  The R package walker extends standard Bayesian general linear models to the
case where the effects of the explanatory variables can vary in time. This
allows, for example, to model the effects of interventions such as changes in
tax policy which gradually increases their effect over time. The Markov chain
Monte Carlo algorithms powering the Bayesian inference are based on Hamiltonian
Monte Carlo provided by Stan software, using a state space representation of
the model to marginalise over the regression coefficients for efficient
low-dimensional sampling.
