient and every robust variance matrix NaN. The "loglog" mu.eta() function evaluated to NaN rather than 0 at a linear predictor of -Inf, which is where the lowest threshold sits.

• When br = TRUE in glm_weightit() and a weightit object supplying M-estimation components is used, the derivative of the outcome score with respect to the weighting model's coefficients is now computed by numeric differentiation. It previously used a shortcut that assumes the outcome score is linear in the weights, which the bias-reducing adjustment is not. Standard errors change slightly.

• Fixed a bug in method = "ebal" with tols greater than 0 that could cause the FISTA solver to stop before the requested balance tolerance was reliably achieved. Thanks to Ivan Geshev for pointing it out.

• Fixed a bug in method = "ebal" and method = "energy" with tols greater than 0, a continuous treatment, and sampling weights that could allow the treatment-covariate correlation to slightly exceed the requested tolerance.

• Fixed a bug in method = "energy" with estimand = "ATC" and moments greater than 0.

• Fixed a bug in method = "energy" and method = "cfd" with min.w set to -Inf.

• Fixed a bug in which method = "gbm" with criterion set to a cross-validation specification (e.g., "cv5") would fail with an error.

• Fixed a bug in method = "cbps" with is.MSM.method = TRUE in which the balance conditions for a multi-category treatment used the treatment levels of the last time point rather than their own. This produced an error when the last time point was binary and silently used the wrong level set when two multi-category time points had the same number of levels.

• Fixed a bug in method = "cbps" with is.MSM.method = TRUE in which a time point at which every unit was treated would compute the weights for all units as though none were treated.

• Fixed a bug in coxph_weightit() in which a rank-deficient model (one with collinear predictors, so that some coefficients are not estimable) would fail with "non-conformable arguments". The score contributions used for the variance had one column per model matrix column, while the model matrix itself had been reduced to its estimable columns. Such fits now succeed, reporting the estimable coefficients and their standard errors and NA for the rest, as glm_weightit() does.

• Fixed a bug in weightitMSM() in which supplying a single formula to num.formula would fail to correctly incorporate its terms into the stabilization model at all but the first time point.

• Fixed a bug in which predict() on an ordinal_weightit object with type = "link" or type = "stdlv" and no newdata supplied could return incorrect values due to a scaling error.

• Fixed a bug in coxph_weightit() in which vcov = "const" would always fail with the error "attempt to set an attribute on NULL". The model-based variance is now returned, matching that of an unweighted survival::coxph() fit.

• Fixed a bug in ordinal_weightit() and multinom_weightit() in which vcov = "const" would always fail with the error "no applicable method for 'vcov' applied to an object of class \"list\"". The model-based variance is now returned, matching that of MASS::polr() and mlogit::mlogit(), respectively.

• Fixed a bug in coxph_weightit() in which a model with no covariates (e.g., Surv(time, event) ~ 1) would fail with the error "'score' residuals are not defined for a null model". Such a model now returns an object with no coefficients and an empty variance matrix, which also makes anova() against a null model work.

• Fixed a bug in glm_weightit() and lm_weightit() in which a model with collinear covariates would fail, with the error "length of 'dimnames' [1] not equal to array extent" when vcov = "const" and "the Hessian for the outcome model could not be inverted" when vcov = "HC0" or vcov = "asympt". As with stats::glm(), the redundant coefficients are now NA, and the variance matrix covers the estimable coefficients.

• Fixed a bug in summary() for glm_weightit, lm_weightit, multinom_weightit, and ordinal_weightit objects in which all standard errors (and the test statistics and p-values derived from them) would be NA when any coefficient was aliased due to collinear covariates.

• Fixed a bug in coxph_weightit() in which a model fit to data with no observed events would fail with the error "object 'jacob' not found" (or "replacement has length zero" for a model with no covariates). As with survival::coxph(), the coefficients are now NA, along with the variance matrix, and a warning is thrown.

• weights is now correctly ignored with a warning in glm_weightit() and friends.

Other

• Added unit tests for all weighting methods.

WeightIt 1.7.0

• coxph_weightit() can now estimate standard errors that account for estimation of the weights using M-estimation as described by Shu et al. (2021).

• weightit() with method = "ebal" now accepts an optional tols argument, which can be used to set a balance threshold for inexact balance. This uses a new algorithm for optimizing with L1 regularization.

• Fixed bugs in entropy balancing that made the M-estimation covariance slightly too small. Thanks to Chad Hazlett for pointing them out.

• When using the tols argument with method = "energy" with a continuous treatment, the imbalance tolerances now yield correlations no larger than the imbalance tolerance as computed with cobalt::col_w_corr(). Previously, they were allowed to be slightly too large.

• glm_weightit(), multinom_weightit(), ordinal_weightit(), and coxph_weightit() are now each documented on their own page.

• The Estimating Effects vignettes (vignette("estimating-effects")) now describes how to use the adrftools package to estimate the effect of a continuous treatment.

• arg is a new dependency for producing error messages and checking arguments.

• Minor changes to plot.summary.weightit(), in particular to remove a deprecation warning from ggplot2.

• In the summary.weightit() output, units with the highest weights are now named according to their rownames in data. summary.weightit.sbps() has been updated to be more in line with summary.weightit().

WeightIt 1.6.0

• Added method = "cfd" for characteristic function distance balancing as described by Santra, Chen, and Park (2026). Energy balancing is a special case of this method.

