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walker: Bayesian Generalized Linear Models with Time-Varying Coefficients

The R package 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 importance sampling as in Vihola, Helske, Franks (2020).

See the package vignette and documentation manual for details and examples.

You can download the development version of walker from Github using the devtools package:

devtools::install_github("helske/walker")

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