mvgam-package {mvgam} | R Documentation |
mvgam: Multivariate (Dynamic) Generalized Additive Models
Description
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) doi:10.1111/2041-210X.13974.
Author(s)
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]
See Also
Useful links:
Report bugs at https://github.com/nicholasjclark/mvgam/issues