• Added support for the new version of osqp, which changes some optional argument names and defaults for method = "energy". These should not impact results.

• chk and crayon have been removed as dependencies in favor of cli.

WeightIt 1.5.1

• calibrate() with method = "isoreg" can now be used with sampling weights.

• With predict.ordinal_weightit() and predict.multinom_weightit() with type = "response", an optional level argument can be supplied to request predictions solely for the given outcome level.

• When vcov is "BS" or "FWB" in glm_weightit(), etc., any call to trim() or calibrate() will be applied within each bootstrap replication.

• Fixed a bug when using bootstrapping on glm_weightit(), etc., when the original call to weightit() was called as WeightIt::weightit(). (#85)

• Fixed a bug when using weightit() with only categorical covariates. Thanks to @harshanperera85-commits and Pengfei Guo for pointing it out. (#86)

• When a variable is manually excluded from a formula, e.g., treat ~ . - re78, that variable no longer is included in the covariate output.

WeightIt 1.5.0

• For predict.ordinal_weightit(), type can now be "stdlv" to request predictions on the scale of the standardized latent variable underlying the ordinal responses.

• moments can now be supplied as a named vector with a different integer value for each covariate, e.g., moments = c(x1 = 2, x2 = 3) to balance just the first two moments of x1 and the first three of x2.

• With method = "energy", a new tols argument can be set to request inexact balance on covariate moments.

• Some arguments and documentation for them has changed, in particular moments, int, and subclass, which used to be named arguments of weightit() and are now to be supplied through .... They are documented only on the help pages of the specific methods that allow them.

• The covs component of the weightit() output object now only includes the raw covariates, not any transformations. This will affect cobalt::bal.tab() output.

• In summary.weightit(), a new argument, weight.range can be supplied to specify whether the range of weights in each group should be displayed.

• Fixed a bug in which the output of bread() was off by a factor of -1. This doesn't affect its use in sandwich::sandwich().

• Fixed a bug in which bag.fraction for method = "gbm" with binary and multi-category treatments had a default of .5 instead of the stated 1.

• Fixed bugs relating to method = "optweight"; sampling weights and the fractional weighted bootstrap can now be used with it.

• Fixed bugs related to coxph_weightit() with aliased coefficients and multi-state models. Thanks to Mads Jeppe Tarp-Johansen.

• Fixed a bug in which messages about assuming which treatment level was "treated" were printed many times, including when bootstrapping.

• Fixed a bug in which CBPS for continuous treatments would not yield correct balance in the presence of some extreme density estimates.

• Typo fixes in vignettes and documentation.

WeightIt 1.4.0

• Entropy balancing works slightly differently when sampling weights are supplied. The negative entropy between the estimated weights and the product of the sampling weights and base weights (if any) is now the quantity minimized in the optimization. Previously, the negative entropy between the product of the sampling weights and estimated weights and the base weights was minimized. The new behavior ensures entropy balancing is consistent with mathematically equivalent methods when it ought to be (i.e., CBPS and IPT for the ATT) and prevents counter-intuitive results, like that the ESS after weighting could be larger than that before weighting. Note this will cause results to differ between this and previous versions of WeightIt.

• With method = "cbps", estimand can now be set to "ATO" for binary and multi-category treatments. For binary treatments with the default link, this will yield identical weights to using method = "glm" with estimand = "ATO".

• Two new links can be supplied with method = "glm", "cbps", and "ipt": "loglog" for the log-log link and "clog" for the complementary log link. The link argument can also now be supplied as a link-glm object (e.g., the output of a call to make.link()). This allows for more flexibility in the link function used to estimate the propensity score.

• A new solver argument can be supplied to weightit() with method = "ebal" and with method = "cbps" with over = FALSE (the default); this argument controls whether to use rootSolve::multiroot() or stats::optim() solve the optimization problem for the weights. multiroot() is used by default when rootSolve is installed as it is quicker and more accurate.

• For some methods, analytic formulas for the derivatives used in M-estimation are now used instead of relying on numeric differentiation. This increases speed and accuracy in computing standard errors that adjust for estimation of the weights. The formulas are benchmarked against numeric differentiation results.

• Added new method argument to calibrate() to support isotonic regression calibration as described by van der Laan el al. (2024).

• Added clearer error and warning messages to several functions, most notably glm_weightit() and friends, when missing values are present in the model variables.

• The update() method for glm_weightit objects and friends is now a bit more sophisticated. When data or s.weights are supplied, the weightit object (if any) is refit before refitting the glm_weightit model. This makes it easy to performing bootstrapping by simply calling update() on a fitted object with a new dataset or bootstrap weights.

• The estfun() and bread() methods for glm_weightit objects and friends now correctly extract the estimating function and bread matrices to be used when computing the sandwich covariance matrix using sandwich::sandwich(). estfun() (and thereby sandwich()) have an optional asympt argument, which, controls whether the asymptotic covariance matrix accounting for estimation of the weights is used.

• Improved estimation and convergence for method = "cbps". In particular, for the over-identified CBPS, the generalized inverse is now used only when the GMM weight matrix is singular. Previously, it was always used.

• Improved processing of estimand and focal for binary treatments. weightit() is now better at guessing which level of the treatment is considered "treated", and focal can be used to identify the focal group when requesting the ATT or ATC. (#77)

• Improved estimation of asymptotic and HC0 covariance matrices after glm_weightit() and friends.

