Version 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().
Version 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.
Version 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.
Version 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.
Version 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.
Version 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".
Added support for sampling weights with stable balancing weighting and empirical balancing calibration weighting.
Version 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 potentially colinearity when using ebal or ebcw.
Added a vignette.
Version 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., sbw and ATE)
Minor bug fixes and spelling corrections.
Version 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.
Version 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
Version 0.1.0