Installation
About
Fit Bayesian Dynamic Generalized Additive Models to sets of time series. Users can build dynamic 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.
| Citation | mvgam citation info |
| github.com/nicholasjclark/mvgam | |
| nicholasjclark.github.io/mvgam/ | |
| Bug report | File report |
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Maintainer
| Maintainer | Nicholas J Clark |