• When using method = "super" with SL.method = "method.balance", a new algorithm is used to compute the optimal combination of predictions, which should yield better performance. This may cause results to differ from past versions.

• Fixed a bug when using the fractional weighted bootstrap with glm_weightit() and friends after entropy balancing. This bug is still present with optimization-based weighting (i.e., method = "optweight"), so the fractional weighted bootstrap is no longer allowed with this method.

• Sampling weights can no longer be used with method = "optweight" until a bug is sorted out.

• Performance enhancements.

• Added new tests for CBPS, IPT, and entropy balancing.

• Updated the documentation for msmdata. Thanks to @larsentom. (#79)

WeightIt 1.3.2

• Fixes to tests for CRAN.

• Improvements to weight calculation for continuous treatments with small densities.

WeightIt 1.3.1

• vcov(), summary(), anova(), and confint() for glm_weightit objects (and their relatives) now have a vcov argument that can be used to specify how the variance matrix is computed. This makes it possible to compute a variance matrix different from the one specified in the model fitting call without having to refit the model. anova() now displays which variance matrix was used.

• Added update() methods for glm_weightit, multinom_weightit, ordinal_weightit, and coxph_weightit objects to update the model formula, dataset, or variance matrix. Updating the dataset also refits the weightit object included, if any.

• anova() for glm_weightit objects gets its own help page at help("anova.glm_weightit()").

• Changed defaults with missing = "saem" for binary and multi-category treatments to bypass a bug in misaem code. (#71)

• Preemptively fixed some bugs related to the use of missing, including when missing is used with by.

• The missingness method (if any) is now included in the output of weightit(), weightitMSM(), and weightit.fit() and is printed when using the print() method for these objects.

• When missing = "saem", using vcov = "FWB" in glm_weightit(), etc., now appropriately results in an error. (#71)

• model.matrix.ordinal_weightit() now excludes the (Intercept) column.

• Fixed a bug with predict.multinom_weightit() and predict.ordinal_weightit() when the outcome was not included in newdata.

• Typo fixes in documentation.

WeightIt 1.3.0

• Added anova() methods for glm_weightit, multinom_weightit, ordinal_weightit, and coxph_weightit objects to perform Wald tests for comparing nested models. The models do not have to be symbolically nested.

• Added the new user-facing object .weightit_methods, which contains information on each method and the options allowed with it. This is used within WeightIt for checking arguments but can also be used by other package developers who call functions in WeightIt. See help(".weightit_methods") for details.

• plot.weightit() can be used with method = "optweight" to display the dual variables.

• missing no longer allows partial matching.

• moments can now be set to 0 when quantile is supplied to ensure balance on the quantiles without the moments for the methods that accepts quantiles. Thanks to @BERENZ for the suggestion.

• For ordinal_weightit objects, summary() now has the option to omit thresholds from the output.

• Fixed a bug in ordinal_weightit() where the Hessian (and therefore the HC0 robust variance) were calculated incorrectly when come coefficients were aliased (i.e., due to linearly dependent predictors).

• Fixed a bug in print.summary.glm_weightit() when confidence intervals were requested. A new printing function is used that produces slightly nicer tables.

• Fixes to vignettes and tests to satisfy CRAN checks.

• Minor bug, performance, and readability fixes.

WeightIt 1.2.0

• Added two new functions, multinom_weightit() and ordinal_weightit() for multinomial logistic regression and ordinal regression with capabilities to estimate a covariance matrix that accounts for estimation of the weights using M-estimation. Previously, multinomial logistic regression could be requested using glm_weightit() with family = "multinomial"; this has been deprecated.

• M-estimation can now be used for weighting with ordinal regression for weights with multi-category ordered treatments with method = "glm".

• M-estimation can now be used with bias-reduced regression as implemented in brglm2 for the propensity score (method = "glm" with link = "br.{.}") and for the outcome model (glm_weightit() with br = TRUE). Thanks to Ioannis Kosmidis for supplying some starter code to implement this.

• For any weighting methods with continuous treatments that support a density argument to specify the numerator and denominator densities of the weights, density can now be specified as "kernel" to request kernel density estimation. Previously, this was requested by setting use.kernel = TRUE, which is now deprecated.

• Standard errors are now correctly computed when an offset is included in glm_weightit(). Thanks to @zeynepbaskurt. (#63)

• Improved robustness of get_w_from_ps() to propensity scores of 0 and 1.

• Updates to weightit() with method = "gbm":

  • use.offset is now tunable.
  • The same random seed is used across specifications as requested by @mitchellcameron123. (#64)
  • For binary and multi-category treatments with cross-validation used as the criterion, class.stratify.cv is now set to TRUE by default to stratify on treatment.
  • For continuous treatments, the default density now corresponds to the distribution requested.
  • plot() can be used on the output of weightit() to display the results of the tuning process; see help("plot.weightit") for details.
  • Fixed a bug where distribution was not included in the output when tuned.
  • Fixed a bug when propensity scores were estimated to be 0 or 1. Thanks to @mitchellcameron123. Propensity scores are now shifted slightly away from 0 or 1. (#64)

• When using weightit() with method = "super" for binary and multi-category treatments, cross-validation now stratifies on treatment, as recommended by Phillips et al. (2023).

• Fixed a bug and clarified some error messages when using ordered treatments with method = "glm". Thanks to Steve Worthington for pointing them out.

