nicholasjclark/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
Browse package contents
Package details |
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| Maintainer | |
| License | MIT + file LICENSE |
| Version | 1.1.57 |
| URL | https://github.com/nicholasjclark/mvgam https://nicholasjclark.github.io/mvgam/ |
| Package repository | View on GitHub |
| Installation |
Install the latest version of this package by entering the following in R:
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