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mvgam: Multivariate (Dynamic) Generalized Additive Models
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>.
Getting started
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Package details |
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| Author | Nicholas J Clark [aut, cre] (ORCID: <https://orcid.org/0000-0001-7131-3301>), KANK Karunarathna [ctb] (ARMA parameterisations and factor models, ORCID: <https://orcid.org/0000-0002-8995-5502>), Sarah Heaps [ctb] (VARMA parameterisations, ORCID: <https://orcid.org/0000-0002-5543-037X>), Scott Pease [ctb] (broom enhancements, ORCID: <https://orcid.org/0009-0006-8977-9285>), Matthijs Hollanders [ctb] (ggplot visualizations, ORCID: <https://orcid.org/0000-0003-0796-1018>) |
| Maintainer | Nicholas J Clark <nicholas.j.clark1214@gmail.com> |
| License | MIT + file LICENSE |
| Version | 1.1.594 |
| URL | https://github.com/nicholasjclark/mvgam https://nicholasjclark.github.io/mvgam/ |
| Package repository | View on CRAN |
| Installation |
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