• Updated the help page of get_w_from_ps() to include formulas for the weights.

WeightIt 1.1.0

• Added a new function, coxph_weightit(), for fitting Cox proportional hazards models in the weighted sample, with the option of accounting for estimation of the weights in computing standard errors via bootstrapping. This function uses the summary() and print() methods for glm_weightit objects, which are different from those for coxph objects.

• glm_weightit() gets a new print() method that omits some invalid statistics displayed by the print() method for glm objects and displays the type of standard error estimated.

• summary.glm_weightit() (which is also used for coxph_weightit objects) gets a new argument, transform, which can be used to transform the displayed coefficients and confidence interval bounds (if requested), e.g., by exponentiating them.

• M-estimation is now supported for method = "glm" with continuous treatments.

• A new estimator is now used for method = "cbps" with longitudinal treatments (i.e., using weightitMSM()). Previously, the weights from CBPS applied to each time point were multiplied together. Now, balance at all time points is optimized using a single set of weights. This implementation is close to that described by Huffman and van Gameren (2018), not that of Imai and Ratkovic (2015).

• A new estimator is now used for method = "cbps" with continuous treatments. The unconditional mean and variance are now included as parameters to be estimated. For the just-identified CBPS, this will typically improve balance, but results will depart from those found using CBPS::CBPS().

• For point treatments (i.e., using weightit()), the stabilize argument has some new behavior. It can now be be specified as a formula, and the stabilization factor is estimated separately and included in the M-estimation if allowed. It can now only be used when estimand = "ATE" (weights for other estimands should not be stabilized).

• For binary treatments with method = "glm", link can now be specified as "flic" or "flac" to use Firth corrected logistic regression as implemented in the logistf package.

• With method = "gbm", an error is now thrown if criterion (formerly known as stop.method) is supplied as anything other than a string.

• For binary and continuous treatments with method = "gbm", a new argument, use.offset, can be supplied, which, if TRUE, uses the linear predictor from a generalized linear model as an offset in the boosting model, which can improve performance.

• Added a section on conducting moderation analysis to the estimating effect vignette (vignette("estimating-effects")).

• Fixed a bug when using M-estimation for sequential treatments with weightitMSM() and stabilize = TRUE. Standard errors incorrectly accounted for estimation of the stabilization factor; they are now correct.

• Fixed a bug when using method = "ipt" for the ATE.

• Fixed a bug when some coefficients were aliased for glm_weightit(). Thanks to @kkwi5241.

• Updated kernel balancing example in method_user.

• Improved warnings and errors for bad models throughout the package.

WeightIt 1.0.0

• Added a new function, glm_weightit() (along with wrapper lm_weightit()) and associated methods for fitting generalized linear models in the weighted sample, with the option of accounting for estimation of the weights in computing standard errors via M-estimation or two forms of bootstrapping. glm_weightit() also supports multinomial logistic regression in addition to all models supported by glm(). Cluster-robust standard errors are supported, and output is compatible with any functions that accept glm() objects. Not all weighting methods support M-estimation, but for those that do, a new component is added to the weightit output object. Currently, GLM propensity scores, entropy balancing, just-identified CBPS, and inverse probability tilting (described below) support M-estimation-based standard errors with glm_weightit().

• Added inverse probability tilting (IPT) as described by Graham, Pinto, and Egel (2012), which can be requested by setting method = "ipt". This is similar to entropy balancing and CBPS in that it enforces exact balance and yields a propensity score, but has some theoretical advantages to both methods. IPT does not rely on any other packages and runs very quickly.

• Estimating covariate balancing propensity score weights (i.e., method = "cbps") no longer depends on the CBPS package. The default is now the just-identified versions of the method; the over-identified version can be requested by setting over = TRUE. The ATT for multi-category treatments is now supported, as are arbitrary numbers of treatment groups (CBPS only natively support up to 4 groups and only the ATE for multi-category treatments). For binary treatments, generalized linear models other than logistic regression are now supported (e.g., probit or Poisson regression).

• New function calibrate() to apply Platt scaling to calibrate propensity scores as recommended by Gutman et al. (2024).

• A new argument quantile can be supplied to weightit() with all the methods that accept moments and int ("ebal", "cbps", "ipt", "npcbps", "optweight", and "energy"). This allows one to request balance on the quantiles of the covariates, which can add some robustness as demonstrated by Beręsewicz (2023).

• as.weightit() now has a method for weightit.fit objects, which now have additional components included in the output.

• trim() now has a drop argument; setting to TRUE sets the weights of all trimmed units to 0 (effectively dropping them).

• When using weightit() with a continuous treatment and a method that estimates the generalized propensity score (e.g., "glm", "gbm", "super"), sampling weights are now be incorporated into the density when use.kernel = FALSE (the default) when supplied to s.weights. Previously they were ignored in calculating the density, but have always been and remain used in the modeling the treatment (when allowed).

• Fixed a bug when criterion was not specified when using method = "gbm".

• Fixed a bug when ps was supplied for continuous treatments. Thanks to @taylordunn. (#53)

• Warning messages now display immediately rather than at the end of evaluation.

• The vignettes have been changed to use a slightly different estimator for weighted g-computation. The estimated weights are no longer to be included in the call to avg_comparisons(), etc.; that is, they are only used to fit the outcome model. This makes the estimators more consistent with other software, including teffects ipwra in Stata, and most of the literature on weighted g-computation. Note this will not effect any estimates for the ATT or ATC and will only yield at most minor changes for the ATE. For other estimands (e.g., ATO), the weights are still to be included.

