method argument to calibrate() to support isotonic regression calibration as described by van der Laan el al. (2024).Fixes to tests for CRAN.
Improvements to weight calculation for continuous treatments with small densities.
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.
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.
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.class.stratify.cv is now set to TRUE by default to stratify on treatment.plot() can be used on the output of weightit() to display the results of the tuning process; see help("plot.weightit") for details.distribution was not included in the output when tuned.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.
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.
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". Thus 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.
Fixed a bug when using estimand = "ATC" with multi-category treatments. (#47)
Fixed a bug in the Estimating Effects vignette. (#46)
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.
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.
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.
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".
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.
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.
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.
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".
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.
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.
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().
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.
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.
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.
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.
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".
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.
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.
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.
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