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walker: Efficient Baysian dynamic linear regression models with Stan/R

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.

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