• The word "multinomial" to describe treatments with more than two categories has been replaced with "multi-category" in all documentation and messages.

• Transferred all help files to Roxygen and reorganized package scripts.

• Reorganization of some functions.

WeightIt 0.14.2

• Fixed a bug when using estimand = "ATC" with multi-category treatments. (#47)

• Fixed a bug in the Estimating Effects vignette. (#46)

WeightIt 0.14.1

• cobalt version 4.5.1 or greater is now required.

• Fixed a bug when using balance Super Learner with cobalt 4.5.1.

• Added a section to the Estimating Effects vignette (vignette("estimating-effects")) on estimating the effect of a continuous treatment after weighting.

WeightIt 0.14.0

• Added energy balancing for continuous treatments, requested using method = "energy", as described in Huling et al. (2023). These weights minimize the distance covariance between the treatment and covariates while maintaining representativeness. This method supports exact balance constraints, distributional balance constraints, and sampling weights. The implementation is similar to that in the independenceWeights package. See ?method_energy for details.

• Added a new vignette on estimating effects after weighting, accessible using vignette("estimating-effects", package = "WeightIt"). The new workflow relies on the marginaleffects package. The main vignette (vignette("WeightIt")) has been modernized as well.

• Added a new dataset, msmdata, to demonstrate capabilities for longitudinal treatments. twang is no longer a dependency.

• Methods that use a balance criterion to select a tuning parameter, in particular GBM and balance Super Learner, now rely on cobalt's bal.init() and bal.compute() functionality, which adds new balance criteria. The stop.method argument for these functions has been renamed to criterion and help("stop.method") has been removed; the same page is now available at help("bal.compute", package = "cobalt"), which describes the additional statistics available. This also fixes some bugs that were present in some balance criteria.

• Renamed method = "ps" to method = "glm". "ps" continues to work as it always had for back compatibility. "glm" is a more descriptive name since many methods use propensity scores; what distinguishes this method is that it uses generalized linear models.

• Using method = "ebcw" for empirical balancing calibration weighting is no longer available because the ATE package has been removed. Use method = "ebal" for entropy balancing instead, which is essentially identical.

• Updated the trim() documentation to clarify the form of trimming that is implemented (i.e., winsorizing). Suggested by David Novgorodsky.

• Fixed bugs when some s.weights are equal to zero with method = "ebal", "cbps", and "energy". Suggested by @statzhero. (#41)

• Improved performance of method = "energy" for the ATT.

• Fixed a bug when using method = "energy" with by.

• With method = "energy", setting int = TRUE automatically sets moments = 1 if unspecified.

• Errors and warnings have been updated to use chk.

• The missingness indicator approach now imputes the variable median rather than 0 for missing values. This will not change the performance of most methods, but change others, and doesn't affect balance assessment.

WeightIt 0.13.1

• For ordinal multi-category treatments, setting link = "br.logit" now uses brglm2::bracl() to fit a bias-reduced ordinal regression model.

• Added the vignette "Installing Supporting Packages" to explain how to install the various packages that might be needed for WeightIt to use certain methods, including when the package is not on CRAN. See the vignette at vignette("installing-packages").

• Fixed a bug that would occur when a factor or character predictor with a single level was passed to weightit().

• Improved the code for entropy balancing, fixing a bug when using s.weights with a continuous treatment and improving messages when the optimization fails to converge. (#33)

• Improved robustness of documentation to missing packages.

• Updated the logo, thanks to Ben Stillerman.

WeightIt 0.13.0

• Fixed a bug that would occur when the formula.tools package was loaded, which would occur most commonly when logistf was loaded. It would cause the error The treatment and covariates must have the same number of units. (#25)

• Fixed a bug where the info component would not be included in the output of weightit() when using method = "super".

• Added the ability to specify num.formula as a list of formulas in weightitMSM(). This is primarily to get around the fact that when stabilize = TRUE, a fully saturated model with all treatments is used to compute the stabilization factor, which, for many time points, is time-consuming and may be impossible (especially if not all treatment combinations are observed). Thanks to @maellecoursonnais for bringing up this issue (#27).

• ps.cont() has been retired since the same functionality is available using weightit() with method = "gbm" and in the twangContinuous package.

• With method = "energy", a new argument, lambda, can be supplied, which puts a penalty on the square of the weights to control the effective sample size. Typically this is not needed but can help when the balancing is too aggressive.

• With method = "energy", min.w can now be negative, allowing for negative weights.

• With method = "energy", dist.mat can now be supplied as the name of a method to compute the distance matrix: "scaled_euclidean", "mahalanobis", or "euclidean".

• Support for negative weights added to summary(). Negative weights are possible (though not by default) when using method = "energy" or method = "optweight".

• Fixed a bug where glm() would fail to converge with method = "ps" for binary treatments due to bad starting values. (#31)

• miss = "saem" can once again be used with method = "ps" when missing values are present in the covariates.

• Fixed bugs with processing input formulas.

• An error is now thrown if an incorrect link is supplied with method = "ps".

WeightIt 0.12.0

• The use of method = "twang" has been retired and will now give an error message. Use method = "gbm" for nearly identical functionality with more options, as detailed at ?method_gbm.

