Walker provides a method for fully Bayesian linear regression where the regression coefficients are allowed to vary over "time", either as independent random walks.
Update: walker now supports also Poisson regression with time-varying coefficients!
All computations are done using Hamiltonian Monte Carlo provided by Stan, using a state space representation of the model in order to marginalise over the coefficients for accurate and efficient sampling.
See the package vignette for details and an example.