  We present csSampling, an R package for estimation of Bayesian models for
data collected from complex survey samples. csSampling combines functionality
from the probabilistic programming language Stan (via the rstan and brms R
packages) and the handling of complex survey data from the survey R package.
Under this approach, the user creates a survey-weighted model in brms or
provides a custom weighted model via rstan. Survey design information is
provided via the svydesign function of the survey package. The cs_sampling
function of csSampling estimates the weighted stan model and provides an
asymptotic covariance correction for model mis-specification due to using
survey sampling weights as plug-in values in the likelihood. This is often
known as a ``design effect'' which is the ratio between the variance from a
complex survey sample and a simple random sample of the same size. The
resulting adjusted posterior draws can then be used for the usual Bayesian
inference while also achieving frequentist properties of asymptotic consistency
and correct uncertainty (e.g. coverage).