• With multinomial treatments with link = "logit" (the default), if the mclogit package is installed, it can be requested for estimating the propensity score by setting the option use.mclogit = TRUE, which uses mclogit::mblogit(). It should give the same results as the default, which uses mlogit, but can be faster and so is recommended.

• Added a plot() method for summary.weightitMSM objects that functions just like plot.summary.weightit() for each time point.

• Fixed a bug in summary.weightit() where the labels of the top weights were incorrect. Thanks to Adam Lilly.

• Fixed a bug in sbps() when using a stochastic search (i.e., full.search = FALSE or more than 8 moderator levels). (#17)

• Fixed a bug that would occur when all weights in a treatment group were NA. Bad weights (i.e., all the same) now produce a warning rather than an error so the weights can be diagnosed manually. (#18)

• Fixed a bug when using method = "energy" with estimand = "ATE" and improved = TRUE (the default). The between-treatment energy distance contribution was half of what it should have been; this has now been corrected.

• Added L1 median measure as a balance criterion. See ?stop.method for details.

• Fixed a bug where logical treatments would yield an error. (#21)

• Fixed a bug where Warning: Deprecated would appear sometimes when purrr (part of the tidyverse) was loaded. (#22) Thanks to MrFlick on StackOverflow for the solution.

WeightIt 0.11.0

• Added support for estimating propensity scores using Bayesian additive regression trees (BART) with method = "bart". This method fits a BART model for the treatment using functions in the dbarts package to estimate propensity scores that are used in weights. Binary, multinomial, and continuous treatments are supported. BART uses Bayesian priors for its hyperparameters, so no hyperparameter tuning is necessary to get well-performing predictions.

• Fixed a bug when using method = "gbm" with stop.method = "cv{#}".

• Fixed a bug when setting estimand = "ATC" for methods that produce a propensity score. In the past, the output propensity score was the probability of being in the control group; now, it is the probability of being in the treated group, as it is for all other estimands. This does not affect the weights.

• Setting method = "twang" is now deprecated. Use method = "gbm" for improved performance and increased functionality. method = "twang" relies on the twang package; method = "gbm" calls gbm directly.

• Using method = "ebal" no longer requires the ebal package. Instead, optim() is used, as it has been with continuous treatments. Balance is a little better, but some options have been removed.

• When using method = "ebal" with continuous treatments, a new argument, d.moments, can now be specified. This controls the number of moments of the covariate and treatment distributions that are constrained to be the same in the weighted sample as they are in the original sample. Vegetabile et al. (2020) recommend setting d.moments to at least 3 to ensure generalizability and reduce bias due to effect modification.

• Made some minor changes to summary.weightit() and plot.summary.weightit(). Fixed how negative entropy was computed.

• The option use.mnlogit in weightit() with multi-category treatments and method = "ps" has been removed because mnlogit appears uncooperative.

• Fixed a bug (#16) when using method = "cbps" with factor variables, thanks to @danielebottigliengo.

• Fixed a bug when using binary factor treatments, thanks to Darren Stewart.

• Cleaned up the documentation.

WeightIt 0.10.2

• Fixed a bug where treatment values were accidentally switched for some methods.

WeightIt 0.10.1

• With method = "gbm", added the ability to tune hyperparameters like interaction.depth and distribution using the same criteria as is used to select the optimal tree. A summary of the tuning results is included in info in the weightit output object.

• Fixed a bug where moments and int were ignored unless both were specified.

• Effective sample sizes now print only up to two digits (believe me, you don't need three) and print more cleanly with whole numbers.

• Fixed a bug when using by, thanks to @frankpopham. (#11)

• Fixed a bug when using weightitMSM with methods that process int and moments (though you probably shouldn't use them anyway). Thanks to Sven Reiger.

• Fixed a bug when using method = "npcbps" where weights could be excessively small and mistaken for all being the same. The weights now sum to the number of units.

WeightIt 0.10.0

• Added support for energy balancing with method = "energy". This method minimizes the energy distance between samples, which is a multivariate distance measure. This method uses code written specifically for WeightIt (i.e., it does not call a package specifically designed for energy balancing) using the osqp package for the optimization (same as optweight). See Huling & Mak (2020) for details on this method. Also included is an option to require exact balance on moments of the covariates while minimizing the energy distance. The method works for binary and multinomial treatments with the ATE, ATT, or ATC. Sampling weights are supported. Because the method requires the calculation and manipulation of a distance matrix for all units, it can be slow and/or memory intensive for large datasets.

• Improvements to method = "gbm" and to method = "super" with SL.method = "method.balance". A new suite of stop.methods are allowed. For binary treatments, these include the energy distance, sample Mahalanobis distance, and pseudo-R2 of the weighted treatment model, among others. See ?stop.method for allowable options. In addition, performance for both is quite a bit faster.

• With multinomial treatments with link = "logit" (the default), if the mnlogit package is installed, it can be requested for estimating the propensity score by setting the option use.mnlogit = TRUE. It should give the same results as the default, which uses mlogit, but can be faster for large datasets.

• Added option estimand = "ATOS" for the "optimal subset" treatment effect as described by Crump et al. (2009). This estimand finds the subset of units who, with ATE weights applied, yields a treatment effect with the lowest variance, assuming homoscedasticity (and other assumptions). It is only available for binary treatments with method = "ps". In general it makes more sense to use estimand = "ATO" if you want a low-variance estimate and don't care about the target population, but I added this here for completeness. It is available in get_w_from_ps() as well.

