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 tretaments 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
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