print.mvgam_summary

Print method for mvgam_summary objects

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>.

Nicholas J Clark

mvgam

Multivariate (Dynamic) Generalized Additive Models

print.mvgam_summary function

<dl><dt>x</dt>
<dd>An object of class <code>mvgam_summary</code></dd>
<dt>...</dt>
<dd>Additional arguments (ignored)</dd></dl>

Arguments

Print method for mvgam_summary objects — print.mvgam_summary

<dl>

<dt>x</dt>
<dd>An object of class <code>mvgam_summary</code></dd>


<dt>...</dt>
<dd>Additional arguments (ignored)</dd>

</dl>

print.mvgam_summary: Print method for mvgam_summary objects

Description

Usage

Value

Arguments