mvgam-package {mvgam}R Documentation

mvgam: Multivariate (Dynamic) Generalized Additive Models

Description

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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:

See Also

Useful links:


[Package mvgam version 1.1.51 Index]