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Fit Bayesian Dynamic Generalized Additive Models to sets of time series. Users can build dynamic nonlinear StateSpace models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2022) <doi:10.1111/2041210X.13974>.
Package details 


Author  Nicholas J Clark [aut, cre] (<https://orcid.org/0000000171313301>) 
Maintainer  Nicholas J Clark <nicholas.j.clark1214@gmail.com> 
License  MIT + file LICENSE 
Version  1.1.2 
URL  https://github.com/nicholasjclark/mvgam https://nicholasjclark.github.io/mvgam/ 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

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