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,
|Date of publication||2018-01-09 17:27:02 UTC|
|Maintainer||Jouni Helske <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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