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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.1.594 |
2026-04-09 windows/windows R-4.5 | mvgam_1.1.594.zip |
9.3 MiB |