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 nonGaussian models, walker uses the importance sampling type estimators based on
approximate marginal MCMC as in Vihola, Helske, Franks (2017,
Package details 


Author  Jouni Helske 
Date of publication  20180109 17:27:02 UTC 
Maintainer  Jouni Helske <[email protected]> 
License  GPL (>= 2) 
Version  0.2.1 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

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