Builds survey weights from design base weights by chaining
    hierarchical adjustments (unknown eligibility, nonresponse and calibration)
    through a declarative, pipeable, 'tidymodels'-style API. Calibration follows
    Deville and Sarndal (1992) <doi:10.2307/2290268>. Variances are obtained with
    a bootstrap that resamples primary sampling units and re-applies the whole
    recipe on each replicate, following the rescaling bootstrap of Rao and Wu
    (1988) <doi:10.1080/01621459.1988.10478591>, so the replicate weights carry
    the variability of every adjustment. The weights also bridge to the 'survey'
    and 'srvyr' packages for design-based inference.

Generates balancing weights for causal effect estimation in observational studies with
             binary, multi-category, or continuous point or longitudinal treatments by easing and
             extending the functionality of several R packages and providing in-house estimation methods.
             Available methods include those that rely on parametric modeling, optimization, and machine learning. Also
             allows for assessment of weights and checking of covariate balance by interfacing directly
             with the 'cobalt' package. Methods for estimating weighted regression models that take into account 
             uncertainty in the estimation of the weights via M-estimation or bootstrapping are available. See the vignette "Installing Supporting Packages" for instructions on how
             to install any optional package 'WeightIt' uses, including those that may not be on CRAN.

Applies the item weighting method from Kilic & Dogan (2019) <doi:10.21031/epod.516057>. To improve construct validity, this method re-computes scores by utilizing the item discrimination index in conjunction with a condition established upon person ability and item difficulty.

Estimation of observation-specific weights for incomplete longitudinal data and bootstrap procedure for weighted quantile regressions. See Jacqmin-Gadda, Rouanet, Mba, Philipps, Dartigues (2020) for details <doi:10.1177/0962280220909986>.