mvgam-package | R Documentation |
Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space 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 (2023) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/2041-210X.13974")}.
Maintainer: Nicholas J Clark nicholas.j.clark1214@gmail.com (ORCID)
Other contributors:
Sarah Heaps (ORCID) (VARMA parameterisations) [contributor]
Scott Pease (ORCID) (broom enhancements) [contributor]
Matthijs Hollanders (ORCID) (ggplot visualizations) [contributor]
Useful links:
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