  Propensity score weighting is an important tool for comparative effectiveness
research.Besides the inverse probability of treatment weights (IPW), recent
development has introduced a general class of balancing weights, corresponding
to alternative target populations and estimands. In particular, the overlap
weights (OW) lead to optimal covariate balance and estimation efficiency, and a
target population of scientific and policy interest. We develop the R package
PSweight to provide a comprehensive design and analysis platform for causal
inference based on propensity score weighting. PSweight supports (i) a variety
of balancing weights, (ii) binary and multiple treatments,(iii) simple and
augmented weighting estimators, (iv) nuisance-adjusted sandwich variances,
and(v) ratio estimands. PSweight also provides diagnostic tables and graphs for
covariate balance assessment. We demonstrate the functionality of the package
using a data example from the NationalChild Development Survey (NCDS), where we
evaluate the causal effect of educational attainment on income.
