  A common problem in many disciplines is the need to assign a set of items
into categories or classes with known labels. This is often done by one or more
expert raters, or sometimes by an automated process. If these assignments or
`ratings' are difficult to make accurately, a common tactic is to repeat them
by different raters, or even by the same rater multiple times on different
occasions. We present an R package `rater`, available on CRAN, that implements
Bayesian versions of several statistical models for analysis of repeated
categorical rating data. Inference is possible for the true underlying (latent)
class of each item, as well as the accuracy of each rater. The models are
extensions of, and include, the Dawid-Skene model, and we implemented them
using the Stan probabilistic programming language. We illustrate the use of
`rater` through a few examples. We also discuss in detail the techniques of
marginalisation and conditioning, which are necessary for these models but also
apply more generally to other models implemented in Stan.
