mvgam_irf-class

<p>A <code>mvgam_irf</code> object returned by function <code>irf</code>.
Run <code>methods(class = "mvgam_irf")</code> to see an overview of available methods.</p>

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 (2022) <doi:10.1111/2041-210X.13974>.

Nicholas J Clark

mvgam

Multivariate (Dynamic) Generalized Additive Models

Sarah Heaps

Scott Pease

Matthijs Hollanders

mvgam_irf-class function

Author

mvgam_irf object description — mvgam_irf-class

<code>mvgam_irf</code> object description

mvgam_irf-class: `mvgam_irf` object description

Description

Arguments

Author

Details

See Also