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, <arXiv:1609.02541>).
Package details 


Author  Jouni Helske [aut, cre] (<https://orcid.org/000000017130793X>) 
Maintainer  Jouni Helske <[email protected]> 
License  GPL (>= 2) 
Version  0.2.31 
URL  https://github.com/helske/walker 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

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