Added b.weights argument to supply base weights. When supplied, rather than minimizing the variance of the weights, the squared distance from each base weight is minimized, mirroring the functionality of the base.weights argument in ebalance for entropy balancing.
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.
Negative values are now allowed for the min.w argument of optweight.fit().
osqp instead of rosqp now that osqp works. cobalt is back.rosqp instead of osqp due to package failure. Also removed reliance on cobalt in favor of MatchIt for data. Both changes are temporary.The rosqp package is now osqp, and is faster with fewer bugs.
If focal is set, the estimand is automatically changed to "ATT". In the past, focal would be ignored unless estimand = "ATT".
Fixed some bugs with processing formula inputs. In particular, functions can be used inside lapply loops and nested functions more gracefully.
Other bugs fixes and small changes.
Changed default min.w in optweight.fit() and optweight.svy.fit() to 1E-8 from 0. This ensures all weights are nonzero, which can reduce bugs in other functions that require nonzero weights (e.g, summ() in jtools and svyglm() in survey`).
Fixed warning that would occur when interactions were present in the model formula in optweight().
optweights have been discovered to be invalid for longitudinal treatments, so attempting to use optweight()or optweight.fit() with longitudinal treatments will now produce an error. This can be overridden by setting force = TRUE, though this is not recommended until further research is done.
Added optweight.svy and associated methods and functions for estimating survey weights using optimization. These weights when applied to the sample will yield a sample whose covariate means are equal (within a specified tolerance) to given target values.
Minor changes to check.targets. It will now produce the covariate means when the targets argument is empty and will produce the previous empty output, a named vector of NAs, when targets = NULL.
Changes to how dual variables are processed and displayed. Now, each dual variable coming from optweight represents the change in the objective function corresponding to a 1-unit change in tols. The reported duals are the sum of all the duals affected by the constraint, so you can now reliably predict the change in the objective function from a change in tols (it was obscured and error-prone previously). The distinction between targeting duals and balance duals is maintained.