• make_full_rank() is now faster.

• Cleaning up of some error messages.

• Fixed a bug when using link = "log" for method = "ps" with binary treatments.

• Fixed a bug when using method = "cbps" with continuous treatments and sampling weights. Previously the returned weights included the sampling weights multiplied in; now they are separated, as they are in all other scenarios and for all other methods.

• Improved processing of non-0/1 binary treatments, including for method = "gbm". A guess will be made as to which treatment is considered "treated"; this only affects produced propensity scores but not weights.

• Changed default value of at in trim() from .99 to 0.

• Added output for the number of weights equal to zero in summary.weightit. This can be especially helpful when using "optweight" or "energy" methods or when using estimand = "ATOS".

WeightIt 0.9.0

• Added support for entropy balancing (method = "ebal") for continuous treatments as described by Tübbicke (2020). Relies on hand-written code contributed by Stefan Tübbicke rather than another R package. Sampling weights and base weights are both supported as they are with binary and multi-category treatments.

• Added support for Balance SuperLearner as described by Pirracchio and Carone (2018) with method = "super". Rather than using NNLS to choose the optimal combination of predictions, you can now optimize balance. To do so, set SL.method = "method.balance". You will need to set an argument to stop.method, which works identically to how it does for method = "gbm". For example, for stop.method = "es.max", the predicted values given will be the combination of predicted values that minimizes the largest absolute standardized mean difference of the covariates in the sample weighted using the predicted values as propensity scores.

• Changed some of the statistics displayed when using summary(): the weight ratio is gone (because weights can be 0, which is not problematic but would explode the ratio), and the mean absolute deviation and entropy of the weights are now present.

• Added crayon for prettier printing of summary() output.

WeightIt 0.8.0

• Formula interfaces now accept poly(x, .) and other matrix-generating functions of variables, including the rms-class-generating functions from the rms package (e.g., pol(), rcs(), etc.) (the rms package must be loaded to use these latter ones) and the basis-class-generating functions from the splines package (i.e., bs() and ns()). A bug in an early version of this was found by @ahinton-mmc.

• Added support for marginal mean weighting through stratification (MMWS) as described by Hong (2010, 2012) for weightit() and get_w_from_ps() through the subclass argument (see References at ?get_w_from_ps). With this method, subclasses are formed based on the propensity score and weights are computed based on the number of units in each subclass. MMWS can be used with any method that produces a propensity score. The implementation here ensures all subclasses have a least one member by filling in empty subclasses with neighboring units.

• Added stabilize option to get_w_from_ps().

• A new missing argument has been added to weightit() to choose how missing data in the covariates is handled. For most methods, only "ind" (i.e., missing indicators with single-value imputation) is allowed, but for "ps", "gbm", and "twang", other methods are possible. For method = "ps", a stochastic approximation of the EM algorithm (SAEM) can be used through the misaem package by setting missing = "saem".

• For continuous treatments with the "ps", "gbm", and "super" methods (i.e., where the conditional density of the treatment needs to be estimated), the user can now supply their own density as a string or function rather than using the normal density or kernel density estimation. For example, to use the density of the t-distribution with 3 degrees of freedom, one can set density = "dt_3". T-distributions often work better than normal distributions for extreme values of the treatment.

• Some methods now have an info component in the output object. This contains information that might be useful in diagnosing or reporting the method. For example, when method = "gbm", info contains the tree that was used to compute the weights and the balance resulting from all the trees, which can be plotted using plot(). When method = "super", info contains the coefficients in the stacking model and the cross-validation risk of each of the component methods.

• For method = "gbm", the best tree can be chosen using cross validation rather than balance by setting stop.method = "cv5", e.g., to do 5-fold cross-validation.

• For method = "gbm", a new optional argument start.tree can be set to select the tree at which balance begins to be computed. This can speed things up when you know that the best tree is not within the first 100 trees, for example.

• When using method = "gbm" with multi-category treatments and estimands other than the ATE, ATT, or ATC are used with standardized mean differences as the stopping rule, the mean differences will be between the weighted overall sample and each treatment group. Otherwise, some efficiency improvements.

• When using method = "ps" with multi-category treatments, the use of use.mlogit = FALSE to request multiple binary regressions instead of multinomial regression is now documented and an associated bug is now fixed, thanks to @ahinton-mmc.

• When use method = "super", one can now set discrete = TRUE to use discrete SuperLearner instead of stacked SuperLearner, but you probably shouldn't.

• moments and int can now be used with method = "npcbps".

• Performance enhancements.

WeightIt 0.7.1

• Fixed bug when using weightit() inside another function that passed a by argument explicitly. Also changed the syntax for by; it must now either be a string (which was always possible) or a one-sided formula with the stratifying variable on the right-hand side. To use a variable that is not in data, you must use the formula interface.

• Fixed bug when trying to use ps with by in weightit().

WeightIt 0.7.0

• Added new sbps() function for estimating subgroup balancing propensity score weights, including both the standard method and a new smooth version.

• Setting method = "gbm" and method = "twang" will now do two different things. method = "gbm" uses gbm and cobalt functions to estimate the weights and is much faster, while method = "twang" uses twang functions to estimate the weights. The results are similar between the two methods. Prior to this version, method = "gbm" and method = "twang" both did what method = "twang" does now.

