Bayesian dynamic regression models where the regression
coefficients can vary over time as random walks.
Gaussian, Poisson, and binomial observations are supported.
The Markov chain Monte Carlo 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 efficient sampling.
For non-Gaussian models, walker uses the importance sampling type estimators based on
approximate marginal MCMC as in Vihola, Helske, Franks (2017,
Walker provides a method for fully Bayesian generalized linear regression where the regression coefficients are allowed to vary over "time" as a first or second order integrated random walk.
The Markov chain Monte Carlo (MCMC) algorithm uses 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. For non-Gaussian models the MCMC targets approximate marginal posterior based on Gaussian approximation, which is then corrected using sequential Monte Carlo as in Vihola, Helske, Franks (2018).
See the package vignette for details and an examples.