We describe an R package focused on Bayesian analysis of dynamic linear models. The main features of the package are its flexibility to deal with a variety of constant or time-varying, univariate or multivariate models, and the numerically stable singular value decomposition-based algorithms used for filtering and smoothing. In addition to the examples of "out-of-the-box" use, we illustrate how the package can be used in advanced applications to implement a Gibbs sampler for a user-specified model.