• Bug fixes when stabilize = TRUE, thanks to @ulriksartipy and Sven Rieger.

• Fixes for using base.weight argument with method = "ebal". Now the supplied vector should have a length equal to the number of units in the dataset (in contrast to its use in ebalance, which requires a length equal to the number of control units).

• Restored dependency on cobalt for examples and vignette.

• When method = "ps" and the treatment is ordered (i.e., ordinal), MASS::polr() is used to fit an ordinal regression. Make the treatment un-ordered to to use multinomial regression instead.

• Added support for using bias-reduced fitting functions when method = "ps" as provided by the brglm2 package. These can be accessed by changing the link to, for example, "br.logit" or "br.probit". For multinomial treatments, setting link = "br.logit" fits a bias-reduced multinomial regression model using brglm2::brmultinom(). This can be helpful when regular maximum likelihood models fail to converge, though this may also be a sign of lack of overlap.

WeightIt 0.6.0

• Bug fixes. Functions now work better when used inside other functions (e.g., lapply).

• Behavior of weightit() in the presence of non-NULL focal has changed. When focal is specified, estimand is assumed to be ATT. Previously, focal would be ignored unless estimand = "ATT".

• Processing of estimand and focal is improved. Functions are smarter about guessing which group is the focal group when one isn't specified, especially with non-numeric treatments. focal can now be used with estimand = "ATC" to indicate which group is the control group, so "ATC" and "ATT" now function more similarly.

• Added function get_w_from_ps() to transform propensity scores into weights (instead of having to go through weightit()).

• Added functions as.weightit() and as.weightitMSM() to convert weights and treatments and other components into weightit objects so that summary.weightit() can be used on them.

• Updated documentation to describe how missing data in the covariates is handled. Some bugs related to missing data have been fixed as well, thanks to Yong Hao Pua.

• ps.cont() had the "z-transformed correlation" options removed to simplify output. This function and its supporting functions will be deprecated as soon as the new version of twang is released.

• When using method = "ps" or method = "super" with continuous treatments, setting use.kernel = TRUE and plot = TRUE, the plot is now made with ggplot2 rather than the base R plots.

• Added plot.summary.weightit() to plot the distribution of weights (a feature also in optweight).

• Removed dependency on cobalt temporarily, which means the examples and vignette won't run.

• Added ggplot2 to Imports.

WeightIt 0.5.1

• Fixed a bug when using the ps argument in weightit().

• Fixed a bug when setting include.obj = TRUE in weightitMSM().

• Added warnings for using certain methods with longitudinal treatments as they are not validated and may lead to incorrect inferences.

WeightIt 0.5.0

• Added super method to estimate propensity scores using the SuperLearner package.

• Added optweight method to estimate weights using optimization (but you should probably just use the optweight package).

• weightit() now uses the correct formula to estimate weights for the ATO with multinomial treatments as described by Li & Li (2018).

• Added include.obj option in weightit() and weightitMSM() to include the fitted object in the output object for inspection. For example, with method = "ps", the glm object containing the propensity score model will be included in the output.

• Rearranged the help pages. Each method now has its own documentation page, linked from the weightit help page.

• Propensity scores are now included in the output for binary treatments with gbm and cbps methods. Thanks to @Blanch-Font for the suggestion.

• Other bug fixes and minor changes.

WeightIt 0.4.0

• Added trim() function to trim weights.

• Added ps.cont() function, which estimates generalized propensity score weights for continuous treatments using generalized boosted modeling, as in twang. This function uses the same syntax as ps() in twang, and can also be accessed using weightit() with method = "gbm". Support functions were added to make it compatible with twang functions for assessing balance (e.g., summary, bal.table, plot). Thanks to Donna Coffman for enlightening me about this method and providing the code to implement it.

• The input formula is now much more forgiving, allowing objects in the environment to be included. The data argument to weightit() is now optional. To simplify things, the output object no longer contains a data field.

• Under-the-hood changes to facilitate adding new features and debugging. Some aspects of the output objects have been slightly changed, but it shouldn't affect use for most users.

• Fixed a bug where variables would be thrown out when method = "ebal".

WeightIt 0.3.2

• Added new moments and int options for some weightit() methods to easily specify moments and interactions of covariates.

• Fixed bug when using objects not in the data set in weightit(). Behavior has changed to include transformed covariates entered in formula in weightit() output.

• Fixed bug resulting from potential collinearity when using ebal or ebcw.

• Added a vignette.

WeightIt 0.3.1

• Edits to code and help files to protect against missing CBPS package.

• Corrected sampling weights functionality so they work correctly. Also expanded sampling weights to be able to be used with all methods, including those that do not natively allow for sampling weights (e.g., ATE).

• Minor bug fixes and spelling corrections.

WeightIt 0.3.0

• Added weightitMSM() function (and supporting print() and summary() functions) to estimate weights for marginal structural models with time-varying treatments and covariates.

• Fixed some bugs, including when using CBPS with continuous treatments, and when using focal incorrectly.

WeightIt 0.2.0

• Added method = "sbw" for stable balancing weights (now removed and replaced with method = "optweight")

• Allowed for estimation of multinomial propensity scores using multiple binary regressions if mlogit is not installed

• Allowed for estimation of multinomial CBPS using multiple binary CBPS for more than 4 groups

• Added README and NEWS

WeightIt 0.1.0

• First version!

WeightIt documentation built on Aug. 4, 2026, 1:09 a.m.