mvgam-package

<img src="figures/logo.png?package=mvgam&version=1.1.4" style="float: right" alt="logo" width="120">
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 &amp; Wells (2022) tools:::Rd_expr_doi("10.1111/2041-210X.13974").

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

Nicholas J Clark

mvgam

Multivariate (Dynamic) Generalized Additive Models

Sarah Heaps

Scott Pease

Matthijs Hollanders

mvgam-package function

Maintainer: Nicholas J Clark <a href="/link/nicholas.j.clark1214%40gmail.com?package=mvgam&version=1.1.4" data-mini-rdoc="mvgam::nicholas.j.clark1214@gmail.com">nicholas.j.clark1214@gmail.com</a> (<a href="https://orcid.org/0000-0001-7131-3301">ORCID</a>)
Other contributors:<ul>
<li>Sarah Heaps (<a href="https://orcid.org/0000-0002-5543-037X">ORCID</a>) (VARMA parameterisations) [contributor]</li>
<li>Scott Pease (<a href="https://orcid.org/0009-0006-8977-9285">ORCID</a>) (broom enhancements) [contributor]</li>
<li>Matthijs Hollanders (<a href="https://orcid.org/0000-0003-0796-1018">ORCID</a>) (ggplot visualizations) [contributor]</li>
</ul>

Author

<img src='figures/logo.png' style='float: right' alt='logo' width='120' />
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 &amp; Wells (2022) tools:::Rd_expr_doi("10.1111/2041-210X.13974").

mvgam: Multivariate (Dynamic) Generalized Additive Models — mvgam-package

Maintainer: Nicholas J Clark <a href='mailto:nicholas.j.clark1214@gmail.com'>nicholas.j.clark1214@gmail.com</a> (<a href='https://orcid.org/0000-0001-7131-3301'>ORCID</a>)
Other contributors:<ul>
<li>Sarah Heaps (<a href='https://orcid.org/0000-0002-5543-037X'>ORCID</a>) (VARMA parameterisations) [contributor]</li>
<li>Scott Pease (<a href='https://orcid.org/0009-0006-8977-9285'>ORCID</a>) (broom enhancements) [contributor]</li>
<li>Matthijs Hollanders (<a href='https://orcid.org/0000-0003-0796-1018'>ORCID</a>) (ggplot visualizations) [contributor]</li>
</ul>

mvgam-package: mvgam: Multivariate (Dynamic) Generalized Additive Models

Description

Arguments

Author

See Also