Version 0.2.2

- Fixed bug with duals displaying improperly when factor variables are present.

Version 0.2.1

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.

Version 0.2.0

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`NA`

s, 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.

Version 0.1.0

- First version!